WO2023004026A1

WO2023004026A1 - Drillstring equipment controller

Info

Publication number: WO2023004026A1
Application number: PCT/US2022/037846
Authority: WO
Inventors: Shreya SINGH; Fatma Mahfoudh; Syed Aaquib HUSSAIN
Original assignee: Schlumberger Technology Corporation; Schlumberger Canada Limited; Services Petroliers Schlumberger; Geoquest Systems B.V.
Priority date: 2021-07-22
Filing date: 2022-07-21
Publication date: 2023-01-26

Abstract

A method may include receiving formation log data acquired via drillstring equipment. The method may also include determining a test type using at least a portion of the formation log data and a trained machine learning model. The method may further include issuing an instruction to the drillstring equipment to perform a test according to the test type.

Description

DRILLSTRING EQUIPMENT CONTROLLER

CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims the benefit of and priority to Indian Patent Application No. 202121033057, which was filed July 22, 2021. The patent application identified above is incorporated herein by reference in its entirety.

BACKGROUND

[0002] A resource field can be an accumulation, pool or group of pools of one or more resources (e.g., oil, gas, oil and gas) in a subsurface environment. A resource field can include at least one reservoir. A reservoir may be shaped in a manner that can trap hydrocarbons and may be covered by an impermeable or sealing rock. A bore can be drilled into an environment where the bore (e.g., a borehole) may be utilized to form a well that can be utilized in producing hydrocarbons from a reservoir.

[0003] A rig can be a system of components that can be operated to form a bore in an environment, to transport equipment into and out of a bore in an environment, etc. As an example, a rig can include a system that can be used to drill a bore and to acquire information about an environment, about drilling, etc. A resource field may be an onshore field, an offshore field or an on- and offshore field. A rig can include components for performing operations onshore and/or offshore. A rig may be, for example, vessel-based, offshore platform-based, onshore, etc.

[0004] Field planning and/or development can occur over one or more phases, which can include an exploration phase that aims to identify and assess an environment (e.g., a prospect, a play, etc.), which may include drilling of one or more bores (e.g., one or more exploratory wells, etc.).

SUMMARY

[0005] A method can include receiving formation log data acquired via drillstring equipment; determining a test type using at least a portion of the formation log data and a trained machine learning model; and issuing an instruction to the drillstring equipment to perform a test according to the test type. A system can include a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive formation log data acquired via drillstring equipment; determine a test type using at least a portion of the formation log data and a trained machine learning model; and issue an instruction to the drillstring equipment to perform a test according to the test type. One or more computer-readable storage media can include computer-executable instructions executable to instruct a computing system to: receive formation log data acquired via drillstring equipment; determine a test type using at least a portion of the formation log data and a trained machine learning model; and issue an instruction to the drillstring equipment to perform a test according to the test type. Various other apparatuses, systems, methods, etc., are also disclosed.

[0006] This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Features and advantages of the described implementations can be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.

[0008] Fig. 1 illustrates an example of a system and examples of equipment in a geologic environment;

[0009] Fig. 2 illustrates an example of a system and examples of equipment in a geologic environment;

[0010] Fig. 3 illustrates examples of equipment and examples of hole types; [0011] Fig. 4 illustrates an example of a system;

[0012] Fig. 5 illustrates an example of a wellsite system and an example of a computing system;

[0013] Fig. 6 illustrates an example of equipment in a geologic environment;

[0014] Fig. 7 illustrates an example of a graphical user interface;

[0015] Fig. 8 illustrates an example of a graphical user interface;

[0016] Fig. 9 illustrates an example of a system and an example of a method;

[0017] Fig. 10 illustrates an example of a table of test types; [0018] Fig. 11 illustrates an example of a method;

[0019] Fig. 12 illustrates an example of a trained machine learning model and an example of a plot that includes outputs of the trained machine learning model; [0020] Fig. 13 illustrates an example of a method;

[0021] Fig. 14 illustrates an example of a graphical user interface;

[0022] Fig. 15 illustrates an example of a method and an example of a system;

[0023] Fig. 16 illustrates an example of a system;

[0024] Fig. 17 illustrates an example of a computing system; and

[0025] Fig. 18 illustrates example components of a system and a networked system.

DETAILED DESCRIPTION

[0026] The following description includes the best mode presently contemplated for practicing the described implementations. This description is not to be taken in a limiting sense, but rather is made merely for the purpose of describing the general principles of the implementations. The scope of the described implementations should be ascertained with reference to the issued claims.

[0027] Fig. 1 shows an example of a system 100 that includes a workspace framework 110 that can provide for instantiation of, rendering of, interactions with, etc., a graphical user interface (GUI) 120. In the example of Fig. 1, the GUI 120 can include graphical controls for computational frameworks (e.g., applications) 121, projects 122, visualization 123, one or more other features 124, data access 125, and data storage 126.

[0028] In the example of Fig. 1 , the workspace framework 110 may be tailored to a particular geologic environment such as an example geologic environment 150. For example, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and that may be intersected by a fault 153. As an example, the geologic environment 150 may be outfitted with a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a wellsite and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, Fig. 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0029] Fig. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0030] In the example of Fig. 1, the GUI 120 shows some examples of computational frameworks, including the DRILLPLAN, PETREL, TECHLOG, PETROMOD, ECLIPSE, INTERSECT, PIPESIM and OMEGA frameworks (Schlumberger Limited, Houston, Texas). As to another type of framework, consider, for example, an test framework (TF), which may be operable in combination with one or more other frameworks to make determinations as to test type to be performed by drillstring equipment (e.g., for use in one or more field operations, etc.). In such an example, a TF may provide feedback such that another framework can operate on output of the TF, for example, to revise a plan, revise a control scheme, etc.

[0031] The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency. As an example, where a TF can generate recommendations for drilling equipment, the TF may be operatively coupled to the DRILLPLAN framework. In such an example, interactions may exist, which may be automatic. For example, consider a TF that can dynamically generate test type determinations responsive to progression of a plan being generated by a framework such as the DRILLPLAN framework.

[0032] The PETREL framework can be part of the DELFI cognitive E&P environment (Schlumberger Limited, Houston, Texas) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir.

[0033] The TECHLOG framework can handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework can structure wellbore data for analyses, planning, etc. [0034] The PETROMOD framework provides petroleum systems modeling capabilities that can combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework can predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions.

[0035] The ECLIPSE framework provides a reservoir simulator (e.g., as a computational framework) with numerical solutions for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes.

[0036] The INTERSECT framework provides a high-resolution reservoir simulator for simulation of detailed geological features and quantification of uncertainties, for example, by creating accurate production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework can produce reliable results, which may be continuously updated by real-time data exchanges (e.g., from one or more types of data acquisition equipment in the field that can acquire data during one or more types of field operations, etc.). The INTERSECT framework can provide completion configurations for complex wells where such configurations can be built in the field, can provide detailed chemical-enhanced-oil-recovery (EOR) formulations where such formulations can be implemented in the field, can analyze application of steam injection and other thermal EOR techniques for implementation in the field, advanced production controls in terms of reservoir coupling and flexible field management, and flexibility to script customized solutions for improved modeling and field management control. The INTERSECT framework, as with the other example frameworks, may be utilized as part of the DELFI cognitive E&P environment, for example, for rapid simulation of multiple concurrent cases. For example, a workflow may utilize one or more of the DELFI on demand reservoir simulation features.

[0037] The PIPESIM simulator includes solvers that may provide simulation results such as, for example, multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc. The PIPESIM simulator may be integrated, for example, with the AVOCET production operations framework (Schlumberger Limited, Houston Texas). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as steam-assisted gravity drainage (SAGD), etc.). As an example, the PIPESIM simulator may be an optimizer that can optimize one or more operational scenarios at least in part via simulation of physical phenomena.

[0038] The OMEGA framework includes finite difference modelling (FDMOD) features for two-way wavefield extrapolation modelling, generating synthetic shot gathers with and without multiples. The FDMOD features can generate synthetic shot gathers by using full 3D, two-way wavefield extrapolation modelling, which can utilize wavefield extrapolation logic matches that are used by reverse-time migration (RTM). A model may be specified on a dense 3D grid as velocity and optionally as anisotropy, dip, and variable density. The OMEGA framework also includes features for RTM, FDMOD, adaptive beam migration (ABM), Gaussian packet migration (Gaussian PM), depth processing (e.g., Kirchhoff prestack depth migration (KPSDM), tomography (Tomo)), time processing (e.g., Kirchhoff prestack time migration (KPSTM), general surface multiple prediction (GSMP), extended interbed multiple prediction (XIMP)), framework foundation features, desktop features (e.g., GUIs, etc.), and development tools. Various features can be included for processing various types of data such as, for example, one or more of: land, marine, and transition zone data; time and depth data; 2D, 3D, and 4D surveys; isotropic and anisotropic (TTI and VTI) velocity fields; and multicomponent data.

[0039] The aforementioned DELFI environment provides various features for workflows as to subsurface analysis, planning, construction and production, for example, as illustrated in the workspace framework 110. As shown in Fig. 1 , outputs from the workspace framework 110 can be utilized for directing, controlling, etc., one or more processes in the geologic environment 150 and, feedback 160, can be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions, etc.).

[0040] As an example, a workflow may progress to a geology and geophysics (“G&G”) service provider, which may generate a well trajectory, which may involve execution of one or more G&G software packages. Examples of such software packages include the PETREL framework. As an example, a system or systems may utilize a framework such as the DELFI framework (Schlumberger Limited, Houston, Texas). Such a framework may operatively couple various other frameworks to provide for a multi-framework workspace. As an example, the GUI 120 of Fig. 1 may be a GUI of the DELFI framework.

[0041] In the example of Fig. 1 , the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks as associated with one or more of subsurface regions, planning operations, constructing wells and/or surface fluid networks, and producing from a reservoir.

[0042] As an example, a visualization process can implement one or more of various features that can be suitable for one or more web applications. For example, a template may involve use of the JAVASCRIPT object notation format (JSON) and/or one or more other languages/formats. As an example, a framework may include one or more converters. For example, consider a JSON to PYTHON converter and/or a PYTHON to JSON converter.

[0043] As an example, visualization features can provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features can provide for rendering of information in multiple dimensions, which may optionally include multiple resolution rendering. In such an example, information being rendered may be associated with one or more frameworks and/or one or more data stores. As an example, visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations. As an example, a workflow may utilize one or more frameworks to generate information that can be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment, etc.).

[0044] As to a reservoir model that may be suitable for utilization by a simulator, consider acquisition of seismic data as acquired via reflection seismology, which finds use in geophysics, for example, to estimate properties of subsurface formations. As an example, reflection seismology may provide seismic data representing waves of elastic energy (e.g., as transmitted by P-waves and S-waves, in a frequency range of approximately 1 Hz to approximately 100 Hz). Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks. Such interpretation results can be utilized to plan, simulate, perform, etc., one or more operations for production of fluid from a reservoir (e.g., reservoir rock, etc.).

[0045] Field acquisition equipment may be utilized to acquire seismic data, which may be in the form of traces where a trace can include values organized with respect to time and/or depth (e.g., consider 1 D, 2D, 3D or 4D seismic data). For example, consider acquisition equipment that acquires digital samples at a rate of one sample per approximately 4 ms. Given a speed of sound in a medium or media, a sample rate may be converted to an approximate distance. For example, the speed of sound in rock may be on the order of around 5 km per second. Thus, a sample time spacing of approximately 4 ms would correspond to a sample “depth” spacing of about 10 meters (e.g., assuming a path length from source to boundary and boundary to sensor). As an example, a trace may be about 4 seconds in duration; thus, for a sampling rate of one sample at about 4 ms intervals, such a trace would include about 1000 samples where latter acquired samples correspond to deeper reflection boundaries. If the 4 second trace duration of the foregoing example is divided by two (e.g., to account for reflection), for a vertically aligned source and sensor, a deepest boundary depth may be estimated to be about 10 km (e.g., assuming a speed of sound of about 5 km per second).

[0046] As an example, a model may be a simulated version of a geologic environment. As an example, a simulator may include features for simulating physical phenomena in a geologic environment based at least in part on a model or models. A simulator, such as a reservoir simulator, can simulate fluid flow in a geologic environment based at least in part on a model that can be generated via a framework that receives seismic data. A simulator can be a computerized system (e.g., a computing system) that can execute instructions using one or more processors to solve a system of equations that describe physical phenomena subject to various constraints. In such an example, the system of equations may be spatially defined (e.g., numerically discretized) according to a spatial model that that includes layers of rock, geobodies, etc., that have corresponding positions that can be based on interpretation of seismic and/or other data. A spatial model may be a cell-based model where cells are defined by a grid (e.g., a mesh). A cell in a cell-based model can represent a physical area or volume in a geologic environment where the cell can be assigned physical properties (e.g., permeability, fluid properties, etc.) that may be germane to one or more physical phenomena (e.g., fluid volume, fluid flow, pressure, etc.). A reservoir simulation model can be a spatial model that may be cell-based.

[0047] While several simulators are illustrated in the example of Fig. 1 , one or more other simulators may be utilized, additionally or alternatively. For example, consider the VISAGE geomechanics simulator (Schlumberger Limited, Houston Texas), etc. The VISAGE simulator includes finite element numerical solvers that may provide simulation results such as, for example, results as to compaction and subsidence of a geologic environment, well and completion integrity in a geologic environment, cap-rock and fault-seal integrity in a geologic environment, fracture behavior in a geologic environment, thermal recovery in a geologic environment, CO2 disposal, etc. The MANGROVE simulator (Schlumberger Limited, Houston, Texas) provides for optimization of stimulation design (e.g., stimulation treatment operations such as hydraulic fracturing) in a reservoir-centric environment. The MANGROVE framework can combine scientific and experimental work to predict geomechanical propagation of hydraulic fractures, reactivation of natural fractures, etc., along with production forecasts within 3D reservoir models (e.g., production from a drainage area of a reservoir where fluid moves via one or more types of fractures to a well and/or from a well). The MANGROVE framework can provide results pertaining to heterogeneous interactions between hydraulic and natural fracture networks, which may assist with optimization of the number and location of fracture treatment stages (e.g., stimulation treatment(s)), for example, to increased perforation efficiency and recovery.

[0048] The PETREL framework provides components that allow for optimization of exploration and development operations. The PETREL framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes (e.g., with respect to one or more geologic environments, etc.). Such a framework may be considered an application (e.g., executable using one or more devices) and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0049] As mentioned, a framework may be implemented within or in a manner operatively coupled to the DELFI cognitive exploration and production (E&P) environment (Schlumberger, Houston, Texas), which is a secure, cognitive, cloud- based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning. As an example, such an environment can provide for operations that involve one or more frameworks. The DELFI environment may be referred to as the DELFI framework, which may be a framework of frameworks. As an example, the DELFI framework can include various other frameworks, which can include, for example, one or more types of models (e.g., simulation models, etc.).

[0050] Fig. 2 shows an example of a geologic environment 210 that includes reservoirs 211-1 and 211-2, which may be faulted by faults 212-1 and 212-2, an example of a network of equipment 230, an enlarged view of a portion of the network of equipment 230, referred to as network 240, and an example of a system 250. Fig. 2 shows some examples of offshore equipment 214 for oil and gas operations related to the reservoir 211-2 and onshore equipment 216 for oil and gas operations related to the reservoir 211-1.

[0051] In the example of Fig. 2, the various equipment 214 and 216 can include drilling equipment, wireline equipment, production equipment, etc. For example, consider the equipment 214 as including a drilling rig that can drill into a formation to reach a reservoir target where a well can be completed for production of hydrocarbons. In such an example, one or more features of the system 100 of Fig. 1 may be utilized. For example, consider utilizing the DRILLPLAN framework to plan, execute, etc., one or more drilling operations.

[0052] In Fig. 2, the network 240 can be an example of a relatively small production system network. As shown, the network 240 forms somewhat of a tree like structure where flowlines represent branches (e.g., segments) and junctions represent nodes. As shown in Fig. 2, the network 240 provides for transportation of oil and gas fluids from well locations along flowlines interconnected at junctions with final delivery at a central processing facility. [0053] In the example of Fig. 2, various portions of the network 240 may include conduit. For example, consider a perspective view of a geologic environment that includes two conduits which may be a conduit to Man1 and a conduit to Man3 in the network 240.

[0054] As shown in Fig. 2, the example system 250 includes one or more information storage devices 252, one or more computers 254, one or more networks 260 and instructions 270 (e.g., organized as one or more sets of instructions). As to the one or more computers 254, each computer may include one or more processors (e.g., or processing cores) 256 and memory 258 for storing the instructions 270 (e.g., one or more sets of instructions), for example, executable by at least one of the one or more processors. As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc. As an example, imagery such as surface imagery (e.g., satellite, geological, geophysical, etc.) may be stored, processed, communicated, etc. As an example, data may include SAR data, GPS data, etc. and may be stored, for example, in one or more of the storage devices 252. As an example, information that may be stored in one or more of the storage devices 252 may include information about equipment, location of equipment, orientation of equipment, fluid characteristics, etc.

[0055] As an example, the instructions 270 can include instructions (e.g., stored in the memory 258) executable by at least one of the one or more processors 256 to instruct the system 250 to perform various actions. As an example, the system 250 may be configured such that the instructions 270 provide for establishing a framework, for example, that can perform modeling, simulation, etc. As an example, one or more methods, techniques, etc. may be performed using one or more sets of instructions, which may be, for example, the instructions 270 of Fig. 2. [0056] Various equipment that may be at a site can include rig equipment.

For example, consider rig equipment that includes a platform, a derrick, a crown block, a line, a traveling block assembly, drawworks and a landing (e.g., a monkeyboard). As an example, the line may be controlled at least in part via the drawworks such that the traveling block assembly travels in a vertical direction with respect to the platform. For example, by drawing the line in, the drawworks may cause the line to run through the crown block and lift the traveling block assembly skyward away from the platform; whereas, by allowing the line out, the drawworks may cause the line to run through the crown block and lower the traveling block assembly toward the platform. Where the traveling block assembly carries pipe (e.g., casing, etc.), tracking of movement of the traveling block may provide an indication as to how much pipe has been deployed.

[0057] A derrick can be a structure used to support a crown block and a traveling block operatively coupled to the crown block at least in part via line. A derrick may be pyramidal in shape and offer a suitable strength-to-weight ratio. A derrick may be movable as a unit or in a piece by piece manner (e.g., to be assembled and disassembled).

[0058] As an example, drawworks may include a spool, brakes, a power source and assorted auxiliary devices. Drawworks may controllably reel out and reel in line. Line may be reeled over a crown block and coupled to a traveling block to gain mechanical advantage in a “block and tackle” or “pulley” fashion. Reeling out and in of line can cause a traveling block (e.g., and whatever may be hanging underneath it), to be lowered into or raised out of a bore. Reeling out of line may be powered by gravity and reeling in by a motor, an engine, etc. (e.g., an electric motor, a diesel engine, etc.).

[0059] As an example, a crown block can include a set of pulleys (e.g., sheaves) that can be located at or near a top of a derrick or a mast, over which line is threaded. A traveling block can include a set of sheaves that can be moved up and down in a derrick or a mast via line threaded in the set of sheaves of the traveling block and in the set of sheaves of a crown block. A crown block, a traveling block and a line can form a pulley system of a derrick or a mast, which may enable handling of heavy loads (e.g., drillstring, pipe, casing, liners, etc.) to be lifted out of or lowered into a bore. As an example, line may be about a centimeter to about five centimeters in diameter as, for example, steel cable. Through use of a set of sheaves, such line may carry loads heavier than the line could support as a single strand.

[0060] As an example, a derrickman may be a rig crew member that works on a platform attached to a derrick or a mast. A derrick can include a landing on which a derrickman may stand. As an example, such a landing may be about 10 meters or more above a rig floor. In an operation referred to as trip out of the hole (TOH), a derrickman may wear a safety harness that enables leaning out from the work landing (e.g., monkeyboard) to reach pipe located at or near the center of a derrick or a mast and to throw a line around the pipe and pull it back into its storage location (e.g., fingerboards), for example, until it may be desirable to run the pipe back into the bore. As an example, a rig may include automated pipe-handling equipment such that the derrickman controls the machinery rather than physically handling the pipe.

[0061] As an example, a trip may refer to the act of pulling equipment from a bore and/or placing equipment in a bore. As an example, equipment may include a drillstring that can be pulled out of a hole and/or placed or replaced in a hole. As an example, a pipe trip may be performed where a drill bit has dulled or has otherwise ceased to drill efficiently and is to be replaced. As an example, a trip that pulls equipment out of a borehole may be referred to as pulling out of hole (POOH) and a trip that runs equipment into a borehole may be referred to as running in hole (RIH). [0062] Fig. 3 shows an example of a wellsite system 300 (e.g., at a wellsite that may be onshore or offshore). As shown, the wellsite system 300 can include a mud tank 301 for holding mud and other material (e.g., where mud can be a drilling fluid), a suction line 303 that serves as an inlet to a mud pump 304 for pumping mud from the mud tank 301 such that mud flows to a vibrating hose 306, a drawworks 307 for winching drill line or drill lines 312, a standpipe 308 that receives mud from the vibrating hose 306, a kelly hose 309 that receives mud from the standpipe 308, a gooseneck or goosenecks 310, a traveling block 311 , a crown block 313 for carrying the traveling block 311 via the drill line or drill lines 312, a derrick 314, a kelly 318 or a top drive 340, a kelly drive bushing 319, a rotary table 320, a drill floor 321, a bell nipple 322, one or more blowout preventors (BOPs) 323, a drillstring 325, a drill bit 326, a casing head 327 and a flow pipe 328 that carries mud and other material to, for example, the mud tank 301.

[0063] In the example system of Fig. 3, a borehole 332 is formed in subsurface formations 330 by rotary drilling; noting that various example embodiments may also use one or more directional drilling techniques, equipment, etc.

[0064] As shown in the example of Fig. 3, the drillstring 325 is suspended within the borehole 332 and has a drillstring assembly 350 that includes the drill bit 326 at its lower end. As an example, the drillstring assembly 350 may be a bottom hole assembly (BHA). [0065] The wellsite system 300 can provide for operation of the drillstring 325 and other operations. As shown, the wellsite system 300 includes the traveling block 311 and the derrick 314 positioned over the borehole 332. As mentioned, the wellsite system 300 can include the rotary table 320 where the drillstring 325 pass through an opening in the rotary table 320.

[0066] As shown in the example of Fig. 3, the wellsite system 300 can include the kelly 318 and associated components, etc., or the top drive 340 and associated components. As to a kelly example, the kelly 318 may be a square or hexagonal metal/alloy bar with a hole drilled therein that serves as a mud flow path. The kelly 318 can be used to transmit rotary motion from the rotary table 320 via the kelly drive bushing 319 to the drillstring 325, while allowing the drillstring 325 to be lowered or raised during rotation. The kelly 318 can pass through the kelly drive bushing 319, which can be driven by the rotary table 320. As an example, the rotary table 320 can include a master bushing that operatively couples to the kelly drive bushing 319 such that rotation of the rotary table 320 can turn the kelly drive bushing 319 and hence the kelly 318. The kelly drive bushing 319 can include an inside profile matching an outside profile (e.g., square, hexagonal, etc.) of the kelly 318; however, with slightly larger dimensions so that the kelly 318 can freely move up and down inside the kelly drive bushing 319.

[0067] As to a top drive example, the top drive 340 can provide functions performed by a kelly and a rotary table. The top drive 340 can turn the drillstring 325. As an example, the top drive 340 can include one or more motors (e.g., electric and/or hydraulic) connected with appropriate gearing to a short section of pipe called a quill, that in turn may be screwed into a saver sub or the drillstring 325 itself. The top drive 340 can be suspended from the traveling block 311, so the rotary mechanism is free to travel up and down the derrick 314. As an example, a top drive 340 may allow for drilling to be performed with more joint stands than a kelly/rotary table approach.

[0068] In the example of Fig. 3, the mud tank 301 can hold mud, which can be one or more types of drilling fluids. As an example, a wellbore may be drilled to produce fluid, inject fluid or both (e.g., hydrocarbons, minerals, water, etc.).

[0069] In the example of Fig. 3, the drillstring 325 (e.g., including one or more downhole tools) may be composed of a series of pipes threadably connected together to form a long tube with the drill bit 326 at the lower end thereof. As the drillstring 325 is advanced into a wellbore for drilling, at some point in time prior to or coincident with drilling, the mud may be pumped by the pump 304 from the mud tank 301 (e.g., or other source) via a the lines 306, 308 and 309 to a port of the kelly 318 or, for example, to a port of the top drive 340. The mud can then flow via a passage (e.g., or passages) in the drillstring 325 and out of ports located on the drill bit 326 (see, e.g., a directional arrow). As the mud exits the drillstring 325 via ports in the drill bit 326, it can then circulate upwardly through an annular region between an outer surface(s) of the drillstring 325 and surrounding wall(s) (e.g., open borehole, casing, etc.), as indicated by directional arrows. In such a manner, the mud lubricates the drill bit 326 and carries heat energy (e.g., frictional or other energy) and formation cuttings to the surface where the mud (e.g., and cuttings) may be returned to the mud tank 301 , for example, for recirculation (e.g., with processing to remove cuttings, etc.).

[0070] The mud pumped by the pump 304 into the drillstring 325 may, after exiting the drillstring 325, form a mudcake that lines the wellbore which, among other functions, may reduce friction between the drillstring 325 and surrounding wall(s) (e.g., borehole, casing, etc.). A reduction in friction may facilitate advancing or retracting the drillstring 325. During a drilling operation, the entire drillstring 325 may be pulled from a wellbore and optionally replaced, for example, with a new or sharpened drill bit, a smaller diameter drillstring, etc. As mentioned, the act of pulling a drillstring out of a hole or replacing it in a hole is referred to as tripping. A trip may be referred to as an upward trip or an outward trip or as a downward trip or an inward trip depending on trip direction.

[0071] As an example, consider a downward trip where upon arrival of the drill bit 326 of the drillstring 325 at a bottom of a wellbore, pumping of the mud commences to lubricate the drill bit 326 for purposes of drilling to enlarge the wellbore. As mentioned, the mud can be pumped by the pump 304 into a passage of the drillstring 325 and, upon filling of the passage, the mud may be used as a transmission medium to transmit energy, for example, energy that may encode information as in mud-pulse telemetry.

[0072] As an example, mud-pulse telemetry equipment may include a downhole device configured to effect changes in pressure in the mud to create an acoustic wave or waves upon which information may modulated. In such an example, information from downhole equipment (e.g., one or more modules of the drillstring 325) may be transmitted uphole to an uphole device, which may relay such information to other equipment for processing, control, etc.

[0073] As an example, telemetry equipment may operate via transmission of energy via the drillstring 325 itself. For example, consider a signal generator that imparts coded energy signals to the drillstring 325 and repeaters that may receive such energy and repeat it to further transmit the coded energy signals (e.g., information, etc.).

[0074] As an example, the drillstring 325 may be fitted with telemetry equipment 352 that includes a rotatable drive shaft, a turbine impeller mechanically coupled to the drive shaft such that the mud can cause the turbine impeller to rotate, a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine impeller causes said modulator rotor to rotate, a modulator stator mounted adjacent to or proximate to the modulator rotor such that rotation of the modulator rotor relative to the modulator stator creates pressure pulses in the mud, and a controllable brake for selectively braking rotation of the modulator rotor to modulate pressure pulses. In such example, an alternator may be coupled to the aforementioned drive shaft where the alternator includes at least one stator winding electrically coupled to a control circuit to selectively short the at least one stator winding to electromagnetically brake the alternator and thereby selectively brake rotation of the modulator rotor to modulate the pressure pulses in the mud.

[0075] In the example of Fig. 3, an uphole control and/or data acquisition system 362 may include circuitry to sense pressure pulses generated by telemetry equipment 352 and, for example, communicate sensed pressure pulses or information derived therefrom for process, control, etc.

[0076] The assembly 350 of the illustrated example includes a logging-while- drilling (LWD) module 354, a measurement-while-drilling (MWD) module 356, an optional module 358, a rotary-steerable system (RSS) and/or motor 360, and the drill bit 326. Such components or modules may be referred to as tools where a drillstring can include a plurality of tools.

[0077] As to a RSS, it involves technology utilized for directional drilling. Directional drilling involves drilling into the Earth to form a deviated bore such that the trajectory of the bore is not vertical; rather, the trajectory deviates from vertical along one or more portions of the bore. As an example, consider a target that is located at a lateral distance from a surface location where a rig may be stationed. In such an example, drilling can commence with a vertical portion and then deviate from vertical such that the bore is aimed at the target and, eventually, reaches the target. Directional drilling may be implemented where a target may be inaccessible from a vertical location at the surface of the Earth, where material exists in the Earth that may impede drilling or otherwise be detrimental (e.g., consider a salt dome, etc.), where a formation is laterally extensive (e.g., consider a relatively thin yet laterally extensive reservoir), where multiple bores are to be drilled from a single surface bore, where a relief well is desired, etc.

[0078] One approach to directional drilling involves a mud motor; however, a mud motor can present some challenges depending on factors such as rate of penetration (ROP), transferring weight to a bit (e.g., weight on bit, WOB) due to friction, etc. A mud motor can be a positive displacement motor (PDM) that operates to drive a bit (e.g., during directional drilling, etc.). A PDM operates as drilling fluid is pumped through it where the PDM converts hydraulic power of the drilling fluid into mechanical power to cause the bit to rotate.

[0079] As an example, a PDM may operate in a combined rotating mode where surface equipment is utilized to rotate a bit of a drillstring (e.g., a rotary table, a top drive, etc.) by rotating the entire drillstring and where drilling fluid is utilized to rotate the bit of the drillstring. In such an example, a surface RPM (SRPM) may be determined by use of the surface equipment and a downhole RPM of the mud motor may be determined using various factors related to flow of drilling fluid, mud motor type, etc. As an example, in the combined rotating mode, bit RPM can be determined or estimated as a sum of the SRPM and the mud motor RPM, assuming the SRPM and the mud motor RPM are in the same direction.

[0080] As an example, a PDM mud motor can operate in a so-called sliding mode, when the drillstring is not rotated from the surface. In such an example, a bit RPM can be determined or estimated based on the RPM of the mud motor.

[0081] A RSS can drill directionally where there is continuous rotation from surface equipment, which can alleviate the sliding of a steerable motor (e.g., a PDM). A RSS may be deployed when drilling directionally (e.g., deviated, horizontal, or extended-reach wells). A RSS can aim to minimize interaction with a borehole wall, which can help to preserve borehole quality. A RSS can aim to exert a relatively consistent side force akin to stabilizers that rotate with the drillstring or orient the bit in the desired direction while continuously rotating at the same number of rotations per minute as the drillstring.

[0082] The LWD module 354 may be housed in a suitable type of drill collar and can contain one or a plurality of selected types of logging tools. It will also be understood that more than one LWD and/or MWD module can be employed, for example, as represented at by the module 356 of the drillstring assembly 350.

Where the position of an LWD module is mentioned, as an example, it may refer to a module at the position of the LWD module 354, the module 356, etc. An LWD module can include capabilities for measuring, processing, and storing information, as well as for communicating with the surface equipment. In the illustrated example, the LWD module 354 may include a seismic measuring device.

[0083] The MWD module 356 may be housed in a suitable type of drill collar and can contain one or more devices for measuring characteristics of the drillstring 325 and the drill bit 326. As an example, the MWD tool 354 may include equipment for generating electrical power, for example, to power various components of the drillstring 325. As an example, the MWD tool 354 may include the telemetry equipment 352, for example, where the turbine impeller can generate power by flow of the mud; it being understood that other power and/or battery systems may be employed for purposes of powering various components. As an example, the MWD module 356 may include one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device, and an inclination measuring device.

[0084] Fig. 3 also shows some examples of types of holes that may be drilled. For example, consider a slant hole 372, an S-shaped hole 374, a deep inclined hole 376 and a horizontal hole 378.

[0085] As an example, a drilling operation can include directional drilling where, for example, at least a portion of a well includes a curved axis. For example, consider a radius that defines curvature where an inclination with regard to the vertical may vary until reaching an angle between about 30 degrees and about 60 degrees or, for example, an angle to about 90 degrees or possibly greater than about 90 degrees.

[0086] As an example, a directional well can include several shapes where each of the shapes may aim to meet particular operational demands. As an example, a drilling process may be performed on the basis of information as and when it is relayed to a drilling engineer. As an example, inclination and/or direction may be modified based on information received during a drilling process.

[0087] As an example, deviation of a bore may be accomplished in part by use of a downhole motor and/or a turbine. As to a motor, for example, a drillstring can include a positive displacement motor (PDM).

[0088] As an example, a system may be a steerable system and include equipment to perform method such as geosteering. As mentioned, a steerable system can be or include an RSS. As an example, a steerable system can include a PDM or of a turbine on a lower part of a drillstring which, just above a drill bit, a bent sub can be mounted. As an example, above a PDM, MWD equipment that provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc.) and/or LWD equipment may be installed. As to the latter, LWD equipment can make it possible to send to the surface various types of data of interest, including for example, geological data (e.g., gamma ray log, resistivity, density and sonic logs, etc.).

[0089] The coupling of sensors providing information on the course of a well trajectory, in real time or near real time, with, for example, one or more logs characterizing the formations from a geological viewpoint, can allow for implementing a geosteering method. Such a method can include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.

[0090] As an example, a drillstring can include an azimuthal density neutron (ADN) tool for measuring density and porosity; a MWD tool for measuring inclination, azimuth and shocks; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; one or more variable gauge stabilizers; one or more bend joints; and a geosteering tool, which may include a motor and optionally equipment for measuring and/or responding to one or more of inclination, resistivity and gamma ray related phenomena.

[0091] As an example, geosteering can include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region, zone (e.g., a pay zone), etc. As an example, geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and/or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.

[0092] Referring again to Fig. 3, the wellsite system 300 can include one or more sensors 364 that are operatively coupled to the control and/or data acquisition system 362. As an example, a sensor or sensors may be at surface locations. As an example, a sensor or sensors may be at downhole locations. As an example, a sensor or sensors may be at one or more remote locations that are not within a distance of the order of about one hundred meters from the wellsite system 300. As an example, a sensor or sensor may be at an offset wellsite where the wellsite system 300 and the offset wellsite are in a common field (e.g., oil and/or gas field). [0093] As an example, one or more of the sensors 364 can be provided for tracking pipe, tracking movement of at least a portion of a drillstring, etc.

[0094] As an example, the system 300 can include one or more sensors 366 that can sense and/or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit). For example, in the system 300, the one or more sensors 366 can be operatively coupled to portions of the standpipe 308 through which mud flows. As an example, a downhole tool can generate pulses that can travel through the mud and be sensed by one or more of the one or more sensors 366. In such an example, the downhole tool can include associated circuitry such as, for example, encoding circuitry that can encode signals, for example, to reduce demands as to transmission. As an example, circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. As an example, circuitry at the surface may include encoder circuitry and/or decoder circuitry and circuitry downhole may include encoder circuitry and/or decoder circuitry. As an example, the system 300 can include a transmitter that can generate signals that can be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.

[0095] As an example, one or more portions of a drillstring may become stuck. The term stuck can refer to one or more of varying degrees of inability to move or remove a drillstring from a bore. As an example, in a stuck condition, it might be possible to rotate pipe or lower it back into a bore or, for example, in a stuck condition, there may be an inability to move the drillstring axially in the bore, though some amount of rotation may be possible. As an example, in a stuck condition, there may be an inability to move at least a portion of the drillstring axially and rotationally.

[0096] As to the term “stuck pipe”, this can refer to a portion of a drillstring that cannot be rotated or moved axially. As an example, a condition referred to as “differential sticking” can be a condition whereby the drillstring cannot be moved (e.g., rotated or reciprocated) along the axis of the bore. Differential sticking may occur when high-contact forces caused by low reservoir pressures, high wellbore pressures, or both, are exerted over a sufficiently large area of the drillstring. Differential sticking can have time and financial cost.

[0097] As an example, a sticking force can be a product of the differential pressure between the wellbore and the reservoir and the area that the differential pressure is acting upon. This means that a relatively low differential pressure (delta p) applied over a large working area can be just as effective in sticking pipe as can a high differential pressure applied over a small area.

[0098] As an example, a condition referred to as “mechanical sticking” can be a condition where limiting or prevention of motion of the drillstring by a mechanism other than differential pressure sticking occurs. Mechanical sticking can be caused, for example, by one or more of junk in the hole, wellbore geometry anomalies, cement, keyseats or a buildup of cuttings in the annulus.

[0099] Fig. 4 shows an example of a system 400 that includes various equipment for evaluation 410, planning 420, engineering 430 and operations 440.

For example, a drilling workflow framework 401 , a seismic-to-simulation framework 402, a technical data framework 403 and a drilling framework 404 may be implemented to perform one or more processes such as a evaluating a formation 414, evaluating a process 418, generating a trajectory 424, validating a trajectory 428, formulating constraints 434, designing equipment and/or processes based at least in part on constraints 438, performing drilling 444 and evaluating drilling and/or formation 448.

[00100] In the example of Fig. 4, the seismic-to-simulation framework 402 can be, for example, the PETREL framework (Schlumberger, Houston, Texas) and the technical data framework 403 can be, for example, the TECHLOG framework (Schlumberger, Houston, Texas).

[00101] As an example, the system 400 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a workflow may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable at least in part in the PETREL framework, for example, that operates on seismic data, seismic attribute(s), etc.

[00102] As mentioned, a drillstring can include various tools that may make measurements. As an example, a wireline tool or another type of tool may be utilized to make measurements. As an example, a tool may be configured to acquire electrical borehole images. As an example, the fullbore Formation Microimager (FMI) tool (Schlumberger, Houston, Texas) can acquire borehole image data. A data acquisition sequence for such a tool can include running the tool into a borehole with acquisition pads closed, opening and pressing the pads against a wall of the borehole, delivering electrical current into the material defining the borehole while translating the tool in the borehole, and sensing current remotely, which is altered by interactions with the material.

[00103] Analysis of formation information may reveal features such as, for example, vugs, dissolution planes (e.g., dissolution along bedding planes), stress- related features, dip events, etc. As an example, a tool may acquire information that may help to characterize a reservoir, optionally a fractured reservoir where fractures may be natural and/or artificial (e.g., hydraulic fractures). As an example, information acquired by a tool or tools may be analyzed using a framework such as the TECHLOG framework. As an example, the TECHLOG framework can be interoperable with one or more other frameworks such as, for example, the PETREL framework.

[00104] As an example, various aspects of a workflow may be completed automatically, may be partially automated, or may be completed manually, as by a human user interfacing with a software application that executes using hardware (e.g., local and/or remote). As an example, a workflow may be cyclic, and may include, as an example, four stages such as, for example, an evaluation stage (see, e.g., the evaluation equipment 410), a planning stage (see, e.g., the planning equipment 420), an engineering stage (see, e.g., the engineering equipment 430) and an execution stage (see, e.g., the operations equipment 440). As an example, a workflow may commence at one or more stages, which may progress to one or more other stages (e.g., in a serial manner, in a parallel manner, in a cyclical manner, etc.).

[00105] As an example, a workflow can commence with an evaluation stage, which may include a geological service provider evaluating a formation (see, e.g., the evaluation block 414). As an example, a geological service provider may undertake the formation evaluation using a computing system executing a software package tailored to such activity; or, for example, one or more other suitable geology platforms may be employed (e.g., alternatively or additionally). As an example, the geological service provider may evaluate the formation, for example, using earth models, geophysical models, basin models, petrotechnical models, combinations thereof, and/or the like. Such models may take into consideration a variety of different inputs, including offset well data, seismic data, pilot well data, other geologic data, etc. The models and/or the input may be stored in the database maintained by the server and accessed by the geological service provider.

[00106] As an example, a workflow may progress to a geology and geophysics (“G&G”) service provider, which may generate a well trajectory (see, e.g., the generation block 424), which may involve execution of one or more G&G software packages. Examples of such software packages include the PETREL framework.

As an example, a G&G service provider may determine a well trajectory or a section thereof, based on, for example, one or more model(s) provided by a formation evaluation (e.g., per the evaluation block 314), and/or other data, e.g., as accessed from one or more databases (e.g., maintained by one or more servers, etc.). As an example, a well trajectory may take into consideration various “basis of design”

(BOD) constraints, such as general surface location, target (e.g., reservoir) location, and the like. As an example, a trajectory may incorporate information about tools, bottom-hole assemblies, casing sizes, etc., that may be used in drilling the well. A well trajectory determination may take into consideration a variety of other parameters, including risk tolerances, fluid weights and/or plans, bottom-hole pressures, drilling time, etc.

[00107] As an example, a workflow may progress to a first engineering service provider (e.g., one or more processing machines associated therewith), which may validate a well trajectory and, for example, relief well design (see, e.g., the validation block 428). Such a validation process may include evaluating physical properties, calculations, risk tolerances, integration with other aspects of a workflow, etc. As an example, one or more parameters for such determinations may be maintained by a server and/or by the first engineering service provider; noting that one or more model(s), well trajectory(ies), etc. may be maintained by a server and accessed by the first engineering service provider. For example, the first engineering service provider may include one or more computing systems executing one or more software packages. As an example, where the first engineering service provider rejects or otherwise suggests an adjustment to a well trajectory, the well trajectory may be adjusted or a message or other notification sent to the G&G service provider requesting such modification.

[00108] As an example, one or more engineering service providers (e.g., first, second, etc.) may provide a casing design, bottom-hole assembly (BHA) design, fluid design, and/or the like, to implement a well trajectory (see, e.g., the design block 338). In some embodiments, a second engineering service provider may perform such design using one of more software applications. Such designs may be stored in one or more databases maintained by one or more servers, which may, for example, employ STUDIO framework tools (Schlumberger, Houston, Texas), and may be accessed by one or more of the other service providers in a workflow. [00109] As an example, a second engineering service provider may seek approval from a third engineering service provider for one or more designs established along with a well trajectory. In such an example, the third engineering service provider may consider various factors as to whether the well engineering plan is acceptable, such as economic variables (e.g., oil production forecasts, costs per barrel, risk, drill time, etc.), and may request authorization for expenditure, such as from the operating company’s representative, well-owner’s representative, or the like (see, e.g., the formulation block 434). As an example, at least some of the data upon which such determinations are based may be stored in one or more database maintained by one or more servers. As an example, a first, a second, and/or a third engineering service provider may be provided by a single team of engineers or even a single engineer, and thus may or may not be separate entities.

[00110] As an example, where economics may be unacceptable or subject to authorization being withheld, an engineering service provider may suggest changes to casing, a bottom-hole assembly, and/or fluid design, or otherwise notify and/or return control to a different engineering service provider, so that adjustments may be made to casing, a bottom-hole assembly, and/or fluid design. Where modifying one or more of such designs is impracticable within well constraints, trajectory, etc., the engineering service provider may suggest an adjustment to the well trajectory and/or a workflow may return to or otherwise notify an initial engineering service provider and/or a G&G service provider such that either or both may modify the well trajectory.

[00111] As an example, a workflow can include considering a well trajectory, including an accepted well engineering plan, and a formation evaluation. Such a workflow may then pass control to a drilling service provider, which may implement the well engineering plan, establishing safe and efficient drilling, maintaining well integrity, and reporting progress as well as operating parameters (see, e.g., the blocks 344 and 348). As an example, operating parameters, formation encountered, data collected while drilling (e.g., using logging-while-drilling or measuring-while- drilling technology), may be returned to a geological service provider for evaluation. As an example, the geological service provider may then re-evaluate the well trajectory, or one or more other aspects of the well engineering plan, and may, in some cases, and potentially within predetermined constraints, adjust the well engineering plan according to the real-life drilling parameters (e.g., based on acquired data in the field, etc.).

[00112] Whether the well is entirely drilled, or a section thereof is completed, depending on the specific embodiment, a workflow may proceed to a post review (see, e.g., the evaluation block 418). As an example, a post review may include reviewing drilling performance. As an example, a post review may further include reporting the drilling performance (e.g., to one or more relevant engineering, geological, or G&G service providers).

[00113] Various activities of a workflow may be performed consecutively and/or may be performed out of order (e.g., based partially on information from templates, nearby wells, etc. to fill in any gaps in information that is to be provided by another service provider). As an example, undertaking one activity may affect the results or basis for another activity, and thus may, either manually or automatically, call for a variation in one or more workflow activities, work products, etc. As an example, a server may allow for storing information on a central database accessible to various service providers where variations may be sought by communication with an appropriate service provider, may be made automatically, or may otherwise appear as suggestions to the relevant service provider. Such an approach may be considered to be a holistic approach to a well workflow, in comparison to a sequential, piecemeal approach.

[00114] As an example, various actions of a workflow may be repeated multiple times during drilling of a wellbore. For example, in one or more automated systems, feedback from a drilling service provider may be provided at or near real-time, and the data acquired during drilling may be fed to one or more other service providers, which may adjust its piece of the workflow accordingly. As there may be dependencies in other areas of the workflow, such adjustments may permeate through the workflow, e.g., in an automated fashion. In some embodiments, a cyclic process may additionally or instead proceed after a certain drilling goal is reached, such as the completion of a section of the wellbore, and/or after the drilling of the entire wellbore, or on a per-day, week, month, etc., basis.

[00115] Well planning can include determining a path of a well (e.g., a trajectory) that can extend to a reservoir, for example, to economically produce fluids such as hydrocarbons therefrom. Well planning can include selecting a drilling and/or completion assembly which may be used to implement a well plan. As an example, various constraints can be imposed as part of well planning that can impact design of a well. As an example, such constraints may be imposed based at least in part on information as to known geology of a subterranean domain, presence of one or more other wells (e.g., actual and/or planned, etc.) in an area (e.g., consider collision avoidance), etc. As an example, one or more constraints may be imposed based at least in part on characteristics of one or more tools, components, etc. As an example, one or more constraints may be based at least in part on factors associated with drilling time and/or risk tolerance.

[00116] As an example, a system can allow for a reduction in waste, for example, as may be defined according to LEAN. In the context of LEAN, consider one or more of the following types of waste: transport (e.g., moving items unnecessarily, whether physical or data); inventory (e.g., components, whether physical or informational, as work in process, and finished product not being processed); motion (e.g., people or equipment moving or walking unnecessarily to perform desired processing); waiting (e.g., waiting for information, interruptions of production during shift change, etc.); overproduction (e.g., production of material, information, equipment, etc. ahead of demand); over processing (e.g., resulting from poor tool or product design creating activity); and defects (e.g., effort involved in inspecting for and fixing defects whether in a plan, data, equipment, etc.). As an example, a system that allows for actions (e.g., methods, workflows, etc.) to be performed in a collaborative manner can help to reduce one or more types of waste. [00117] As an example, a system can be utilized to implement a method for facilitating distributed well engineering, planning, and/or drilling system design across multiple computation devices where collaboration can occur among various different users (e.g., some being local, some being remote, some being mobile, etc.). In such a system, the various users via appropriate devices may be operatively coupled via one or more networks (e.g., local and/or wide area networks, public and/or private networks, land-based, marine-based and/or areal networks, etc.). [00118] As an example, a system may allow well engineering, planning, and/or drilling system design to take place via a subsystems approach where a wellsite system is composed of various subsystem, which can include equipment subsystems and/or operational subsystems (e.g., control subsystems, etc.). As an example, computations may be performed using various computational platforms/devices that are operatively coupled via communication links (e.g., network links, etc.). As an example, one or more links may be operatively coupled to a common database (e.g., a server site, etc.). As an example, a particular server or servers may manage receipt of notifications from one or more devices and/or issuance of notifications to one or more devices. As an example, a system may be implemented for a project where the system can output a well plan, for example, as a digital well plan, a paper well plan, a digital and paper well plan, etc. Such a well plan can be a complete well engineering plan or design for the particular project. [00119] Fig. 5 shows an example of a wellsite system 500, specifically, Fig. 5 shows the wellsite system 500 in an approximate side view and an approximate plan view along with a block diagram of a system 570.

[00120] In the example of Fig. 5, the wellsite system 500 can include a cabin 510, a rotary table 522, drawworks 524, a mast 526 (e.g., optionally carrying a top drive, etc.), mud tanks 530 (e.g., with one or more pumps, one or more shakers, etc.), one or more pump buildings 540, a boiler building 542, an HPU building 544 (e.g., with a rig fuel tank, etc.), a combination building 548 (e.g., with one or more generators, etc.), pipe tubs 562, a catwalk 564, a flare 568, etc. Such equipment can include one or more associated functions and/or one or more associated operational risks, which may be risks as to time, resources, and/or humans.

[00121] As shown in the example of Fig. 5, the wellsite system 500 can include a system 570 that includes one or more processors 572, memory 574 operatively coupled to at least one of the one or more processors 572, instructions 576 that can be, for example, stored in the memory 574, and one or more interfaces 578. As an example, the system 570 can include one or more processor-readable media that include processor-executable instructions executable by at least one of the one or more processors 572 to cause the system 570 to control one or more aspects of the wellsite system 500. In such an example, the memory 574 can be or include the one or more processor-readable media where the processor-executable instructions can be or include instructions. As an example, a processor-readable medium can be a computer-readable storage medium that is not a signal and that is not a carrier wave. [00122] Fig. 5 also shows a battery 580 that may be operatively coupled to the system 570, for example, to power the system 570. As an example, the battery 580 may be a back-up battery that operates when another power supply is unavailable for powering the system 570. As an example, the battery 580 may be operatively coupled to a network, which may be a cloud network. As an example, the battery 580 can include smart battery circuitry and may be operatively coupled to one or more pieces of equipment via a SMBus or other type of bus.

[00123] In the example of Fig. 5, services 590 are shown as being available, for example, via a cloud platform. Such services can include data services 592, query services 594 and drilling services 596. As an example, the services 590 may be part of a system such as the system 400 of Fig. 4. As an example, a service may include a test type service. For example, consider the drilling services 596 as including a test type service that can determine a type of test to perform by drillstring equipment at a wellsite.

[00124] As an example, the system 570 may be utilized to generate one or more rate of penetration drilling parameter values, which may, for example, be utilized to control one or more drilling operations.

[00125] Fig. 6 shows a schematic diagram depicting an example of a drilling operation of a directional well in multiple sections. The drilling operation depicted in Fig. 6 includes a wellsite drilling system 600 and a field management tool 620 for managing various operations associated with drilling a bore hole 650 of a directional well 617. The wellsite drilling system 600 includes various components (e.g., drillstring 612, annulus 613, bottom hole assembly (BHA) 614, kelly 615, mud pit 616, etc.). As shown in the example of Fig. 6, a target reservoir may be located away from (as opposed to directly under) the surface location of the well 617. In such an example, special tools or techniques may be used to ensure that the path along the bore hole 650 reaches the particular location of the target reservoir.

[00126] As an example, the BHA 614 may include sensors 608, a rotary steerable system (RSS) 609, and a bit 610 to direct the drilling toward the target guided by a pre-determined survey program for measuring location details in the well. Furthermore, the subterranean formation through which the directional well 617 is drilled may include multiple layers (not shown) with varying compositions, geophysical characteristics, and geological conditions. Both the drilling planning during the well design stage and the actual drilling according to the drilling plan in the drilling stage may be performed in multiple sections (see, e.g., sections 601, 602,

603 and 604), which may correspond to one or more of the multiple layers in the subterranean formation. For example, certain sections (e.g., sections 601 and 602) may use cement 607 reinforced casing 606 due to the particular formation compositions, geophysical characteristics, and geological conditions.

[00127] In the example of Fig. 6, a surface unit 611 may be operatively linked to the wellsite drilling system 600 and the field management tool 620 via communication links 618. The surface unit 611 may be configured with functionalities to control and monitor the drilling activities by sections in real time via the communication links 618. The field management tool 620 may be configured with functionalities to store oilfield data (e.g., historical data, actual data, surface data, subsurface data, equipment data, geological data, geophysical data, target data, anti-target data, etc.) and determine relevant factors for configuring a drilling model and generating a drilling plan. The oilfield data, the drilling model, and the drilling plan may be transmitted via the communication link 618 according to a drilling operation workflow. The communication links 618 may include a communication subassembly.

[00128] During various operations at a wellsite, data can be acquired for analysis and/or monitoring of one or more operations. Such data may include, for example, subterranean formation, equipment, historical and/or other data. Static data can relate to, for example, formation structure and geological stratigraphy that define the geological structures of the subterranean formation. Static data may also include data about a bore, such as inside diameters, outside diameters, and depths. Dynamic data can relate to, for example, fluids flowing through the geologic structures of the subterranean formation over time. The dynamic data may include, for example, pressures, fluid compositions (e.g. gas oil ratio, water cut, and/or other fluid compositional information), and states of various equipment, and other information.

[00129] The static and dynamic data collected via a bore, a formation, equipment, etc. may be used to create and/or update a three dimensional model of one or more subsurface formations. As an example, static and dynamic data from one or more other bores, fields, etc. may be used to create and/or update a three dimensional model. As an example, hardware sensors, core sampling, and well logging techniques may be used to collect data. As an example, static measurements may be gathered using downhole measurements, such as core sampling and well logging techniques. Well logging involves deployment of a downhole tool into the wellbore to collect various downhole measurements, such as density, resistivity, etc., at various depths. Such well logging may be performed using, for example, a drilling tool and/or a wireline tool, or sensors located on downhole production equipment. Once a well is formed and completed, depending on the purpose of the well (e.g., injection and/or production), fluid may flow to the surface (e.g., and/or from the surface) using tubing and other completion equipment. As fluid passes, various dynamic measurements, such as fluid flow rates, pressure, and composition may be monitored. These parameters may be used to determine various characteristics of a subterranean formation, downhole equipment, downhole operations, etc.

[00130] As an example, a system can include a framework that can acquire data such as, for example, real time data associated with one or more operations such as, for example, a drilling operation or drilling operations. As an example, consider the PERFORM toolkit framework (Schlumberger Limited, Houston, Texas). [00131] As an example, a service can be or include one or more of OPTI DRILL, OPTILOG and/or other services marketed by Schlumberger Limited, Houston,

T exas. [00132] The OPTIDRILL technology can help to manage downhole conditions and BHA dynamics as a real time drilling intelligence service. The service can incorporate a rigsite display (e.g., a wellsite display) of integrated downhole and surface data that provides actionable information to mitigate risk and increase efficiency. As an example, such data may be stored, for example, to a database system (e.g., consider a database system associated with the STUDIO framework). [00133] The OPTILOG technology can help to evaluate drilling system performance with single- or multiple-location measurements of drilling dynamics and internal temperature from a recorder. As an example, post-run data can be analyzed to provide input for future well planning.

[00134] As an example, information from a drill bit database may be accessed and utilized. For example, consider information from Smith Bits (Schlumberger Limited, Houston, Texas), which may include information from various operations (e.g., drilling operations) as associated with various drill bits, drilling conditions, formation types, etc.

[00135] As an example, one or more QTRAC services (Schlumberger Limited, Houston Texas) may be provided for one or more wellsite operations. In such an example, data may be acquired and stored where such data can include time series data that may be received and analyzed, etc.

[00136] As an example, one or more M-l SWACO services (M-l L.L.C.,

Houston, Texas) may be provided for one or more wellsite operations. For example, consider services for value-added completion and reservoir drill-in fluids, additives, cleanup tools, and engineering. In such an example, data may be acquired and stored where such data can include time series data that may be received and analyzed, etc.

[00137] As an example, one or more ONE-TRAX services (e.g., via the ONE- TRAX software platform, M-l L.L.C., Houston, Texas) may be provided for one or more wellsite operations. In such an example, data may be acquired and stored where such data can include time series data that may be received and analyzed, etc.

[00138] As an example, various operations can be defined with respect to WITS orWITSML, which are acronyms for well-site information transfer specification or standard (WITS) and markup language (WITSML). WITS/WITSML specify how a drilling rig or offshore platform drilling rig can communicate data. For example, as to slips, which are an assembly that can be used to grip a drillstring in a relatively non damaging manner and suspend the drillstring in a rotary table, WITS/WITSML define operations such as “bottom to slips” time as a time interval between coming off bottom and setting slips, fora current connection; “in slips” as a time interval between setting the slips and then releasing them, for a current connection; and “slips to bottom” as a time interval between releasing the slips and returning to bottom (e.g., setting weight on the bit), for a current connection.

[00139] Well construction can occur according to various procedures, which can be in various forms. As an example, a procedure can be specified digitally and may be, for example, a digital plan such as a digital well plan. A digital well plan can be an engineering plan for constructing a wellbore. As an example, procedures can include information such as well geometries, casing programs, mud considerations, well control concerns, initial bit selections, offset well information, pore pressure estimations, economics and special procedures that may be utilized during the course of well construction, production, etc. While a drilling procedure can be carefully developed and specified, various conditions can occur that call for adjustment to a drilling procedure.

[00140] As an example, an adjustment can be made at a rigsite when acquisition equipment acquire information about conditions, which may be for conditions of drilling equipment, conditions of a formation, conditions of fluid(s), etc. Such an adjustment may be made on the basis of personal knowledge of one or more individuals at a rigsite. As an example, an operator may understand that conditions call for an increase in mudflow rate, a decrease in weight on bit, etc.

Such an operator may assess data as acquired via one or more sensors (e.g., torque, temperature, vibration, etc.). Such an operator may call for performance of a procedure, which may be a test procedure to acquire additional data to understand better actual physical conditions and physical phenomena that may occur or that are occurring. An operator may be under one or more time constraints, which may be driven by physical phenomena, such as fluid flow, fluid pressure, compaction of rock, borehole stability, etc. In such an example, decision making by the operator can depend on time as conditions evolve. For example, a decision made at one fluid pressure may be sub-optimal at another fluid pressure in an environment where fluid pressure is changing. In such an example, timing as to implementing a decision as an adjustment to a procedure can have a broad ranging impact. An adjustment to a procedure that is made too late or too early can adversely impact other procedures compared to an adjustment to a procedure that is made at an optimal time (e.g., and implemented at the optimal time).

[00141] Fig. 7 shows an example of a graphical user interface (GUI) 700 that includes information associated with a well plan. Specifically, the GUI 700 includes a panel 710 where surfaces representations 712 and 714 are rendered along with well trajectories where a location 716 can represent a position of a drillstring 717 along a well trajectory. The GUI 700 may include one or more editing features such as an edit well plan set of features 730. The GUI 700 may include information as to individuals of a team 740 that are involved, have been involved and/or are to be involved with one or more operations. The GUI 700 may include information as to one or more activities 750.

[00142] As shown in the example of Fig. 7, the GUI 700 can include a graphical control of a drillstring 760 where, for example, various portions of the drillstring 760 may be selected to expose one or more associated parameters (e.g., type of equipment, equipment specifications, operational history, etc.). In the example of Fig. 7, the drillstring graphical control 760 includes components such as drill pipe, heavy weight drill pipe (HWDP), subs, collars, jars, stabilizers, motor(s) and a bit. A drillstring can be a combination of drill pipe, a bottom hole assembly (BHA) and one or more other tools, which can include one or more tools that can help a drill bit turn and drill into material (e.g., a formation).

[00143] As an example, a workflow can include utilizing the graphical control of the drillstring 760 to select and/or expose information associated with a component or components such as, for example, a bit and/or a mud motor. In the example of Fig. 7, a graphical control 765 is shown that can be rendered responsive to interaction with the graphical control of the drillstring 760, for example, to select a type of component and/or to specify one or more features of the drillstring 760 (e.g., for training a neural network model, etc.). As to the graphical control 765, it may be utilized to get a recommendation for a component and/or to determine what types of tests a component may be able to perform. For example, consider a workflow that can utilize a test framework (TF) that can determine type of test to perform at one or more depths in a borehole. In such an example, interactions with the GUI 700 may provide for automated selection and/or input-based selection of one or more pieces of equipment. [00144] As an example, a TF can output a schedule, which may be a schedule associated with depths for stages of drilling. As an example, a schedule may be a test schedule that relates to formation characteristics, depths, etc. As an example, a TF may determine a test type dynamically during performance of one or more drilling operations (e.g., drilling, tripping, etc.).

[00145] Fig. 7 also shows an example of a table 770 as a point spreadsheet that specifies information for a plurality of wells. As shown in the example table 770, coordinates such as “x” and “y” and “depth” can be specified for various features of the wells, which can include pad parameters, spacings, toe heights, step outs, initial inclinations, kick offs, etc.

[00146] Fig. 8 shows an example of a graphical user interface 800 that includes various types of information for construction of a well where times are rendered for corresponding actions. In the example of Fig. 8, the times are shown as an estimated time (ET) in hours and a total or cumulative time (TT), which is in days. Another time may be a clean time, which can be for performing an action or actions without occurrence of non-productive time (NPT) while the estimated time (ET) can include NPT, which may be determined using one or more databases, probabilistic analysis, etc. In the example of Fig. 8, the total time (TT or cumulative time) may be a sum of the estimated time column. As an example, during execution and/or replanning the GUI 800 may be rendered and revised accordingly to reflect changes. As shown in the example of Fig. 8, the GUI 800 can include selectable elements and/or highlightable elements. As an example, an element may be highlighted responsive to a signal that indicates that an activity is currently being performed, is staged, is to be revised, etc. For example, a color coding scheme may be utilized to convey information to a user via the GUI 800.

[00147] As to the highlighted element 810 (“Drill to depth (3530-6530 ft)”) the estimated time is 102.08 hours, which is greater than four days. For the drilling run for the 8.5 inch section of the borehole, the highlighted element 810 is the longest in terms of estimated time. Fig. 8 also shows a GUI 820 for a borehole trajectory and a GUI 830 of a portion of a drillstring with a drill bit where drilling may proceed according to a weight on bit (WOB) and a rotational speed (RPM) to achieve a rate of penetration (ROP) or where a test may be performed (e.g., after halting rotation of the drill bit). In the example of Fig. 8, the GUI 830 and parameters thereof may be associated with drill bit performance (e.g., ROP, wear, remaining life, etc.) and/or with performance of one or more tests. As an example, the GUI 830 may be operatively coupled to a test framework (TF) such that, for example, types of tests can be visualized (e.g., in relationship to a depth, etc.). For example, consider a GUI that can render a type of test to be performed, optionally in a ranked list where the types of tests in the ranked list may be generated using a TF that includes one or more machine learning (ML) models (e.g., one or more trained ML models).

[00148] As an example, the GUI 800 can be operatively coupled to one or more systems that can assist and/or control one or more drilling operations. For example, consider a system that generates rate of penetration values, which may be, for example, rate of penetration set points. Such a system may be an automation assisted system and/or a control system. For example, a system may render a GUI that displays one or more generated rate of penetration values and/or a system may issue one or more commands to one or more pieces of equipment to cause operation thereof at a generated rate of penetration (e.g., per a WOB, a RPM, etc.) and/or to select and/or perform a test. As an example, a time estimate may be given for the drill to depth operation using manual, automated and/or semi-automated drilling. For example, where a driller enters a sequence of modes, the time estimate may be based on that sequence; whereas, for an automated approach, a sequence can be generated (e.g., an estimated automated sequence, a recommended estimated sequence, etc.) with a corresponding time estimate. In such an approach, a driller may compare the sequences and select one or the other or, for example, generate a hybrid sequence (e.g., part manual and part automated, etc.).

[00149] As an example, a framework environment can include an option for execution of a framework that may run in the background, foreground or both. For example, consider executing the DRILLPLAN framework in the example system 100 of Fig. 1 where a test framework (TF) can be optionally instantiated for foreground and/or background execution that can assess information of the DRILLPLAN framework with respect to test type choices.

[00150] Fig. 9 shows an example of a system 910 for formation pressure testing and an example of a method 960. As shown, the system 910 can include a downhole tool 920 that includes formation pressure testing equipment 940. In the example of Fig. 9, the system 910 can include one or more features for sensing, transmitting, receiving, etc. For example, the system 910 can include a telemetry subsystem that can transmit and receive information, for example, via mud-pulse telemetry and/or one or more other telemetry techniques.

[00151] As to the formation pressure testing (FPT) equipment 940, it can include one or more mechanisms that can act to seal one or more ports of the downhole tool 920. For example, consider one or more pistons, one or more packers, etc. As to a piston or pistons, piston actuation can force the downhole tool 920 or a portion thereof against a formation such as a borehole wall to thereby create a seal between the formation and one or more ports of the FPT equipment 940. As to a packer or packers, packer actuation can expand one or more packers to create a seal between the downhole tool 920 or a portion thereof with respect to a formation. Such an approach may isolate one or more ports of the FPT equipment. [00152] As shown in the example of Fig. 9, the FPT equipment 940 can include various components, which are shown in a schematic view 941. As shown, FPT equipment components can include a pressure chamber 944, a pressure gauge 942 and a valve 946 where fluid can flow from one or more ports via flow passages such that a pressure test can be performed for formation pressure.

[00153] As an example, the system 910 can include one or more features of the SPECTRASPHERE system (Schlumberger Limited, Houston, Texas), which includes a pretest probe that can operate as a stand-alone formation pressure while drilling tool or can be combined with one or more other components, systems, etc. The pretest probe can perform various operations such as time-optimized and pumps-off pretesting. The pretest probe can include a high precision quartz gauge, valves and setting pistons. A system can include one or more stabilizers, which may be of a diameter of approximately 10 cm to approximately 40 cm. As an example, FPT equipment may be of a length more than one meter, for example, consider a length of approximately 1 m to approximately 20 m or more. A downhole tool may be rated with respect to tool curvature, for example, consider 8 degrees while rotating and 16 degrees while sliding.

[00154] As an example, the system 910 can include one or more features of the STETHOSCOPE system (Schlumberger Limited, Houston, Texas), which can include circuitry (e.g., memory, hardware, software, etc.) for performing various pretests. For example, consider circuitry that can control features and/or interpret data. Such circuitry can include one or more processors, cores, and memory, which may store processor executable instructions (e.g., firmware, etc.) and that may store data acquired via one or more sensors of a downhole tool that can be part of a drillstring (e.g., drillstring equipment). As an example, circuitry may be capable of storing one or more trained ML models that can receive data and output a type of test based at least in part on the data. In such an example, a downhole tool can be capable of making decisions downhole as to type of test to perform at a depth in a borehole. As an example, circuitry may be operable to optimize pretest volume and drawdown rate, for example, with respect to formation characteristics. For example, consider pretest volume being adjustable up to 25 cm³ or more and consider a drawdown rate that can be set from 0.1 cm³/s to 2.0 cm³/s. As to specifications, a volume may be given in cubic centimeters, which may be abbreviated as “cm³” or “cc”.

[00155] As an example, FPT equipment can be powered from a turbine, a battery pack, and/or one or more other power sources and/or power generation mechanisms. As to a battery pack, consider power sufficient to perform 50 pretests or more. Power management circuitry can reserve power for retraction of a piston, pistons, a packer, packers, etc.

[00156] As to data, FPT equipment can include circuitry that can provide for various indicators of performance, which may point to validity and/or invalidity of a pretest. Such indicators may be available in real time to help validate pressure data, indicate confidence, etc. As to transmission to surface equipment, various options may be available (e.g., standard, intermediate, advanced interpretation). Acquired data may be stored locally in memory, which may be later accessed, for example, one the FPT equipment has been brought to surface.

[00157] FPT equipment may operate in various modes. For example, consider a sleep mode, a standby mode and a deploy mode. In the deploy mode, the FPT equipment can be activated to set (e.g., seal) and perform a pressure test, followed by retraction (e.g., piston or packer), and return to a standby mode or entry into a sleep mode. A deploy mode sequence may take a number of minutes and may include a short downlink to trigger a next measurement (e.g., as desired).

[00158] As to the method 960, it includes an identification block 962 for identifying a location (e.g., via gamma log information, etc.), a seal block 964 for generating a seal (e.g., via piston, packer, etc.) at an identified location (e.g., where rotation of equipment is halted), a performance block 966 for performing a pretest, and a characterization block 968 for characterizing a formation based at least in part on pretest results. After the pretest, drilling may resume via one or more drilling modes (e.g., rotary, sliding, etc.), which may be controlled based at least in part on the pretest results.

[00159] In the example of Fig. 9, the method 960 is shown along with example plots 963, 965, 967 and 969. The plots 963, 965 and 967 are pressure versus time plots while the plot 969 is a depth versus pressure plot. In the plot 965, a time to is shown that indicates a pressure drop, while in the plot 967, times tib and tsb are shown, which demarcate a pressure build-up period. Unsealing can follow the time tsb, which can then end the pretest. As to the plot 969, the pressure values can be analyzed with respect to depth (e.g., vertical depth, measured depth, etc.) where one or more types of analyses may be performed (e.g., gradient analysis, etc.). One or more operational decisions (e.g., control, etc.) may be made using the pretest data and/or analysis thereof.

[00160] As an example, the characterization block 968 may be utilized to assess behavior of a reservoir during production, responsive to a treatment (e.g., chemical treatment, heat treatment, fracturing, etc.), and/or at a time of shut in. As to production, a pretest may provide information as to energy where the energy is a driving force for movement of fluid from a reservoir to a well. Where energy diminishes, a decision may be made to take one or more actions, which may aim to enhance production (e.g., enhanced oil recovery (EOR), etc.). As an example, a decision may be made to implement an artificial lift technology such as gas lift and/or electric submersible pump (ESP) lift. As explained, pretests may be performed at various stages (e.g., during tripping, during drilling, etc.). As to drilling, in some instances, a branch or lateral may be drilled in an existing well, which may provide for opportunities to perform pretests that may help guide direction, distance, etc., of one or more branches, etc. Decisions relating to simulating, stimulating, cementing, casing, injection, waterflooding, steering, mud formulation, mud weight, mud flow, etc., may be made using results from one or more pretests. As to mud decisions, such decisions may help to avoid pressure related issues (e.g., kicks, etc.). As explained, pretests may be utilized in various scenarios, whether onshore, offshore, shallow, deep, vertical, deviated, etc.

[00161] Fig. 10 shows an example of a pretest table 1000 that includes examples of pretests (e.g., tests). As shown, tests can include fixed type (Type 0) and time optimized (Type 1 or Type 2). Each of the tests is shown as including two flow rates (FR1 and FR2), two volumes (V1 and V2) and two times (T1 and T2), along with a total time (e.g., not including telemetry time). As mentioned, during performance of a test, drilling is halted. As such, performing a test does not deepen a borehole and may be considered non-productive time (NPT). Performing a test that does not provide desired or desirable results can be a waste.

[00162] As to telemetry times, where data are to be transmitted prior to pulling a drillstring out of a borehole (e.g., for reading at surface), they can depend on depth, quantity of data and various other factors. Telemetry for pretest data (e.g., raw and/or processed) may take an amount of time that is of the order of minutes, which can be tens of minutes. Telemetry can provide early notice as to whether a test is valid or invalid. As an example, each of the tests in the table 1000 can be associated with a code that can be transmitted via telemetry (e.g., a short code for ease of transmission, etc.).

[00163] In various instances, for example, for a particular field, some tests may be performed more than others. For example, of the tests in the table 1000, in some instances types 0-B and 1-B may be more frequently selected. As an example, a number of predefined test types may be greater than or equal to two.

[00164] The Type 0 tests include 0-A, 0-B, 0-C and 0-D. These types can be evolved over time as may be corresponding to particular types of formations. As shown, the Type 1 and 2 tests include 1-A, 1-B, 2-C and 2-D. As to the type 0-D, the times T 1 and T2 can depend on particular equipment (e.g., different models of a probe), as represented by a slash between two entries.

[00165] As an example, a method can include implementing a trained machine learning model (ML model) to select a test to be performed using a downhole tool that includes FPT equipment. As an example, a method can include training a ML model to generate a trained ML model where the ML model provides for test type selection for real time drillstring based reservoir monitoring (e.g., pressure formation testing, etc.).

[00166] Reservoir data can be acquired using a formation tester (e.g., formation testing equipment). Data can include formation pressure and mobility data, which when acquired via a drillstring, can be referred to as formation pressure while drilling (FPWD). FPWD can be employed for at various times for various types of wells, for example, during development and for high angle wells. [00167] As to logging while drilling (LWD) tools, for communications uphole and/or downhole, they tend to be dependent on pulse telemetry, which can be of a limited bandwidth (e.g., mud-pulse telemetry). Mud-pulse telemetry can impose limits as to interaction and control. Mud-pulse telemetry can provide for transmitting data (e.g., LWD and MWD data) acquired downhole using downhole equipment to surface equipment using pressure pulses in mud (e.g., drilling fluid) that can be present in an annulus between a tubular and a casing, an open hole borewall, etc. Data may be converted into an amplitude- or frequency-modulated pattern of mud pulses. Mud-pulse telemetry may be utilized to transmit commands from surface equipment to downhole equipment. Where available, a wire-based telemetry system may be utilized and/ora wireless electromagnetic telemetry system may be utilized. [00168] Mud-pulse telemetry systems can include one or more of positive- pulse, negative-pulse, and continuous-wave systems. Negative-pulse systems create a pressure pulse lower than that of the mud volume by venting a small amount of high-pressure drillstring mud from a drillpipe to an annulus. Positive-pulse systems create a momentary flow restriction (e.g., higher pressure than the drilling- mud volume) in a drillpipe. Continuous-wave systems create a carrier frequency that is transmitted through the mud, and they encode data using the phase shifts of the carrier. Data-coding systems may help to optimize life and reliability of a pulser (e.g., pressure pulse equipment). In various systems, a turbine may be controllable for generation of mud pulses. Telemetry-signal detection may be performed by one or more transducers located at surface and/or downhole.

[00169] As explained, a LWD formation tester tool can provide different test types. A type of test or types of tests may be selected based on expected reservoir quality, for example, as may be understood from one or more logs, the wellbore environment, etc. A selected test or tests may aim to provide for efficient reservoir characterization.

[00170] As explained, a test takes time and occurs without drilling that deepens a borehole. The approach to test selection impacts the ability to achieve a high level of success. Choice can depend on adequate test volumes, rates and times, as may be set forth in a library of downhole test types.

[00171] A test may be selected with an aim to match the test to response of certain reservoir characteristics such as, for example, rock permeability.

Inappropriate assumptions for test type selection (e.g., too aggressive or too passive for what a formation can deliver) and/or no result lead to an invalid test. When a test is deemed invalid, another test may be called for, which will add additional time and delay. As time can equate to waste (e.g., NPT), improper test selection can lead to losses of thousands of dollars per day.

[00172] As explained, a trained ML model can facilitate test selection where a library of tests can be preprogrammed and optionally tailored suitably to respond accordingly to certain reservoir characteristics (e.g., rock permeability, etc.).

[00173] ML model selection, training technique, training data, etc., are factors that can make or break an ML model based approach to test selection. For example, complexity of interpreting test data, limited functionalities of tools, low success rate, and poor quality of acquired data can impact an ability to implement a ML model based approach to test selection.

[00174] A ML model based approach can offer increased independence from reliance on individual experience and can improve risk identification, foster faster knowledge sharing among teams and leverage lessons and data acquired from historical data gathered from fields. A ML model based approach can increase the value of historical data and capture knowledge that may have been previously hidden. For example, training and testing of a ML model or ML models can help to uncover trends, indicators, etc., in data.

[00175] A ML model based approach can provide for test type output, where a trained ML model or ML models can output a particular type as a class based on data, which can include real time data during field operations (e.g., drilling, etc.). As explained, a method can provide for acquiring reservoir data relating to pressure and mobility in a reservoir. A ML model based approach can be versatile and adaptive to an operating environment and an operator's demands.

[00176] As to types of ML models, consider one or more classifiers where each type of test can be considered to be a class. An ML model may utilize a tree structure such as a decision tree where one or more decisions are made to arrive at a type of test to be selected.

[00177] As an example, one or more boosting tree types of ML models may be utilized. Boosting can be an iterative, ensemble approach where various models can be combined to perform a task. In boosting, models can be trained in succession, with each new model being trained to adjust for error made by one or more prior models. As an example, models can be added sequentially until an improvement limit is reached (e.g., progressing to another model does not reduce error below an error cutoff).

[00178] As mentioned, boosting, as an iterative approach, can add new models that focus on accounting for mistakes which were caused by other models. In contrast to a standard ensemble approach, where models can be trained individually and possibly make common mistakes, boosting aims to account for mistakes. [00179] In Gradient Boosting, new models can be trained to predict residuals (e.g., errors) of prior models. A library known as XGBoost (extreme Gradient Boost) provides various features for building an XGBoost ML model, which may be an ensemble of models. Using pip in a Python virtual environment, the following command can be executed: pip install xgboost. As an example, the scikit-learn framework may be utilized, for example, for import of datasets, processing of data, etc. For example, consider the following code for a dataset “iris”: from sklearn import datasets import xgboost as xgb iris = datasets. load_iris()

X = iris data y = iris.target

[00180] In such an example, the dataset can be partitioned into a training set and a testing set (e.g., consider an 80/20 partition), followed by data formatting: from sklearn. model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.2)

D_train = xgb.DMatrix(X_train, label=Y_train)

D_test = xgb.DMatrix(X_test, label=Y_test)

[00181] An XGBoost model can be defined, for example, using parameters of a gradient boosting ensemble. For example, consider one or more of the following parameters: param = { 'eta': 0.3,

'max_depth': 3,

'objective': 'multi:softprob',

'num_class': 3} steps = 20 # The number of training iterations

[00182] Above, max_depth is the maximum depth of the decision trees being trained, objective is the loss function being used, and num_class is the number of classes in the dataset. The parameter eta pertains to fitting, where appropriate selection of a value for eta can help to reduce overfitting (e.g., consider 0.01 to 0.3 or more). The parameter eta can be multiplied by residuals being adding to reduce their weight, which can effectively reduce complexity of an overall model.

[00183] Gradient Boosting involves creating and adding decision trees to an ensemble model sequentially. New trees can be created to account for residual errors in the outcomes from the existing ensemble.

[00184] Using the scikit-learn framework, a defined model can be trained using, for example: model = xgb.train(param, D_train, steps). The trained model can then be evaluated, for example, with respect to accuracy.

[00185] Another parameter is the XGBoost gamma parameter, which can also help with controlling overfitting (not to be confused with gamma as a measurement of a formation). The XGBoost gamma parameter specifies the minimum reduction in the loss to make a further partition on a leaf node of the tree. Another parameter is the booster parameter, which allow for setting the type of model to use when building an ensemble. For example, consider gbtree which builds an ensemble of decision trees. Another option may be gblinear, which builds an ensemble of linear models. [00186] The XGBoost library can be utilized to optimize setting of hyperparameters of a ML model, for example, via a grid search with an XGBoost classifier: from sklearn.model_selection import GridSearchCV elf = xgb.XGBCIassifierO parameters = { "eta" : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,

"max_depth" : [ 3, 4, 5, 6, 8, 10, 12, 15],

"min_child_weight" : [ 1, 3, 5, 7 ],

"gamma" : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],

"colsample_bytree" : [ 0.3, 0.4, 0.5 , 0.7 ]

} grid = GridSearchCV(clf, parameters, nJobs=4, scoring="neg_log_loss", cv=3) grid.fit(X_train, Y_train)

[00187] An article by Chen and Guestrin, XGBoost: A Scalable Tree Boosting System, KDD’16, August 13-17, 2016, San Francisco, CA, USA, is incorporated by reference herein. An article by Chawla et al. , SMOTE: Synthetic Minority Over- sampling Technique, Journal of Artificial Intelligence Research 16 (2002) 321-357, is incorporated by reference herein. An article by Tomek, Two Modifications of CNN [Condensed Nearest Neighbor], IEEE Transactions on Systems Man and Communications, SMC-6:769-772, 1976, is incorporated by reference herein. An article by Batista et al., Balancing Training Data for Automated Annotation of Keywords: a Case Study, Conference: II Brazilian Workshop on Bioinformatics, December 3-5, 2003, Macae, RJ, Brazil, is incorporated by reference herein.

[00188] As an example, a method can include utilizing SMOTE, which is an approach to construction of classifiers from imbalanced datasets. For example, a dataset can be imbalanced if the classification categories are not approximately equally represented. In the context of types of tests, consider historical data for a number of tests such as eight different types of tests where equal representation would be 12.5 percent of the historical data corresponding to each of the eight different types of tests. As some types of tests may be implemented more commonly, some amount of imbalance can exist. For example, consider one type of test being at 30 percent (e.g. over represented) and another type of test being at 3 percent (e.g., under represented). As an example, one or more percentages can be utilized as parameters for particular fields, tests, etc., such that a SMOTE approach may be implemented as desired and appropriately tailored.

[00189] As an example, over-sampling of a minority class and under-sampling of a majority class can be utilized for improved classifier performance (e.g., compared to solely under-sampling the majority class).

[00190] Example pseudo-code for SMOTE

Algorithm SMOTE (T, N, k)

Input: Number of minority class samples T ; Amount of SMOTE N%; Number of nearest neighbors k

Output: (N/100) ^* T synthetic minority class samples

1. (* If N is less than 100%, randomize the minority class samples as only a random percent of them will be SMOTEd. *)

2. if N < 100

3. then Randomize the T minority class samples

4. T = (N/100) * T

5. N = 100

6. endif

7. N = (int)(N/100) (* The amount of SMOTE is assumed to be in integral multiples of 100. *)

8. k = Number of nearest neighbors

9. numattrs = Number of attributes

10. Sample[ ][ ]: array for original minority class samples

11. newindex: keeps a count of number of synthetic samples generated, initialized to 0

12. Synthetic[ ][ ]: array for synthetic samples

(* Compute k nearest neighbors for each minority class sample only. *)

13. for i < — 1 to T

14. Compute k nearest neighbors for i, and save the indices in the nnarray

15. Populate(N, i, nnarray)

16. endfor

Populate(N, i, nnarray) (* Function to generate the synthetic samples. *) 17. while N6= 0

18. Choose a random number between 1 and k, call it nn. This step chooses one of the k nearest neighbors of i.

19. for attr <— 1 to numattrs

20. Compute: dif = Sample[nnarray[nn]][attr] - Sample[i][attr]

21. Compute: gap = random number between 0 and 1

22. Synthetic[newindex][attr] = Sample[i][attr] + gap * dif

23. endfor

24. newindex++

25. N = N - 1

26. endwhile

27. return (* End of Populate. *)

End of Pseudo-Code.

[00191] As an example, a method can include utilizing a combination of SMOTE and Tomek Links undersampling. As explained, SMOTE can be implemented as an oversampling approach that synthesizes new plausible examples in a minority class. Tomek Links refers to an approach for identifying pairs of nearest neighbors in a dataset that have different classes. In such an approach, removing one or both of the examples in these pairs (such as the examples in the majority class) has the effect of making the decision boundary in the training dataset less noisy or ambiguous. As an example, SMOTE can be applied to oversample a minority class to achieve a more balanced distribution, then examples in Tomek Links from one or more majority classes can be identified and removed.

[00192] As an example, a trained ML model can be utilized to automate and enable test type selection. In such an example, an XGBoost approach may be utilized, optionally with one or more approaches where an imbalance may exist in test training data.

[00193] A ML model based approach can replace or augment personal selection and can be data-driven. A ML model based approach can be trained using well logs and/or interpretations of well logs. Such an approach may be implemented where a subject matter expert is not available for making test type decisions.

[00194] Reservoir characteristics, especially reservoir pressure and mobility can be quite valuable for reservoir delineation, volumetric estimates, field development plans and for overall evaluation of a field. Thus, an approach that improves accuracy of measurement and data gathering can be beneficial. As explained, various types of exploration, appraisal, and production well tests tend to be a source of information.

[00195] As the volume of newly drilled high deviated wells and horizontal wells becomes larger and larger in brown fields, demand for logging acquisition in a drilling environment also increases as such acquisition can be a cost-effective way to get logging data in such wells. With reservoir pressure data, achieving high level of success depends on choosing the best test type from an array of downhole test type measurements. In various instances, complexity exists because, once set, the installed library of a downhole tool cannot be altered. And, where desired data are not attainable from a test, one or more alternative tests may be called for at a location, which can increase non-productive time (NPT). Test selection tends to be made on the basis of extensive domain expertise, suggesting the type of test to acquire reservoir pressure and mobility with confidence and reliability. Improper assumptions during test type selection and/or no result in an invalid test generally mean that the test will have to be repeated or an alternative test performed. Test selection may be a local endeavor (in the field) or a remote endeavor (at a monitoring center, etc.). Appropriate test selection can be quite beneficial, in reducing NPT during drilling, in improved drilling, in improved completions, etc. [00196] While XGBoost is mentioned, one or more other types of ML model approaches may be implemented. As to types of machine learning models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model can be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naive Bayes, average on- dependence estimators, Bayesian belief network, Gaussian naive Bayes, multinomial naive Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.

[00197] As an example, a machine model may be built using a computational framework with a library, a toolbox, etc., such as, for example, those of the MATLAB framework (MathWorks, Inc., Natick, Massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor (KNN), k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Another MATLAB framework toolbox is the Deep Learning Toolbox (DLT), which provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The DLT provides convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. The DLT includes features to build network architectures such as generative adversarial networks (GANs) and Siamese networks using custom training loops, shared weights, and automatic differentiation. The DLT provides for model exchange various other frameworks.

[00198] As an example, the TENSORFLOW framework (Google LLC, Mountain View, CA) may be implemented, which is an open source software library for dataflow programming that includes a symbolic math library, which can be implemented for machine learning applications that can include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley Al Research (BAIR) (University of California, Berkeley, California). As another example, consider the SCIKIT platform (e.g., scikit-learn), which utilizes the PYTHON programming language. As an example, a framework such as the APOLLO Al framework may be utilized (APOLLO.AI GmbH, Germany). As an example, a framework such as the PYTORCH framework may be utilized (Facebook Al Research Lab (FAIR), Facebook, Inc., Menlo Park, California).

[00199] As an example, a training method can include various actions that can operate on a dataset to train a ML model. As an example, a dataset can be split into training data and test data where test data can provide for evaluation. A method can include cross-validation of parameters and best parameters, which can be provided for model training.

[00200] The TENSORFLOW framework can run on multiple CPUs and GPUs (with optional CUDA (NVIDIA Corp., Santa Clara, California) and SYCL (The Khronos Group Inc., Beaverton, Oregon) extensions for general-purpose computing on graphics processing units (GPUs)). TENSORFLOW is available on 64-bit LINUX, MACOS (Apple Inc., Cupertino, California), WINDOWS (Microsoft Corp., Redmond, Washington), and mobile computing platforms including ANDROID (Google LLC, Mountain View, California) and IOS (Apple Inc.) operating system based platforms. [00201] TENSORFLOW computations can be expressed as stateful dataflow graphs; noting that the name TENSORFLOW derives from the operations that such neural networks perform on multidimensional data arrays. Such arrays can be referred to as "tensors".

[00202] As to some examples of classifiers, consider the lazypredict framework, the xgboost and lightgbm Python packages, etc. As to the lazypredict framework, it can utilize the scikit-learn framework:

[00203] Example code: from lazypredict.Supervised import LazyClassifier from sklearn. datasets import load_DATA1 from sklearn. model_selection import train_test_split data = load_DATA1 ()

X = data. data y= data. target X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =123) elf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None) models, predictions = clf.fit(X_train, X_test, y_train, y_test) models . . .

[00204] As to models, consider one or more of LinearSVC, SGDCIassifier, MLPCIassifier, Perceptron, LogisticRegression, LogisticRegressionCV, SVC, CalibratedClassifierCV, PassiveAggressiveClassifier, LabelPropagation, LabelSpreading, RandomForestClassifier, GradientBoostingClassifier, QuadraticDiscriminantAnalysis, HistGradientBoostingClassifier, RidgeClassifierCV, RidgeClassifier, AdaBoostClassifier, ExtraTreesClassifier, KNeighborsClassifier, BaggingClassifier, BernoulliNB, LinearDiscriminantAnalysis, GaussianNB, NuSVC, DecisionTreeClassifier, NearestCentroid, ExtraTreeClassifier, CheckingClassifier, DummyClassifier, etc.

[00205] In various trials of machine learning using characteristics of well logs for purposes of test type selection, models with suitable performance included: Random Forest, Light Gradient Boosting Machines (LGBM) and extreme Gradient Boosting (XGBoost) Machines, where the latter exhibited results that were better than the others.

[00206] In various trials, a stratified shuffle split cross-validator was applied on data to preserve samples of each test type in both training and testing dataset. Then the hyperparameters of the model were optimized using randomized grid-search and cross-validation, thus tuning the classifier while reducing overfitting. To evaluate the prediction accuracy of the model, an F1 score of more than 98 percent was achieved keeping a balance between precision and recall of the model.

[00207] Utilizing the XGBoost approach, performance was optimized for performance of formation testers downhole for a variety of reservoirs. As an example, a method may be supplemented by or integrated within, for example, a standard operating procedures (SOP).

[00208] Well test operations tend to be quite challenging operations that are performed at a wellsite, especially in tight oil and gas reservoirs with limited capability or inability of a well to flow naturally and produce reservoir hydrocarbons to surface. Tests such as drill stem tests (DSTs) may also not be applicable in some cases due to the high cost of operations or other constraints. Over time, environmental considerations and regulations have become stricter such that there are more limiting factors for conducting conventional DST operations in some geographical areas. Given such changes, a trend exists toward use of formation testers to gather data to characterize wells and determine reservoir information and minimize field development risks.

[00209] Formation testing is utilized for reservoir characterization, optionally in conjunction with one or more other techniques such as, for example, log interpretation, core analysis, and well testing. The objective from running a formation tester can depend on well type. For example, it can go from simply measuring reservoir pressure in a development well for managing the depletion program to, in the case of an exploration well, discovering the existence of hydrocarbons, identifying formation fluid type and fluid contacts (e.g., from pressure gradient and downhole fluid analysis) as well as one or more other features that a formation tester may offer (e.g., mini-DST, stress test and micro-frac, etc.). As to micro-frac, it may be a type of test process that is performed during a hydraulic fracturing workflow. The results from a micro-frac (e.g., or mini-frac) can be utilized in determining parameters for hydraulic fracturing.

[00210] As explained, reservoir delineation of brown fields can facilitate operational planning and operations such as, for example, operational planning for well placement and drilling of wells. Tools such as LWD tools can be utilized to acquire data during drilling operations in wells such as highly deviated and horizontal wells. Acquired data can be utilized as input for planning infill well drilling and monitor well drilling, for example, by addressing a depletion profile of a reservoir and optimizing mud weight as a by-product.

[00211] As shown in Fig. 9, a tool can include various components for making measurements. As an example, a tool can include components for forming a kind of connecting vessel between a tool flowline and a formation behind mud cake. In such an example, a probe can be set against a borehole wall using a setting piston or setting pistons that push on one portion of the borehole wall to force contact between a portion of the probe and another portion of the borehole wall that includes a port or ports such that a flowline or flowlines inside the tool fluidly connects or connect with the formation, for example, with mud cake in between. As shown in Fig. 9, a tool can include a chamber 944 that includes a chamber piston. In such an example, the chamber piston of the chamber 944 can draw back to generate a negative pressure differential across the mud cake where, at some point, the mud cake is broken due to the force caused by pressure differential. With the mud cake broken, the flowline can be fully fluidly connected with the formation. The fluid inside the formation (e.g., mud filtrate) can then start to flow into the flowline and compensate for the pressure drop caused by the chamber piston backward movement inside the chamber (e.g., pressure drawdown and buildup). When the pressure buildup period ends, a stabilized last read buildup pressure can be taken to be a measured formation pressure. After the pretest finishes, the recycling of pretest starts to squeeze the mud filtrate back into the annulus between the tool and the borehole wall. Then the probe and setting piston(s) can retract. During the process, the tool is parked and largely stationary, being positioned on a specific depth for gathering a measurement. [00212] As explained, various features can vary from tool to tool. As an example, a tool can include components for pretest volume that is adjustable (e.g., from approximately 0 cc to approximately 25cc with flow rates from approximately 0.2 cc/s to approximately 2.0 cc/s.

[00213] As an example, a tool can include an electromechanical pretest mechanism. Such a mechanism can help to reduce uncertainty of the volume drawn down, which is inherent in hydraulic systems. Accurate volume draw-downs also tend to improve accuracy of the drawdown mobility calculation while the ability to accurately control small volume drawdowns is imperative for low mobility zones to enable shorter buildup times and thus minimizing stationary time for the tool.

[00214] Referring again to the example table 1000 of Fig. 10, an operator can select a pretest with a fixed sequence of rates, volumes and buildup times based on expected mobility of the zone to be tested. If the mobility is unknown, an intelligent pretest can be selected. As shown in the table 1000, the eight different types of tests each utilize a minimum of two draw-downs and buildups. Such draw-downs and buildups are executed by a tool prior to retraction of a piston, pistons, packer, packers, etc.

[00215] When selecting volumes and rates to perform a pretest that renders a valid formation pressure, an operator tends to make certain assumptions of formation characteristics, for example, consider assumptions as to the overbalance, expected mobility, etc. The overbalance tends to dictate the amount of volume expansion demanded to decompress a flowline, the mobility relates to a formation being able to contribute a sufficient volume of fluid to reach a stabilized pressure in a reasonable time during the buildup.

[00216] Given the nature of the assumptions, pretest selection benefits from extensive domain expertise, suggesting the type of test to acquire reservoir pressure and mobility with confidence and reliability. As explained, proper pretest selection and execution can reduce NPT where results can benefit decision making, further operations, etc.

[00217] As explained, a ML model based approach can facilitate decision making as to pretest type selection. Such an approach can utilize one or more decision tree models, which can be classification models where each pretest type may be considered a class. A trained ML model can, for example, receive input such as data indicative of reservoir characteristics and progress through features of the trained ML model (e.g., decision structures, etc.) to arrive at a class.

[00218] As an example, a ML model based approach can be utilized to output a type of test for a particular depth in a borehole, which can be a measured depth (MD). The type of test can be probabilistically the best type of test to perform at that particular depth and can be based on one or more measurements using one or more types of sensors, noting that measured depth (MD) can be a measurement. As to some examples of measurements, consider gamma ray (GR), phase shift resistivity (RES), bulk density (ROBB) and thermal neutron porosity (TNPH). In various instances, a GR log of a borehole or a portion thereof may be available as previously measured with respect to depth. Such a log can be utilized for purposes of confirmation of a depth of a drillstring. For example, consider comparing a real time GR measurement to a GR log where a matching process may be utilized between the real time GR measurement and the GR log to determine and/or to confirm a depth.

[00219] As an example, a workflow may include generating log data for a borehole during tripping in of a drillstring, during tripping out of a drillstring, during drilling using a drillstring and/or while a drillstring is stationary.

[00220] As to a GR log, it can include data as to total natural radioactivity, which may be measured in American Petroleum Institute (API) units. Depth of investigation tends to be in centimeters (e.g., 1 cm to 10 cm) such that the GR log normally measures a flushed zone. Shales and clays tend to be responsible for most natural radioactivity such that a GR log may be an indicator of such rocks. Various other rocks are also radioactive, notably some carbonates and feldspar-rich rocks. A GR log may be used for correlation between wells, for depth correlation between open and cased hole, and for depth correlation between logging runs. [00221] As to a RES log, it can characterize a formation’s ability to resist electrical conduction, as derived from the change in position of the peaks of an electromagnetic wave generated in a propagation resistivity measurement. At the frequencies used, the phase shift depends mainly on resistivity of material with a small dependence on dielectric permittivity, particularly at high resistivity.

[00222] As to a ROBB log, it can provide indications as to bulk density of a formation, for example, based on the reduction in gamma ray flux between a source and a detector due to Compton scattering. In a LWD tool, a sleeve may be mounted on a collar around sensors to exclude mud (e.g., drilling fluid). Detectors can measure gamma rays scattered from the formation. Mudcake and/or borehole rugosity can affect ROBB log measurements and compensation for mudcake may occur via use of two or more detectors at different spacings.

[00223] As to a TNPH log, it can be for slowing down and capture of neutrons between a source and one or more thermal neutron detectors. A neutron source emits high-energy neutrons that are slowed mainly by elastic scattering to near thermal levels. Thermal neutrons have about the same energy as the surrounding matter, for example, less than about 0.4 eV. The slowing-down process tends to be dominated by hydrogen. At thermal levels, the neutrons diffuse through the material until they undergo thermal capture. Capture tends to be dominated by chlorine, hydrogen and other thermal neutron absorbers. A tool may include a chemical neutron source and two thermal neutron detectors. In various tools, an accelerator source (neutron generator) may be used.

[00224] An ML model based approach can harness computational resources along with logic and data structures to recognize hidden pattern or relationships in data. The established relationships may be referred to as models where such models can be used to draw one or more conclusions about input data where such input data were not part of the training data (e.g., consider input data as new data). As an example, a ML model based approach can utilize one or more types of learning, which may include one or more of unsupervised learning, supervised learning and reinforcement learning. In various examples, supervised learning is implemented to train a ML model, which may be an ensemble of models. [00225] Fig. 11 shows an example of a method 1100 that includes a process block 1110 for processing data such as the data 1104, a train block 1120 for training one or more ML models such as the model 1124, and an output block 1130 for outputting one or more trained ML models such as the model 1134.

[00226] In the example of Fig. 11, the data 1104 include log data associated with pretests (PTs) at particular measured depths (MDs). The log data include gamma ray (GR), resistivity (RES), bulk density (ROBB) and thermal neutron porosity (TNPH) data with values with respect to MD. The data 1104 can include test type indicators for each of the PTs. For example, each PT can be associated with a particular type of test that was run in the field at the corresponding depth where, at that depth, the formation is characterized at least in part by at least a portion of the log data. In such an example, some relationship or relationships exist between the log data and the test type, though such relationship or relationships may be “hidden” in that they are not readily apparent to the human eye upon observation of the data 1104.

[00227] As to the process block 1110, it can provide for understanding data points and constraints and formulating a data analytics strategy. As to the train block 1120, it can include modeling using one or more approaches. For example, consider XGBoost, which, depending on the nature of the data, may include one or more of SMOTE and Tomek Links.

[00228] The process block 1110 may provide for data exploration, which can include detecting anomalies and patterns in data at an initial investigation stage.

The data acquisition from a formation pressure-while-drilling (FPWD) measurements tool with a particular pretest type is done at a depth, performed in a reservoir. A reservoir engineer can suggest a pretest type to measure formation pressure and fluid mobility from the FPWD measurements tool, for example, on the basis of measured depth and values of open hole logs (e.g., GR, RES, ROBB, TNPH, etc.).

A database or databases can include data of different pretest types that have been collected from multiple wells (e.g., offset wells, etc.) from a particular field.

[00229] As an example, based on the selection of a pretest type, a formation will comprehend to it in the following ways: it can either be a valid test, supercharged, tight test, lost seal or dry test. As the name suggests, a valid test is desired for determining the formation pore pressure and near-wellbore mobility.

Thus, having pretest-summary reports from many wells as a source of data, the process block 1110 can provide for identification of depths of a formation having a valid test type. The process block 1110 can exclude failed tests, which may be referred to as invalid tests. As explained, log data such as open hole logs are generally utilized by a reservoir engineer to decide which particular test type to perform. As such, log data can be included as data for modeling (e.g., training and/or testing). Such log data can be organized for various wells and zones where valid tests have been performed, which may be demarcated by depth. The process block 1110 can prepare data where the data include log data, depths and pretest type. Such data can be quality checked where, for example, one or more outliers may be removed. The processed data may then be utilized for building and testing over one or more ML models.

[00230] As explained, the train block 1120 can utilize one or more approaches to modeling. One approach may include use of a Random Forest model that grows decision trees independently and aggregates the results in the final stage. Another approach can include another ensemble model where trees are grown sequentially in order to minimize residuals where, for example, gradient descent can be used to minimize a loss function. Such an approach includes models of a family referred to as Gradient Boosting Machines. As explained, XGBoost is an example of a Gradient Boosting Machine.

[00231] As explained, XGBoost performance may be improved using one or more of SMOTE and Tomek Links. For example, where data are imbalanced, an approach such as SMOTE may be applied, which is an over-sampling method that creates synthetic data for minority class(es) by interpolating between nearest neighbors from a minority class. SMOTE can implement a formula such as: [(sample)]_new=[(sample)]_i+a (sample_i^Aneighbor-sample_i), where sample_i^Aneighboris one of the k-nearest neighbours from samplej and a is a random number in the range [0,1]

[00232] In various instances, balancing data alone may not be sufficient to achieve a desired level of accuracy score as there may be an overlap between the majority and minority classes in a feature space. A complementary method, Tomek Links, can be utilized to clean data after oversampling with SMOTE, for example, by removing noisy samples from the classes.

[00233] As an example, the train block 1120 can implement XGBoost, optionally with one or more of SMOTE and Tomek Links as applied to data. In the example of Fig. 11, the method 1100 can include application of SMOTE and T omek Links by the process block 1110 and/or by the train block 1120, each of which may be informed by the ML model based approach to be utilized in the train block 1120. For example, particular approaches to address data issues may be related to what ML model or ML models are to be trained (e.g., as to how training and performance may be impacted).

[00234] Fig. 12 shows an example of a trained ML model 1210 and an example plot 1230 of actual versus model output pretest type for particular log data versus depth. As shown, the trained ML model 1210 can include nodes, branches and ultimately leaves where each leaf corresponds to a pretest type (e.g., labeled from 1 to 8, which may correspond to the types in the table 1000). In the example trained ML model 1210 of Fig. 12, various decisions can be made based on measured depth (MD) or another factor (e.g., a value of log data at a depth, etc.). In the example plot 1230, the model output from the trained ML model 1210 matched the actual pretest type at the three measured depths shown. As an example, training data can include data from offset wells that can be in the same field as a well that is being drilled. Where formation depths (e.g., reservoir depths) may be relatively even across the field, measures such as measured depth (e.g., or total vertical depth, etc.) can be factors in a trained ML model or ML models.

[00235] As to the output shown in the plot 1230, an output may include one or more additional types of data, which may indicate confidence, probability, etc. As an example, output may include a ranking of test types per a trained ML model or ML models. In such an example, one or more of confidence, probability, etc., may accompany one or more of the ranked test types.

[00236] As explained, various ML models can capture correlations between reservoir characteristics (e.g., rock petrophysics features such as depth, density, porosity, resistivity and gamma ray) and a target variable (e.g., test type). A trained ML model or models can provide for efficient classification during drilling operations of a well, which can be a well that was not part of a training dataset.

[00237] As an example, a trained ML model can help to optimize performance of formation testers downhole for a variety of reservoirs. In various examples, features exhibited high correlation using a one-way ANOVA statistical test. Of a variety of ML models examined, the best performing models were Random Forest, Light Gradient Boosting Machines (LGBM) and XGBoost, where the latter provided somewhat better results than the others.

[00238] As explained, where class imbalance may exist, to address low performance on one or more of the least represented classes, a SMOTE and Tomek Links approach may be implemented, which can provide a combination between oversampling and under-sampling techniques. The effectiveness of the XGBoost, SMOTE and Tomek Links approach is reflected by a final model that had a more than 98 percent F1 -score with high accuracy for the test types of the test set.

[00239] The ability of a model to enable recognition and prediction of the best suited test-type may be further improved by adding data from yet different regions. The cross validated results of the model shows that machine learning can suggestively improve the success rate and provide for automation of a now human decision making process, which can offer independence from reliance on purely individual experience or insights, along with one or more of improved risk identification, faster knowledge sharing among team and delivery of leveraging lessons and data acquired from historical data gathered from fields.

[00240] Fig. 13 shows an example of a method 1300 that can include a human in the loop (HITL) or not. As shown, an operations block 1310 can provide for performance of field operations that include acquisition of log data 1320. The log data 1320 can be utilized to derive values at depth 1332 where such values can be derived via a reservoir engineer 1330 (e.g., HITL) or via a computational system. As shown, the values at depth 1332 can be received by one or more trained ML models 1340 that can output a test type at depth 1342. The test type at depth 1342 can then be implemented by the operations block 1310, for example, to perform the test type at a particular depth.

[00241] A method such as the method 1300 of Fig. 13 can provide for determination of test type using a trained ML model or ML models. As explained, training can utilize test datasets that make the trained ML model or ML models robust with reduced uncertainty. In the example of Fig. 13, the reservoir engineer 1330 may be in the loop and, rather than having to determine what test type to execute, may merely review the output of the trained ML model(s) 1340, for example, to provide an OK and/or otherwise call for performance of the test type at depth 1342. The method 1300 can help to reduce rig-time and operation risk and improve testing quality. [00242] Fig. 14 shows an example of a GUI 1400 that include various types of regions, formations, basins, etc. As an example, an ML model or ML models may be tailored to a particular region. For example, consider selecting the Marcellus region where the GUI 1400 can provide for indications as to datasets, tools (e.g., LWD tools, etc.) and pretests. In such an example, the GUI 1400 can provide for accessing one or more trained ML models and/or for training one or more ML models that can make decisions as to what type of pretest to utilize during drilling. As an example, a trained ML model or ML models may be accessible using an application programming interface (API, not to be confused with the American Petroleum Institute). For example, consider an API call that includes various data (e.g., log data, etc.) and that, in response, returns a pretest type as determined by one or more trained ML models.

[00243] As an example, a system may include a computational framework that can utilize a Representational State Transfer (REST) API, which is of a style that defines a set of constraints to be used for creating web services. Web services that conform to the REST architectural style, termed RESTful web services, provide interoperability between computer systems on the Internet. RESTful web services can allow one or more requesting systems to access and manipulate textual representations of web resources by using a uniform and predefined set of stateless operations. One or more other kinds of web services may be utilized (e.g., such as SOAP web services) that may expose their own sets of operations.

[00244] As an example, a computational controller operatively coupled to equipment at a rigsite (e.g., a wellsite, etc.) can utilize one or more APIs to interact with a computational framework that includes an agent or agents. In such an example, one or more calls may be made where, in response, one or more actions are provided (e.g., control actions for drilling). In such an example, a call may be made with various types of data (e.g., observables, etc.) and a response can depend at least in part on such data. For example, observables may be transmitted and utilized by an agent to infer a state where an action is generated based at least in part on the inferred state and where the action can be transmitted and utilized by a controller to control activities at a rigsite.

[00245] Fig. 15 shows an example of a method 1500 and an example of a system 1590. As shown, the method 1500 includes a reception block 1510 for receiving formation log data acquired via drillstring equipment; a determination block 1520 for determining a test type using at least a portion of the formation log data and a trained machine learning model; an issuance block 1530 for issuing an instruction to the drillstring equipment to perform a test according to the test type; and an optional performance block 1540 for performing the test according to the test type (e.g., via the drillstring equipment).

[00246] The method 1500 is shown as including various computer-readable storage medium (CRM) blocks 1511, 1521, 1531 and 1541 that can include processor-executable instructions that can instruct a computing system, which can be a control system, to perform one or more of the actions described with respect to the method 1500.

[00247] In the example of Fig. 15, the system 1590 includes one or more information storage devices 1591, one or more computers 1592, one or more networks 1595 and instructions 1596. As to the one or more computers 1592, each computer may include one or more processors (e.g., or processing cores) 1593 and memory 1594 for storing the instructions 1596, for example, executable by at least one of the one or more processors 1593 (see, e.g., the blocks 1511, 1521, 1531 and 1541). As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc.

[00248] As an example, the method 1500 may be a workflow that can be implemented using one or more frameworks that may be within a framework environment. As an example, the system 1590 can include local and/or remote resources. For example, consider a browser application executing on a client device as being a local resource with respect to a user of the browser application and a cloud-based computing device as being a remote resources with respect to the user. In such an example, the user may interact with the client device via the browser application where information is transmitted to the cloud-based computing device (or devices) and where information may be received in response and rendered to a display operatively coupled to the client device (e.g., via services, APIs, etc.).

[00249] As an example, a test framework (TF) may provide for making determinations as to type of test to perform by one or more pieces of drillstring equipment.

[00250] As an example, a system may include backend and frontend sub systems. For example, consider a backend (e.g., framework engine) that can clean data, support direct queries, provide ML model training, ML model lifecycle and updating pipeline, etc. As an example, a frontend can be a web app such that the frontend can be mobile, remote, etc. (e.g., using a Python streamlit library, Python/Docker, GCP hosting, AZURE hosting, Cl/CD pipeline, a code repository access for team sharing and collaboration, etc.).

[00251] Fig. 16 shows an example of a system 1600 that can be a well construction ecosystem. As shown, the system 1600 can include one or more instances of an TF 1601 and can include a rig infrastructure 1610 and a drill plan component 1620 that can generation or otherwise transmit information associated with a plan to be executed utilizing the rig infrastructure 1610, for example, via a drilling operations layer 1640, which includes a wellsite component 1642 and an offsite component 1644. As shown, data acquired and/or generated by the drilling operations layer 1640 can be transmitted to a data archiving component 1650, which may be utilized, for example, for purposes of planning one or more operations (e.g., per the drilling plan component 1620).

[00252] In the example of Fig. 16, the TF 1601 is shown as being implemented with respect to the drill plan component 1620, the wellsite component 1642 and/or the offsite component 1644.

[00253] As an example, a method may be implemented in part using computer- readable media (CRM), for example, as a block, etc. that include information such as instructions suitable for execution by one or more processors (or processor cores) to instruct a computing device or system to perform one or more actions. As an example, a single medium may be configured with instructions to allow for, at least in part, performance of various actions of a method. As an example, a computer- readable medium (CRM) may be a computer-readable storage medium (e.g., a non- transitory medium) that is not a carrier wave.

[00254] According to an embodiment, one or more computer-readable media may include computer-executable instructions to instruct a computing system to output information for controlling a process. For example, such instructions may provide for output to sensing process, an injection process, drilling process, an extraction process, an extrusion process, a pumping process, a heating process, etc. [00255] As an example, a method can include receiving formation log data acquired via drillstring equipment; determining a test type using at least a portion of the formation log data and a trained machine learning model; and issuing an instruction to the drillstring equipment to perform a test according to the test type. In such an example, the test type can be a pressure formation test type where, for example, the drillstring equipment includes a pressure formation test probe.

[00256] As an example, a trained machine learning model can be or include a trained gradient boosted decision tree model.

[00257] As an example, issuing an instruction can be via generating a mud signal (e.g., a mud-pulse signal).

[00258] As an example, a method can include receiving formation log data via receiving generated mud signals that carry the formation log data. As an example, formation log data can include one or more of gamma ray (GR), resistivity (RES), bulk density (ROBB) and thermal neutron porosity (TNPH) data with values with respect to measured depth (MD). As an example, measured depth (MD) can be a measured log (e.g., a MD log).

[00259] As an example, a method can include training a machine learning model. In such an example, training the machine learning model can include processing formation log data and test type data with respect to depth for a plurality of offset wells to generate training data. As an example, method can include implementing at least one technique for an imbalance in training data to generated balanced training data. As an example, a technique can include SMOTE, a Tomek Links, a combination of SMOTE and Tomek Links, etc.

[00260] As an example, training can be for a gradient boosted decision tree model to generate a trained machine learning model.

[00261] As an example, a method can include determining a test type by determining the test type from a plurality of predefined test types. In such an example, the number of predefined test types can be greater than one. For example, consider two or more test types. For example, consider the tests of the table 1000 of Fig. 10. In such an example, some tests may be more performed more frequently for particular fields. For example, consider a field where types 0-B and 1 - B are more frequent. As an example, a number of predefined test types may be greater than two. As an example, drillstring equipment can include preprogrammed circuitry for performing a plurality of predefined test types. In such an example, an instruction can be issued that includes a code that indicates one of a plurality of predefined test types as the test type. [00262] As an example, a method can include performing mud-pulse telemetry between a computing system and drillstring equipment for receiving data and/or issuing an instruction.

[00263] As an example, a system can include a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive formation log data acquired via drillstring equipment; determine a test type using at least a portion of the formation log data and a trained machine learning model; and issue an instruction to the drillstring equipment to perform a test according to the test type. [00264] As an example, one or more computer-readable storage media can include computer-executable instructions executable to instruct a computing system to: receive formation log data acquired via drillstring equipment; determine a test type using at least a portion of the formation log data and a trained machine learning model; and issue an instruction to the drillstring equipment to perform a test according to the test type.

[00265] As an example, a computer program product can include executable instructions that can be executed to cause a system to operate according to one or more methods. For example, consider a computer program product that can include instructions executable to instruct a computing system to: receive formation log data acquired via drillstring equipment; determine a test type using at least a portion of the formation log data and a trained machine learning model; and issue an instruction to the drillstring equipment to perform a test according to the test type. [00266] In some embodiments, a method or methods may be executed by a computing system. Fig. 17 shows an example of a system 1700 that can include one or more computing systems 1701-1, 1701-2, 1701-3 and 1701-4, which may be operatively coupled via one or more networks 1709, which may include wired and/or wireless networks.

[00267] As an example, a system can include an individual computer system or an arrangement of distributed computer systems. In the example of Fig. 17, the computer system 1701-1 can include one or more modules 1702, which may be or include processor-executable instructions, for example, executable to perform various tasks (e.g., receiving information, requesting information, processing information, simulation, outputting information, etc.). [00268] As an example, a module may be executed independently, or in coordination with, one or more processors 1704, which is (or are) operatively coupled to one or more storage media 1706 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1704 can be operatively coupled to at least one of one or more network interface 1707. In such an example, the computer system 1701-1 can transmit and/or receive information, for example, via the one or more networks 1709 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.).

[00269] As an example, the computer system 1701-1 may receive from and/or transmit information to one or more other devices, which may be or include, for example, one or more of the computer systems 1701-2, etc. A device may be located in a physical location that differs from that of the computer system 1701-1.

As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.

[00270] As an example, a processor may be or include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[00271] As an example, the storage media 1706 may be implemented as one or more computer-readable or machine-readable storage media. As an example, storage may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.

[00272] As an example, a storage medium or storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLUERAY disks, or other types of optical storage, or other types of storage devices.

[00273] As an example, a storage medium or media may be located in a machine running machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution. [00274] As an example, various components of a system such as, for example, a computer system, may be implemented in hardware, software, or a combination of both hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.

[00275] As an example, a system may include a processing apparatus that may be or include a general purpose processors or application specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriate devices.

[00276] Fig. 18 shows components of a computing system 1800 and a networked system 1810 with a network 1820. The system 1800 includes one or more processors 1802, memory and/or storage components 1804, one or more input and/or output devices 1806 and a bus 1808. According to an embodiment, instructions may be stored in one or more computer-readable media (e.g., memory/storage components 1804). Such instructions may be read by one or more processors (e.g., the processor(s) 1802) via a communication bus (e.g., the bus 1808), which may be wired or wireless. The one or more processors may execute such instructions to implement (wholly or in part) one or more attributes (e.g., as part of a method). A user may view output from and interact with a process via an I/O device (e.g., the device 1806). According to an embodiment, a computer-readable medium may be a storage component such as a physical memory storage device, for example, a chip, a chip on a package, a memory card, etc.

[00277] According to an embodiment, components may be distributed, such as in the network system 1810. The network system 1810 includes components 1822- 1, 1822-2, 1822-3, . . . 1822-N. For example, the components 1822-1 may include the processor(s) 1802 while the component(s) 1822-3 may include memory accessible by the processor(s) 1802. Further, the component(s) 1822-2 may include an I/O device for display and optionally interaction with a method. The network may be or include the Internet, an intranet, a cellular network, a satellite network, etc. [00278] As an example, a device may be a mobile device that includes one or more network interfaces for communication of information. For example, a mobile device may include a wireless network interface (e.g., operable via IEEE 802.11,

ETSI GSM, BLUETOOTH, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery. As an example, a mobile device may be configured as a cell phone, a tablet, etc. As an example, a method may be implemented (e.g., wholly or in part) using a mobile device. As an example, a system may include one or more mobile devices.

[00279] As an example, a system may be a distributed environment, for example, a so-called “cloud” environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).

[00280] As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that can be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).

[00281] Although only a few examples have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the examples. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means- plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures.

Claims

CLAIMS What is claimed is:

1. A method comprising: receiving formation log data acquired via drillstring equipment; determining a test type using at least a portion of the formation log data and a trained machine learning model; and issuing an instruction to the drillstring equipment to perform a test according to the test type.

2. The method of claim 1 , wherein the test type is a pressure formation test type.

3. The method of claim 2, wherein the drillstring equipment comprises a pressure formation test probe.

4. The method of claim 1, wherein the trained machine learning model is a trained gradient boosted decision tree model.

5. The method of claim 1, wherein the issuing comprises generating a mud signal.

6. The method of claim 1 , wherein the receiving the formation log data comprises receiving generated mud signals that carry the formation log data.

7. The method of claim 1 , wherein the formation log data comprise gamma ray, resistivity, bulk density and thermal neutron porosity data with values with respect to measured depth.

8. The method of claim 1, further comprising training the machine learning model.

9. The method of claim 8, wherein training the machine learning model comprises processing formation log data and test type data with respect to depth for a plurality of offset wells to generate training data.

10. The method of claim 9, comprising implementing at least one technique for an imbalance in the training data to generated balanced training data.

11. The method of claim 10, wherein the at least one technique comprises SMOTE.

12. The method of claim 10, wherein the at least one technique comprises Tomek Links.

13. The method of claim 10, wherein the at least one technique comprises SMOTE and Tomek Links.

14. The method of claim 10, comprising training a gradient boosted decision tree model to generate the trained machine learning model.

15. The method of claim 1 , wherein the determining the test type comprises determining the test type from a plurality of predefined test types.

16. The method of claim 15, wherein the drillstring equipment comprises preprogrammed circuitry for performing the plurality of predefined test types.

17. The method of claim 16, wherein the instruction comprises a code that indicates one of the plurality of predefined test types as the test type.

18. The method of claim 1, further comprising performing mud-pulse telemetry between a computing system and the drillstring equipment for the receiving and the issuing.

19. A system comprising: a processor; memory accessible to the processor; processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive formation log data acquired via drillstring equipment; determine a test type using at least a portion of the formation log data and a trained machine learning model; and issue an instruction to the drillstring equipment to perform a test according to the test type.

20. One or more computer-readable storage media comprising computer-executable instructions executable to instruct a computing system to: receive formation log data acquired via drillstring equipment; determine a test type using at least a portion of the formation log data and a trained machine learning model; and issue an instruction to the drillstring equipment to perform a test according to the test type.