WO2024130108A1 - Field operations framework - Google Patents

Abstract

A method can include receiving drilling fluid input data during operations performed at a field site; predicting a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and outputting the predicted drilling fluid test result.

Description

FIELD OPERATIONS FRAMEWORK

RELATED APPLICATION

[0001] This application claims priority to and the benefit of a US Provisional Application having Serial No. 63/387,604, filed 15 December 2022, which is incorporated by reference herein in its entirety.

BACKGROUND

[0002] A reservoir can be a subsurface formation that can be characterized at least in part by its porosity and fluid permeability. As an example, a reservoir may be part of a basin such as a sedimentary basin. A basin can be a depression (e.g., caused by plate tectonic activity, subsidence, etc.) in which sediments accumulate. As an example, where hydrocarbon source rocks occur in combination with appropriate depth and duration of burial, a petroleum system may develop within a basin, which may form a reservoir that includes hydrocarbon fluids (e.g., oil, gas, etc.). Various operations may be performed in the field to access such hydrocarbon fluids and/or produce such hydrocarbon fluids. For example, consider equipment operations where equipment may be controlled to perform one or more operations. In such an example, control may be based at least in part on characteristics of rock where drilling into such rock forms a borehole that can be completed to form a well to produce from a reservoir and/or to inject fluid into a reservoir. While hydrocarbon fluid reservoirs are mentioned as an example, a reservoir that includes water and brine may be assessed, for example, for one or more purposes such as, for example, carbon storage (e.g., sequestration), water production or storage, geothermal production or storage, metallic extraction from brine, etc.

SUMMARY

[0003] A method can include receiving drilling fluid input data during operations performed at a field site; predicting a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and outputting the predicted drilling fluid test result. A system can include one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive drilling fluid input data during operations performed at a field site; predict a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and output the predicted drilling fluid test result. One or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: receive drilling fluid input data during operations performed at a field site; predict a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and output the predicted drilling fluid test result. Various other apparatuses, systems, methods, etc., are also disclosed.

[0004] 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

[0005] 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.

[0006] Fig. 1 illustrates an example system that includes various framework components associated with one or more geologic environments;

[0007] Fig. 2 illustrates an example of a system;

[0008] Fig. 3 illustrates an example of a drilling equipment and examples of borehole shapes;

[0009] Fig. 4 illustrates an example of a system;

[0010] Fig. 5 illustrates an example of a system;

[0011] Fig. 6 illustrates examples of graphical user interfaces;

[0012] Fig. 7 illustrates an example of a model inputs, an example of model results and an example of model feature ranking;

[0013] Fig. 8 illustrates an example of a gradient boosting method;

[0014] Fig. 9 illustrates example plots for predictions of drilling fluid test results;

[0015] Fig. 10 illustrates an example plot for predictions of drilling fluid test results;

[0016] Fig. 11 illustrates example plots for predictions of drilling fluid test results;

[0017] Fig. 12 illustrates an example plot for predictions of drilling fluid test results; [0018] Fig. 13 illustrates an example plot for predictions of drilling fluid test results;

[0019] Fig. 14 illustrates an example plot for predictions of drilling fluid test results;

[0020] Fig. 15 illustrates an example of a method and an example of a system; and

[0021] Fig. 16 illustrates examples of computer and network equipment.

DETAILED DESCRIPTION

[0022] 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.

[0023] 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 GU1 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.

[0024] 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. A geologic environment 150 may be outfitted with a variety of sensors, detectors, actuators, etc. In such an environment, various types of equipment such as, for example, equipment 152 may include communication circuitry to receive and to transmit information, optionally 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. One or more satellites may be provided for purposes of communications, data acquisition, etc. For example, Fig. 1 shows a satellite 170 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.).

[0025] 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 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.

[0026] In the example of Fig. 1 , the GUI 120 shows some examples of computational frameworks, including the DRILLPLAN, PETREL, TECHLOG, PETROMOD, ECLIPSE, and INTERSECT frameworks (SLB, Houston, Texas).

[0027] 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.

[0028] The DRILLOPS framework (SLB, Houston, Texas), which may be included in the system 100 of FIG. 1 , may execute a digital drilling plan and help to ensure plan adherence, while delivering goal-based automation. The DRILLOPS framework may generate activity plans automatically for individual operations, whether they are monitored and/or controlled on the rig or in town. Automation may utilize data analysis and learning systems to assist and optimize tasks, such as, for example, setting ROP to drilling a stand. A preset menu of automatable drilling tasks may be rendered, and, using data analysis and models, a plan may be executed in a manner to achieve a specified goal, where, for example, measurements may be utilized for calibration and/or one or more other purposes. The DRILLOPS framework provides flexibility to modify and replan activities dynamically, for example, based on a live appraisal of various factors (e.g., equipment, personnel, and supplies). Well construction activities (e.g., tripping, drilling, cementing, etc.) may be continually monitored and dynamically updated using feedback from operational activities. The DRILLOPS framework may provide for various levels of automation based on planning and/or re-planning (e.g., via the DRILLPLAN framework), feedback, etc.

[0029] The PETREL framework can be part of the DELFI cognitive exploration and production (E&P) environment (SLB, Houston, Texas, referred to as the DELFI environment) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir.

[0030] One or more types of frameworks may be implemented within or in a manner operatively coupled to the DELFI environment, which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence (Al) and machine learning (ML). 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. The DELFI environment can include various other frameworks, which may operate using one or more types of models (e.g., simulation models, etc.).

[0031] 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.

[0032] 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 (SLB, Houston Texas). The PIPESIM simulator may be an optimizer that can optimize one or more operational scenarios at least in part via simulation of physical phenomena.

[0033] The ECLIPSE framework provides a reservoir simulator with numerical solvers for prediction of dynamic behavior for various types of reservoirs and development schemes.

[0034] The INTERSECT framework provides a high-resolution reservoir simulator for simulation of geological features and quantification of uncertainties, for example, by creating production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework can produce 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 environment, for example, for rapid simulation of multiple concurrent cases.

[0035] 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.).

[0036] 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.

[0037] Visualization features may provide for visualization of various earth models, properties, etc., in one or more dimensions. 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. 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.).

[0038] 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. 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.). 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).

[0039] A model may be a simulated version of a geologic environment where 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. While several simulators are illustrated in the example of Fig. 1 , one or more other simulators may be utilized, additionally or alternatively.

[0040] Fig. 2 shows an example of a system 200 that can be operatively coupled to one or more databases, data streams, etc. For example, one or more pieces of field equipment, laboratory equipment, computing equipment (e.g., local and/or remote), etc., can provide and/or generate data that may be utilized in the system 200.

[0041] As shown, the system 200 can include a geological/geophysical data block 210, a surface models block 220 (e.g., for one or more structural models), a volume modules block 230, an applications block 240, a numerical processing block 250 and an operational decision block 260. As shown in the example of Fig. 2, the geological/geophysical data block 210 can include data from well tops or drill holes 212, data from seismic interpretation 214, data from outcrop interpretation and optionally data from geological knowledge. As an example, the geological/geophysical data block 210 can include data from digital images, which can include digital images of cores, cuttings, cavings, outcrops, etc. As to the surface models block 220, it may provide for creation, editing, etc. of one or more surface models based on, for example, one or more of fault surfaces 222, horizon surfaces 224 and optionally topological relationships 226. As to the volume models block 230, it may provide for creation, editing, etc. of one or more volume models based on, for example, one or more of boundary representations 232 (e.g., to form a watertight model), structured grids 234 and unstructured meshes 236. [0042] As shown in the example of Fig. 2, the system 200 may allow for implementing one or more workflows, for example, where data of the data block 210 are used to create, edit, etc. one or more surface models of the surface models block 220, which may be used to create, edit, etc. one or more volume models of the volume models block 230. As indicated in the example of Fig. 2, the surface models block 220 may provide one or more structural models, which may be input to the applications block 240. For example, such a structural model may be provided to one or more applications, optionally without performing one or more processes of the volume models block 230 (e.g., for purposes of numerical processing by the numerical processing block 250). Accordingly, the system 200 may be suitable for one or more workflows for structural modeling (e.g., optionally without performing numerical processing per the numerical processing block 250).

[0043] As to the applications block 240, it may include applications such as a well prognosis application 242, a reserve calculation application 244 and a well stability assessment application 246. As to the numerical processing block 250, it may include a process for seismic velocity modeling 251 followed by seismic processing 252, a process for facies and petrophysical property interpolation 253 followed by flow simulation 254, and a process for geomechanical simulation 255 followed by geochemical simulation 256. As indicated, as an example, a workflow may proceed from the volume models block 230 to the numerical processing block 250 and then to the applications block 240 and/or to the operational decision block 260. As another example, a workflow may proceed from the surface models block 220 to the applications block 240 and then to the operational decisions block 260 (e.g., consider an application that operates using a structural model).

[0044] In the example of Fig. 2, the operational decisions block 260 may include a seismic survey design process 261 , a well rate adjustment process 252, a well trajectory planning process 263, a well completion planning process 264 and a process for one or more prospects, for example, to decide whether to explore, develop, abandon, etc. a prospect.

[0045] Referring again to the data block 210, the well tops or drill hole data 212 may include spatial localization, and optionally surface dip, of an interface between two geological formations or of a subsurface discontinuity such as a geological fault; the seismic interpretation data 214 may include a set of points, lines or surface patches interpreted from seismic reflection data, and representing interfaces between media (e.g., geological formations in which seismic wave velocity differs) or subsurface discontinuities; the outcrop interpretation data 216 may include a set of lines or points, optionally associated with measured dip, representing boundaries between geological formations or geological faults, as interpreted on the earth surface; and the geological knowledge data 218 may include, for example knowledge of the paleo-tectonic and sedimentary evolution of a region.

[0046] As to a structural model, it may be, for example, a set of gridded or meshed surfaces representing one or more interfaces between geological formations (e.g., horizon surfaces) or mechanical discontinuities (fault surfaces) in the subsurface. As an example, a structural model may include some information about one or more topological relationships between surfaces (e.g. fault A truncates fault B, fault B intersects fault C, etc.).

[0047] As to the facies and petrophysical property interpolation 253, it may include an assessment of type of rocks and of their petrophysical properties (e.g., porosity, permeability), for example, optionally in areas not sampled by well logs or coring. As an example, such an interpolation may be constrained by interpretations from log and core data, and by prior geological knowledge.

[0048] As to the various applications of the applications block 240, the well prognosis application 242 may include predicting type and characteristics of geological formations that may be encountered by a drill bit, and location where such rocks may be encountered (e.g., before a well is drilled); the reserve calculations application 244 may include assessing total amount of hydrocarbons or ore material present in a subsurface environment (e.g., and estimates of which proportion can be recovered, given a set of economic and technical constraints); and the well stability assessment application 246 may include estimating risk that a well, already drilled or to-be-drilled, will collapse or be damaged due underground stress.

[0049] As to the operational decision block 260, the seismic survey design process 261 may include deciding where to place seismic sources and receivers to optimize the coverage and quality of the collected seismic information while minimizing cost of acquisition; the well rate adjustment process 262 may include controlling injection and production well schedules and rates (e.g., to maximize recovery and production); the well trajectory planning process 263 may include designing a well trajectory to maximize potential recovery and production while minimizing drilling risks and costs; the well trajectory planning process 264 may include selecting proper well tubing, casing and completion (e.g., to meet expected production or injection targets in specified reservoir formations); and the prospect process 265 may include decision making, in an exploration context, to continue exploring, start producing or abandon prospects (e.g., based on an integrated assessment of technical and financial risks against expected benefits).

[0050] The system 200 can include and/or can be operatively coupled to a system such as the system 100 of Fig. 1. For example, the workspace framework 110 may provide for instantiation of, rendering of, interactions with, etc., the graphical user interface (GUI) 120 to perform one or more actions as to the system 200. In such an example, access may be provided to one or more frameworks (e.g., DRILLPLAN, PETREL, TECHLOG, PIPESIM, ECLIPSE, INTERSECT, etc.). One or more frameworks may provide for geo data acquisition as in block 210, for structural modeling as in block 220, for volume modeling as in block 230, for running an application as in block 240, for numerical processing as in block 250, for operational decision making as in block 260, etc.

[0051] As an example, the system 200 may provide for monitoring data, which can include geo data per the geo data block 210. In various examples, geo data may be acquired during one or more operations. For example, consider acquiring geo data during drilling operations via downhole equipment and/or surface equipment. As an example, the operational decision block 260 can include capabilities for monitoring, analyzing, etc., such data for purposes of making one or more operational decisions, which may include controlling equipment, revising operations, revising a plan, etc. In such an example, data may be fed into the system 200 at one or more points where the quality of the data may be of particular interest. For example, data quality may be characterized by one or more metrics where data quality may provide indications as to trust, probabilities, etc., which may be germane to operational decision making and/or other decision making.

[0052] 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 preventers (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 .

[0053] 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.

[0054] 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).

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.).

[0059] 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 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 may be returned to the mud tank 301 , for example, for recirculation with processing to remove cuttings and other material.

[0060] In the example of Fig. 3, processed 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. [0061] 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. Characteristics of the mud can be utilized to determine how pulses are transmitted (e.g., pulse shape, energy loss, transmission time, etc.).

[0062] 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.

[0063] 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.).

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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. 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 MWD 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.

[0071] 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 module 356 may include equipment for generating electrical power, for example, to power various components of the drillstring 325. As an example, the MWD module 356 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.

[0072] 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.

[0073] 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 approximately 30 degrees and approximately 60 degrees or, for example, an angle to approximately 90 degrees or possibly greater than approximately 90 degrees.

[0074] 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.

[0075] As explained, a system may be a steerable system and may include equipment to perform a method such as geosteering. A steerable system can include equipment on a lower part of a drillstring which, just above a drill bit, a bent sub may be mounted. Above directional drilling equipment, a drillstring can include 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. 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.).

[0076] 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 to follow a desired route to reach a desired target or targets.

[0077] A drillstring may 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.

[0078] 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. 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. [0079] 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 approximately one hundred meters from the wellsite system 300.

[0080] 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. Circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. 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.

[0081] Fig. 4 shows an example of an environment 401 that includes a subterranean portion 403 where a rig 410 is positioned at a surface location above a bore 420. In the example of Fig. 4, various wirelines services equipment can be operated to perform one or more wirelines services including, for example, acquisition of data from one or more positions within the bore 420.

[0082] As an example, a wireline tool and/or a wireline service may provide for acquisition of data, analysis of data, data-based determinations, data-based decision making, etc. Some examples of wireline data can include gamma ray (GR), spontaneous potential (SP), caliper (CALI), shallow resistivity (LLS and ILD), deep resistivity (LLD and ILD), density (RHOB), neutron porosity (BPHI or TNPH or NPHI), sonic (DT), photoelectric (PEF), permittivity and conductivity.

[0083] In the example of Fig. 4, the bore 420 includes drillpipe 422, a casing shoe 424, a cable side entry sub (CSES) 423, a wet-connector adaptor 426 and an openhole section 428. As an example, the bore 420 can be a vertical bore or a deviated bore where one or more portions of the bore may be vertical and one or more portions of the bore may be deviated, including substantially horizontal.

[0084] In the example of Fig. 4, the CSES 423 includes a cable clamp 425, a packoff seal assembly 427 and a check valve 429. These components can provide for insertion of a logging cable 430 that includes a portion 432 that runs outside the drillpipe 422 to be inserted into the drillpipe 422 such that at least a portion 434 of the logging cable runs inside the drillpipe 422. In the example of Fig. 4, the logging cable 430 runs past the casing shoe 424 and the wet-connect adaptor 426 and into the openhole section 428 to a logging string 440.

[0085] As shown in the example of Fig. 4, a logging truck 450 (e.g., a wirelines services vehicle) can deploy the wireline 430 under control of a system 460. As shown in the example of Fig. 4, the system 460 can include one or more processors 462, memory 464 operatively coupled to at least one of the one or more processors 462, instructions 466 that can be, for example, stored in the memory 464, and one or more interfaces 468. As an example, the system 460 can include one or more processor- readable media that include processor-executable instructions executable by at least one of the one or more processors 462 to cause the system 460 to control one or more aspects of equipment of the logging string 440 and/or the logging truck 450. In such an example, the memory 464 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.

[0086] Fig. 4 also shows a battery 470 that may be operatively coupled to the system 460, for example, to power the system 460. As an example, the battery 470 may be a back-up battery that operates when another power supply is unavailable for powering the system 460 (e.g., via a generator of the wirelines truck 450, a separate generator, a power line, etc.). As an example, the battery 470 may be operatively coupled to a network, which may be a cloud network. As an example, the battery 470 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.

[0087] As an example, the system 460 can be operatively coupled to a client layer 480. In the example of Fig. 4, the client layer 480 can include features that allow for access and interactions via one or more private networks 482, one or more mobile platforms and/or mobile networks 484 and via the “cloud” 486, which may be considered to include distributed equipment that forms a network such as a network of networks. As an example, the system 460 can include circuitry to establish a plurality of connections (e.g., sessions). As an example, connections may be via one or more types of networks. As an example, connections may be client-server types of connections where the system 460 operates as a server in a client-server architecture. For example, clients may log-in to the system 460 where multiple clients may be handled, optionally simultaneously.

[0088] While the example of Fig. 4 shows the system 460 as being associated with the logging truck 450, one or more features of the system 460 may be included in a downhole assembly, which may be a wireline assembly and/or a LWD assembly. In such an approach, various computations may be performed downhole where results thereof may be optionally transmitted to surface (e.g., to the logging truck 450, etc.) using one or more telemetric technologies and/or techniques (e.g., mud-pulse telemetry, wireline, etc.).

[0089] Fig. 5 shows an example of a drilling fluid system 500 that may aim to provide for various operations, which may include one or more of removing cuttings from a well, controlling formation pressures, suspending and releasing cutting, sealing permeable formations, maintaining wellbore stability, minimizing formation damage, cooling, lubricating and supporting a bit and drilling assembly, transmitting hydraulic energy to one or more downhole tools and/or a bit, ensuring adequate formation evaluation, controlling corrosion, facilitating cementing and completion, preventing gas hydrate formation, and minimizing impact on the environment.

[0090] As shown in the example of Fig. 5, the system 500 can include a return line and a discharge line (see also, e.g., the lines, pipes, hoses, etc., 306, 308, 309, 310, and 328 of Fig. 3). In the example of Fig. 5, the system 500 may include a shaker, a desander, a desilter, and a degasser associated with various mud pits (e.g., mud tanks) that can receive drilling fluid via the return line and output processed drilling fluid to an active pit that may be in fluid communication with a suction pit and a reserve pit where the suction pit may be in fluid communication with a pump that can pump drilling fluid to the discharge line. As an example, one or more mixing units may be included, for example, for addition of one or more materials to the drilling fluid before it is pumped to the discharge line.

[0091] As an example, the system 500 may be utilized for one or more types of operations, which may include drilling, wireline, completions, blow out control, etc. As to completions, as an example, a cementing operation may include pumping and/or receiving of drilling fluid where cement may be positioned between casing and a borehole wall. [0092] Fig. 6 shows an example of a graphical user interface 600 that includes various types of information for construction of a well where times are rendered for corresponding actions. In the example of Fig. 6, 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. 6, 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 600 may be rendered and revised accordingly to reflect changes. As shown in the example of Fig. 6, the GUI 600 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 600.

[0093] As to the highlighted element 610 (“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 610 is the longest in terms of estimated time. Fig. 6 also shows a GUI 620 for a borehole trajectory and a GUI 630 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). In the example of Fig. 6, the GUI 630 and parameters thereof may be associated with drill bit performance (e.g., ROP, wear, remaining life, etc.).

[0094] As an example, the GUI 630 may be operatively coupled to an equipment framework (EF) such that, for example, variations in RPM and/or WOB can be visualized with respect to drill bit performance, which may provide for optimizations, control, etc. As an example, an ROP may be associated with wear where an optimal ROP may be an ROP that considers wear (e.g., in relationship to a depth to be drilled, etc.).

[0095] As an example, the GUI 600 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.). 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.).

[0096] 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 field equipment operations framework, such as, for example, the DRILLOPS framework, can be optionally instantiated for foreground and/or background execution that can assess information of the DRILLPLAN framework with respect to equipment choices, drilling fluid system choices, etc. In such an example, the field equipment operations framework may act in response by making suggestions and/or changes.

[0097] While various operations with respect to drilling are mentioned in the example of Fig. 6, one or more other types of field operations may be organized in a manner with respect to one or more of depth and time. For example, consider logging operations that may utilize one or more wireline tools (see, e.g., Fig. 4).

[0098] Various aspects of various types of field operations can be recorded in the form of reports, which may be, for example, single operation reports, daily reports, weekly reports, monthly reports, etc., where frequency may depend on type of field operation. For example, one or more of the operations described with respect to Fig. 1 , Fig. 2, Fig. 3, Fig. 4, Fig. 5, and Fig. 6 may be accompanied by remarks that can be recorded, for example, on paper, digitally using a framework, etc. Remarks can include valuable information that may be utilized in one or more manners. For example, consider utilization of information included in remarks in combination with one or more of the frameworks of the system 100 of Fig. 1 , the system 200 of Fig. 2, the wellsite system 300 of Fig. 3, the system 460 of Fig. 4, the system 500 of Fig. 5, the GUI 600 of Fig. 6, etc. As an example, a rig control system (RCS) may record various types of data during and/or after various field operations.

[0099] In drilling operations, multiple types of data are collected. Many data streams tend to be in the form of numerical measurements that span a wide range of sampling frequencies, from Hz (e.g., equipment sensors, logging tools) to once in several hours (e.g., drilling fluid property measurements). These data can be recorded in a structured time series or depth-based format, which can be amenable for further analytics. Various types of data can be germane to drilling fluid, for example, its properties, performance, flowrate, ability to clean of cuttings, ability to remediate and/or dispose of, etc.

[00100] Designing and managing drilling fluid (mud) can be field operations used in well construction. Drilling fluids can function to provide sufficient hydrostatic pressure to prevent formation fluid from entering into a wellbore, function to cool a drill bit, function to clean a drill bit during drilling, function to carry out drill cuttings from a wellbore, and function to suspend drilling while drilling is paused. Before drilling commences, one or more drilling fluid engineers can determine type, volume and composition of drilling fluid. During the drilling, one or more engineers may monitor various aspects of drilling fluid (e.g., flowrate, pressure, etc.), at a wellsite to ensure a drilling operation performs as expected.

[00101] As explained, a drilling fluid can be an integral part of a drilling operation and has a number of functions, such as controlling formation pressure, removing cuttings from the wellbore, transmitting hydraulic energy to downhole tools and the bit, and maintaining wellbore stability.

[00102] A drilling fluid, which may be referred to as mud or drilling mud, can include one or more drilling fluid classes such as, for example, water-based muds (WBM) and non-aqueous fluids (NAF) where, within the latter group, oil-based muds (OBM) and synthetic-based muds (SBM) may be distinguished.

[00103] Given the number of functions performed by drilling fluid, various minimum properties of the fluid tend to be desirable to maintain. As an example, measurement of these properties can be a basis for a mud engineer report of the fluid, for example, to record how it is reacting with the formation and the subsurface environment. Examples of properties can include, for example, density, viscosity, fluid loss control, and chemical composition. [00104] Drilling fluid tends to evolve during drilling operations, with factors such as formation dispersed solids, chemical treatments, and dilutions affecting fluid composition and, consequently, fluid properties. To ensure proper functioning of such a dynamic fluid system, which may include solids, the properties of drilling fluid (e.g., and/or solids) can be measured, for example, at time intervals.

[00105] As an example, a variety of drilling fluid properties can be monitored, depending on the type of the fluid (e.g. , aqueous or non-aqueous). Some of properties that are common to both aqueous and non-aqueous drilling fluids are drilling fluid density, retort measurements of fluid composition, measurements of viscosity and gel strength, and fluid loss and filtercake thickness as described in respective API practices (API Recommended Practice 13B-1 and 13B-2). Measurements may be performed manually by a mud engineer on location. Some of the measurements, such as viscosity, may take a few minutes, whereas others, such as retort fluid composition, gel strength, and fluid loss can take hours of waiting and hands-on time. A full API fluid check may be performed, for example, twice a day for land and, for example, four times a day for offshore operations.

[00106] With the increasing availability of automated and inline measurements for drilling fluids, more frequent data collection becomes a possibility due to elimination of manual effort required for the measurement. Automation provides an opportunity for a more detailed monitoring of drilling fluid properties and optimizing the drilling operation. However, measurements of certain fluid properties, such as fluid loss and gel strength, can demand a fluid conditioning period of up to 30 minutes as per API Recommended Practice, which can limit the frequency at which a full API mud check can be performed, even with the inline automated equipment. This can be particularly burdensome for offshore deepwater drilling operations, where viscosity and gel strength data are to be collected at several temperatures for each fluid check, yet the demand for frequent fluid property measurements remains for control of operations related to drilling, pressure maintenance, etc.

[00107] Data on drilling fluid properties have been collected by M-l SWACO for years and are stored in the One-Trax Central (OTC) database. In addition to fluid properties, the database contains fluid and well metadata, such as the specific fluid system, the date and the time of the measurement, and the location of the well. As of late 2022, the OTC database includes data on over 36,000 wells and close to 1 ,000,000 operation days. These historical data can be a source for real-time fluid property prediction based on fluid characteristics, for example, using one or more machine learning (ML) techniques. For example, a ML technique can discern one or more patterns in data. A trained ML model may be utilized for one or more purposes such as, for example, prediction and/or classification.

[00108] As an example, a computational framework can provide for machine learning for fluid property prediction. For example, consider a supervised ML workflow where data are split (e.g., randomly, etc.) into a training set and a test set and a selected model is trained on the training set. Model accuracy may be assessed by computing model predictions on the test set where a known outcome is compared to predicted outcome for test data (e.g., predicted data compared to labeled test data). If model accuracy is satisfactory, the model may be deployed for outcome predictions on new, “unseen” data. While training a model may take substantial time, running model predictions can be performed in real time, depending on model complexity, memory demands, etc. One or more of a variety of ML models may be accessed using libraries in programming languages such as, for example, PYTHON or R.

[00109] As an example, one or more of the following aspects, labeled 1 , 2 and 3, may be considered when creating an ML model for fluid properties prediction.

[00110] 1. The nature of the drilling fluid beyond aqueous or non-aqueous fluid.

For example, M-l SWACO has 49 distinct aqueous and 47 distinct non-aqueous drilling fluid systems in the last 7 years in its drilling fluids portfolio. This is tracked via the fluid system name associated with the fluid property measurements in OTC database. Each fluid system includes a distinct set of additives and different fluid systems may exhibit different property patterns.

[00111] 2. For a given fluid system, practices and operating conditions may differ substantially between locations. In particular, the nature of low-gravity solids may substantially affect properties of the fluids and required treatments.

[00112] 3. Drilling practices and operating conditions can evolve systematically over time. Thus, even though several years’ worth of data may be available, recent data may tend to be more relevant for predicting fluid properties.

[00113] Given such considerations, a brute force approach of randomly splitting available fluid property data into a training set and a test set can be impractical for creating/training one or more ML models for drilling fluid properties. As an example, a more nuanced approach can be applied, where, for example, a fluid system, a location, and a timeline of data can be considered when building an ML model. [00114] In an example trial, an example ML model proved effective at making predictions. In this example, the ML model suitably predicted 30-min gel (API) based on other fluid properties. Following the above considerations, a single fluid system (Megadril) in a single location (Argentina - Neuquen) was considered. Fluid property data collected in 2021 were used as the training set, and fluid property data collected in 2022 were used as the test set. In this example trial, the XGBoost model was implemented as available in the PYTHON XGBoost library. The XGBoost model demonstrated suitable characteristics for property modeling.

[00115] Fig. 7 shows examples of model inputs 710, a plot of model predictions on a test set 720, and a plot of model feature importance 730. As shown in the example of Fig. 7, a combination of fluid properties was used to predict 30-min gel where predicted versus measured 30m in gel values for the test set proved acceptable (see, e.g., R² = 0.87). As indicated, for this example, fluid weight had the highest feature importance of the variables used in the model. As shown, model inputs may include one or more of fluid weight, R600 (rheology), high-gravity solids percentage, R300 (rheology), low-gravity solids percentage, R200 (rheology), oil-water ratio, R100 (rheology), electrical stability, 10 second gel, and 10 minute gel; noting that one or more other inputs may be utilized, additionally or alternatively.

[00116] As explained, various fluid related tests, which may be standardized tests (e.g., American Petroleum Institute (API), etc.), can be time consuming, complex to perform, difficult to perform, pose risks to perform, etc. In the example, the 30-min gel test is predicted adequately, which can conserve a considerable amount of time. As shown, an input can be a result of the 10-min gel test, however, as an example, a model may be generated that can predict 10-min gel test results and/or other gel test results that may be, for example, 10 minutes or more.

[00117] As demonstrated in the example of Fig. 7, the trained ML model can accurately predict 30-min gel results based on historical data. Relative mean square error for the prediction is 1.4 Pa, which is within the uncertainty range of the manual 30-min gel measurement.

[00118] As another example, consider temperature tests where a sample is to be heated to different temperatures. In various deep water drilling scenarios, a protocol may call for heating or otherwise adjusting temperature of a sample. For example, consider a protocol that calls for sample temperatures of 100 degF, 150 degF and 200 degF. Such a protocol can demand careful attention to heating or otherwise adjusting to the exact temperatures (e.g., within some small range such as plus or minus a few degrees). For example, heating too fast to conserve time may result in overshoot and/or may alter a fluid as in various instances rate of energy input to heat or energy reduction to cool may impact a sample (e.g., locally, structurally, etc.). As an example, one or more ML models can be built to predict measurement values at higher temperatures to thereby conserve time. For example, consider an approach that may utilize 100 degF and/or 150 degF results as input to predict a result at 200 degF.

[00119] The results shown in Fig. 7 demonstrate fluid property prediction based on historical data. As an example, prediction can be further improved, for example, by exploring one or more additional and/or alternative model input variables, model hyperparameter optimization, exploring one or more other ML approaches, which may include neural networks, etc., and/or introducing more sophisticated features such as date-based weighting of training data inputs to give more importance to recent samples.

[00120] In a production scenario, a model may be retrained on a regular or other basis to help ensure that most recent data are incorporated in a training set. For example, a method can include dynamic training and/or testing.

[00121] As explained, a particular gradient boosting technique was employed in the example of Fig. 7, known as extreme gradient boosting. Gradient boosting is a machine learning technique used in regression and classification tasks, among others. Gradient boosting can provide a prediction model in the form of an ensemble of weak prediction models, which may be in the form of decision trees.

[00122] In gradient boosting, various hyperparameters can be involved. For example, consider one or more of a number of trees or estimators in a model, a learning rate of a model, a row and column sampling rate for stochastic models, a maximum tree depth, a minimum tree weight, regularization terms alpha and lambda, etc. In various instances, one or more hyperparameters may be tuned. For example, consider a process where the learning rate (lambda) is set to as small as possible value followed by tuning the number of trees (iterations or T) using cross validation. Various platforms, libraries, etc., provide default values for hyperparameters. For example, consider the following SCIKIT-LEARN platform hyperparameter values: learning_rate=0.1 (shrinkage). n_estimators=100 (number of trees). max_depth=3. min_samples_split=2. min_samples_leaf=1 . subsample=1 .0.

[00123] Gradient boosting can involve a loss function to be optimized, a weak learner to make predictions, and an additive model to add weak learners to minimize the loss function. The loss function used can depend on the type of problem being solved and can be differentiable. For example, regression may use a squared error and classification may use logarithmic loss. As to a weak learner, decision trees may be used as the weak learner in gradient boosting. For example, regression trees can be used that output real values for splits and whose output can be added together, allowing subsequent models outputs to be added and adjust the residuals in the predictions. As an example, trees may be constructed in a greedy manner, choosing the best split points based on purity scores like Gini or to minimize the loss. As an example, weak learners can be constrained in specific ways, such as a maximum number of layers, nodes, splits or leaf nodes (e.g., to help ensure that the learners remain weak, but can still be constructed in a greedy manner). As to an additive model, trees may be added one at a time, where existing trees in the model are not changed. As an example, a gradient descent procedure can be used to minimize the loss when adding trees. After calculating the loss, to perform the gradient descent procedure, a process can add a tree to the model that reduces the loss (e.g., follow the gradient). Such a process can involve parameterizing the tree, then modify the parameters of the tree and move in the right direction by reducing the residual loss. The output for the new tree is then added to the output of the existing sequence of trees in an effort to adjust or improve the final output of the model. A fixed number of trees can be added or training stopped once loss reaches an acceptable level or no longer improves on an external validation dataset.

[00124] As an example, cross-validation can be utilized as a statistical technique to estimate skill of one or more ML models. Cross-validation can be used in applied machine learning, for example, to compare and select a model for a given predictive modeling problem because it is easy to understand, easy to implement, and results in skill estimates that generally have a lower bias than other methods. As an example, a k-fold cross-validation procedure can be implemented for estimating skill of one or more ML models. [00125] More specifically, cross-validation is a resampling procedure that can be used to evaluate one or more ML models on a limited data sample. As to k-fold cross- validation, the parameter k that refers to the number of groups that a given data sample is to be split into (e.g., k-fold). When a specific value for k is chosen, it may be used in place of k in a reference to the model, such as k = 10 becoming 10-fold cross- validation.

[00126] As explained, cross-validation may be used in applied machine learning to estimate the skill of a ML model on unseen data. That is, to use a limited sample in order to estimate how the ML model is expected to perform in general when used to make predictions on data not used during the training of the ML model.

[00127] A k-fold approach can result in a less biased or less optimistic estimate of ML model skill than techniques such as a simple train/test split.

[00128] A k-fold approach can include shuffling a dataset randomly, splitting the dataset into k groups where, for each group: (a) take the group as a hold out or test data set; (b) take the remaining groups as a training data set; (c) fit a model on the training set and evaluate it on the test set; and (d) retain the evaluation score and discard the model. In such an example, the k-fold approach can summarize the skill of the model using the sample of model evaluation scores. In such an example, each observation in the data sample can be assigned to an individual group and can stay in that group for the duration of the procedure. This means that each sample is given the opportunity to be used in the hold out set 1 time and used to train the model k-1 times. Accordingly, a k-fold approach involves randomly dividing a set of observations into k groups, or folds, of approximately equal size, where the first fold can be treated as a validation set and where fitting is performed on the remaining k - 1 folds.

[00129] As an example, a method can include using a gradient boosting model for classification along with a k-fold cross-validation approach. For example, a method that includes evaluating a gradient boosting classifier on a test problem using repeated k-fold cross-validation and reporting mean accuracy. In such an example, a single model may be fit on available data and a single prediction made.

[00130] Below are examples of code for the SCIKIT-LEARN platform for gradient boosting classification and k-fold and gradient boosting regression and k-fold:

# gradient boosting for classification in scikit-learn from numpy import mean, from numpy import std from sklearn. datasets import make_classification from sklearn. ensemble import GradientBoostingClassifier from sklearn. model_selection import cross_val_score from sklearn. model_selection import RepeatedStratifiedKFold from matplotlib import pyplot

# define dataset

X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, random_state=1 )

# evaluate the model model = GradientBoostingClassifier() cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1 ) n_scores = cross_val_score(model, X, y, scoring- accuracy', cv=cv, n_jobs=-1 , error_score- raise') print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))

# fit the model on the whole dataset model = GradientBoostingClassifier() model. fit(X, y)

# make a single prediction row = [[2.56999479, -0.13019997, 3.16075093, -4.35936352, -1.61271951 , - 1.39352057, -2.48924933, -1.93094078, 3.26130366, 2.05692145]] yhat = model, predict(row) print('Prediction: %d' % yhat[O])

# gradient boosting for regression in scikit-learn from numpy import mean, from numpy import std from sklearn. datasets import make_regression from sklearn. ensemble import GradientBoostingRegressor from sklearn. model_selection import cross_val_score from sklearn. model_selection import RepeatedKFold from matplotlib import pyplot

# define dataset

X, y = make_regression(n_samples=1000, n_features=10, n_informative=5, random_state=1 )

# evaluate the model model = GradientBoostingRegressor() cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1 ) n_scores = cross_val_score(model, X, y, scoring- neg_mean_absolute_error', cv=cv, n_jobs=-1 , error_score- raise') printfMAE: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))

# fit the model on the whole dataset model = GradientBoostingRegressor() model. fit(X, y)

# make a single prediction row = [[2.02220122, 0.31563495, 0.82797464, -0.30620401 , 0.16003707, - 1.44411381 , 0.87616892, -0.50446586, 0.23009474, 0.76201118]] yhat = model, predict(row) print('Prediction: %.3f' % yhat[O])

[00131] Fig. 8 shows an example of an XGBoost method 800. In the example of Fig. 8, the XGBoost can operate akin to a Newton-Raphson technique in a function space (e.g., gradient boosting may operate as a gradient descent in function space) where a second order Taylor approximation can be used in a loss function to make a connection to the Newton Raphson technique. As shown, the method 800 can include an input block 810 for inputting a training set, a loss function (e.g., differentiable), a number of weak learners (M), and a learning rate; an initialization block 820 for initializing a model with a constant value; a computation block 830 for computing gradients and Hessians for the number of weak learners; a fit block 834 that can fit a base learner (e.g., or weak learner, which may be a tree) using the training set (e.g., for m = 1 to M); an update block 838 for updating the model (e.g., for m = 1 to M); and an output block 840 for outputting a result of the XGBoost method 800.

[00132] As an example, CPU-powered machine learning tasks with XGBoost may take hours to run, where a goal is to create accurate, prediction results involving the creation of thousands of decision trees and the testing of large numbers of parameter combinations. As an example, a method can include utilizing graphics processing units (GPUs), which can provide for massively parallel architecture that may include more than 100 small efficient cores that can launch many parallel threads simultaneously to expedite compute-intensive tasks.

[00133] As an example, a framework may utilize one or more features of the CATBOOST toolkit. The CATBOOST toolkit provides features for machine learning using gradient boosting on decision trees and is available as an open-source library. The CATBOOST toolkit provides features for implementation of ordered boosting (e.g., a permutation-driven alternative to a classic boosting technique), and techniques for processing categorical features. As inherent in the name, the CATBOOST toolkit provides for handling of categorical features. Such a toolkit may expedite handling of drilling fluid predictions using ML as various characteristics of drilling fluid and/or associated operations may be presented readily as being categorical rather than inherently numerical. As an example, a toolkit or other technique may provide for transforming one or more categories into numerical form, which may be in a coded- manner that may be inherent to a toolkit.

[00134] The CATBOOST toolkit provides for native handling for categorical features, fast GPU training, visualizations and tools for model and feature analysis, use of oblivious trees or symmetric trees for faster execution, and ordered boosting to overcome overfitting.

[00135] As to symmetric trees, in each iteration, leaves from a previous tree are split using the same condition where the feature-split pair that accounts for the lowest loss is selected and used for a level’s nodes. Such a balanced tree architecture can aid in efficient CPU/GPU implementation, decrease prediction time, make swift model appliers, and control overfitting as the structure serves as regularization. As to ordered boosting, classic boosting techniques may be prone to overfitting on small and/or noisy datasets due to a problem known as prediction shift. When calculating the gradient estimate of a data instance, such techniques may use the same data instances that the model was built with, thus having no chances of experiencing unseen data. In ordered boosting, a permutation-driven approach to training of a model on a subset of data may be utilized while calculating residuals on another subset, thus preventing target leakage and overfitting. As mentioned, the CATBOOST toolkit supports native categorical features while also supporting other types of features natively (e.g., numeric and/or text).

[00136] As explained, historical data can be a source of training data and/or testing data. Such data may come from reports, where a report can include various remarks as to conditions, activities, etc. For example, one or more engineers can record remarks to generate a mud report on a daily basis. In addition to daily comments, such a report can include one or more types of metadata, which may be connected to one or more remarks. For example, consider index data, well location data (e.g., country, latitude/longitude), well name (e.g., ID), date, fluid system data, present activity data (e.g., event that happened during the working day), and drilling depth data (e.g., depth of drilling at the end of the working day).

[00137] As an example, knowledge of events may be useful in planning and/or controlling one or more drilling fluid operations. As an example, a framework may provide for event detection. As to some types of events, consider lost circulation, water influx, H2S, and stuck pipe as types of unscheduled events. As an example, a framework may be operable to detect such types of events. As an example, one or more ML models may provide for prediction of fluid test results and/or one or more other types of results, which may be, for example, event-related.

[00138] As an example, a framework may provide for determinations as to input and determinations as to features, which may be engineered features as determined via feature engineering as part of one or more machine learning techniques. As an example, a framework for field operations that can include drilling fluid related field operations may take a substantially chemical composition-free approach to drilling fluid. For example, a framework may utilize various types of data that may be other than chemical composition data. In such an example, the framework may operate without having knowledge of specific chemical components in one or more drilling fluids while, for example, optionally having knowledge of one or more bulk types of properties. For example, consider a bulk property such as solids, whether high-gravity solids and/or low-gravity solids. As another example, for a WBM, consider pH, bentonite, alkalinity, etc., as some types of properties that do not include specifics as to particular chemicals (e.g., surfactants, polymers, etc.). As to a NAF, consider, for example, properties such as oil/water ratio, chlorides, lime, etc., as some types of properties that do not include specifics as to particular chemicals (e.g., surfactants, polymers, etc.). As an example, drilling fluid may be characterized on various types of data that may be readily measured in the field. For example, pH may be measured in the field using a portable or fixed pH meter. In contrast, chemical compositions may demand sophisticated and time-consuming laboratory tests that may not be amenable for use in the field. Further, for drilling fluid in general, knowledge of chemical composition at one point in time does not necessarily match chemical composition at another point in time. For example, chemical composition may change substantially due to contact with reservoir fluid, due to contact with rock, due to mixing of drilling fluid from a reserve pit, etc. Hence, an approach that relies on chemical composition may be impractical, particularly where resource demands for determining chemical composition make it impractical to provide chemical composition in a timely and/or otherwise meaningful manner.

[00139] As an example, a framework may include use of drilling fluid data (e.g., type and name), drilling fluid properties (e.g., density, funnel viscosity, shear rheology, gel strength), drilling fluid composition (e.g., solids, pH, bentonite, alkalinity, etc., for WBM and oil/water ratio, chlorides, lime, etc., for NAF), and operational data (e.g., location, operator, depths, inclination, bottom-hole circulation temperature (BHCT)). Such a framework may provide for generation of engineered features. For example, consider 10-min gel test results, 30-min gel test results, and previous shear rheology and gel strength. As an example, one or more other features may be included such as, for example, multiple temperature rheology (e.g., previous shear rheology at more than one temperature).

[00140] As an example, a framework may provide for generation of results for one or more gel tests, plastic viscosity (PV), yield point (YP) and/or low shear YP (LSYP). As an example, one or more parameters may provide for characterization of drilling fluid. For example, an optimum drilling fluid may be characterized using one or more of PV, YP and LSYP. In such an example, an optimum rheology of a drilling fluid may be characterized by a low PV, an acceptable YP and an acceptable LSYP. As to PV, lower may be better, while an acceptable YP may be in a range between approximately 15 and 25 lb/100ft² and an acceptable LSYP may be in a range from approximately 1 to 1 .5 times a hole diameter.

[00141] Fig. 9 shows example plots 900 for 10-min gel strength and 30-min gel strength where the plots 900 indicate prediction versus actual along with residuals. In the example of Fig. 9, the predictions were generated with aid of the CATBOOST toolkit. As shown, the test size for the 10-min gel strength is 97,000 and the test size for the 30-min gel strength is 51 ,000 where R² values are 0.94 and 0.97, respectively, and RMSE values are 2.0 and 1.6, respectively. The plots 900 demonstrate that an ML-based approach can effectively predict results of one or more gel tests in an expeditious manner, which can be in a time less than the time required to run an actual gel test. In such an approach, actual gel tests may be run less frequently and/or predicted results may be run as frequently as desired, which may provide for improved field operations. [00142] Fig. 10 shows an example plot 1000 for gel strength versus time. As shown, predictions can include confidence information (e.g., uncertainty information), which may be part of output of one or more ML models. In the example of Fig. 10, the predictions were generated with aid of the CATBOOST toolkit. In the example plot 1000, the predicted values may be utilized in combination with confidence intervals (e.g., 95% confidence intervals (Cis), etc.) and in combination with actual values, which, as explained, may be acquired on a less frequent basis than the predicted values. As explained, a prior or previous value that is an actual value may be an input to a ML model (e.g., as determined during feature engineering, etc.); noting that more than one prior or previous value may be utilized as input.

[00143] In the plot 1000, one or more differences may be utilized, for example, to issue one or more triggers. In the plot 1000, for the 10-min gel strength, at approximately 60 days, a deviation occurs between actual and predicted values where the actual values may be outside of a confidence interval for the predicted values. In such an example, a trigger may issue a signal, which may be a notification signal and/or a control signal. As an example, a trigger may call for performing an actual drilling fluid test, which may be at a time that differs from a scheduled actual drilling fluid test. In such an example, a prediction may be assessed that may be based at least in part on result of the actual drilling fluid test. As an example, a deviation may occur in response to an event such as, for example, an influx of formation fluid into drilling fluid or, for example, responsive to one or more field operations (e.g., mixing, changing tanks, etc.). As to various field operations, as explained, one or more types of field operational data may be taken into account by a framework (e.g., as input to an ML model, etc.). Hence, a framework may be able to account for one or more types of field operations that may otherwise, if not accounted for, impact predictions.

[00144] Fig. 11 shows example plots 1100 for PV, YP and LSYP, which include plots of predicted versus actual and plots of residuals. In the example of Fig. 11 , the predictions were generated with aid of the CATBOOST toolkit. As shown, PV may be predicted using an ML-based approach with an R² of 0.96 and an RMSE of 5.6, YP may be predicted using an ML-based approach with an R² of 0.61 and an RMSE of 2.3, and LSYP may be predicted using an ML-based approach with an R² of 0.66 and an RMSE of 1 .4. Such predictions may be utilized in the field for one or more purposes (e.g., notifications, control, etc.). [00145] Fig. 12 shows an example plot 1200 of PV actual and predicted values versus time for multiple temperatures, including 40 degF, 100 degF, and 150 degF, along with confidence intervals (e.g., 95% Cl). In the example of Fig. 12, the predictions were generated with aid of the CATBOOST toolkit.

[00146] Fig. 13 shows an example plot 1300 of YP actual and predicted values versus time for multiple temperatures, including 40 degF, 100 degF, and 150 degF, along with confidence intervals (e.g., 95% Cl). In the example of Fig. 13, the predictions were generated with aid of the CATBOOST toolkit.

[00147] Fig. 14 shows an example plot 1300 of LSYP actual and predicted values versus time for multiple temperatures, including 40 degF, 100 degF, and 150 degF, along with confidence intervals (e.g., 95% Cl). In the example of Fig. 14, the predictions were generated with aid of the CATBOOST toolkit.

[00148] Fig. 15 shows an example of a method 1500 and an example of a system 1590. As shown, the method 1500 can include a reception block 1510 for receiving drilling fluid input data during operations performed at a field site; a prediction block 1520 for predicting a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and an output block 1530 for outputting the predicted drilling fluid test result.

[00149] The method 1500 is shown in Fig. 15 in association with various computer-readable media (CRM) blocks 1511 , 1521 and 1531. Such blocks generally include 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. While various blocks are shown, a single medium may be configured with instructions to allow for, at least in part, performance of various actions of the method 1500. As an example, a computer-readable medium (CRM) may be a computer-readable storage medium that is non-transitory and that is not a carrier wave. As an example, one or more of the blocks 1511 , 1521 and 1531 may be in the form processor-executable instructions.

[00150] 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 and 1531 ). 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. [00151] As to types of machine learning models that may be implemented for one or more purposes, which may include event detection, 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.

[00152] 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 shortterm 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.

[00153] 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. Al 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).

[00154] 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.

[00155] 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. [00156] 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".

[00157] As an example, a device may utilize TENSORFLOW LITE (TFL) or another type of lightweight framework. TFL is a set of tools that enables on-device machine learning where models may run on mobile, embedded, and loT devices. TFL is optimized for on-device machine learning, by addressing latency (no round-trip to a server), privacy (no personal data leaves the device), connectivity (Internet connectivity is demanded), size (reduced model and binary size) and power consumption (e.g., efficient inference and a lack of network connections). Multiple platform support, covering ANDROID and iOS devices, embedded LINUX, and microcontrollers. Diverse language support, which includes JAVA, SWIFT, Objective- C, C++, and PYTHON. High performance, with hardware acceleration and model optimization. Machine learning tasks may include, for example, image classification, object detection, pose estimation, question answering, text classification, etc., on multiple platforms.

[00158] A method can include receiving drilling fluid input data during operations performed at a field site; predicting a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and outputting the predicted drilling fluid test result.

[00159] As an example, predicting a drilling fluid test result can occur (e.g., be generated) for a drilling fluid at a field site without performing a corresponding drilling fluid test. In such an example, the corresponding drilling fluid test may include performance time in excess of 5 minutes, in excess of 10 minutes, in excess of 15 minutes, etc. As an example, a corresponding drilling fluid test can be a 10 minute gel test, a 30 minute gel test, etc.

[00160] As an example, a corresponding drilling fluid test can call for heating a drilling fluid at a field site to a specified temperature. For example, consider heating to at least 100 degrees F, 150 degrees F, 200 degrees F, etc.

[00161] As an example, a trained machine learning model can include decision trees. For example, consider gradient boosted decision trees. As an example, a trained machine learning model may include one or more neural networks. [00162] As an example, a trained machine learning model can be trained using historical data from drilling fluid reports in a drilling fluid reports database.

[00163] As an example, a method can include generating a trained machine learning model. In such an example, the method may include implementing gradient boosting. For example, consider implementing extreme gradient boosting.

[00164] As an example, predicting a drilling fluid test result can be for a drilling fluid at a field site and in a method that can include altering the drilling fluid at the site responsive to the predicting.

[00165] As an example, a method can include predicting a drilling fluid test result for a drilling fluid at a field site using a local instance of a trained machine learning model and/or a remote instance of the trained machine learning model.

[00166] As an example, a method can include automatically generating an instruction to alter a drilling fluid at a field site based on a predicted drilling fluid test result. In such an example, the method may include automatically altering the drilling fluid using the instruction. In such an example, the instruction can be or include one or more of a viscosity adjustment instruction, a density adjustment instruction, a solids adjustment instruction, etc.

[00167] As an example, a system can include one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive drilling fluid input data during operations performed at a field site; predict a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and output the predicted drilling fluid test result.

[00168] As an example, one or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: receive drilling fluid input data during operations performed at a field site; predict a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and output the predicted drilling fluid test result.

[00169] As an example, a piece of field equipment may be equipped to perform and/or to receive one or more types of drilling fluid data and automatically generate a predicted result for one or more drilling fluid tests. In such an example, a controller may implement control action, for example, to adjust a drilling fluid property, a drilling fluid flowrate, a drilling fluid solids removal operation, a drilling fluid disposal operation, a drilling fluid remediation operation, etc. [00170] As an example, a system can account for energy consumption and/or emissions generation of one or more types of field operations, including materials, materials process, materials disposal, etc. As an example, drilling fluid may be taken into account as a drilling fluid where a mud may be oil based or water based (e.g., or of another type). In various instances, mud can come into contact with hydrocarbons during drilling activities. Mud may be subject to processing for purposes of disposal, re-use, re-purposing, etc.

[00171] As an example, a computer program product can include one or more computer-readable storage media that can include processor-executable instructions to instruct a computing system to perform one or more methods and/or one or more portions of a method.

[00172] In some embodiments, a method or methods may be executed by a computing system. Fig. 16 shows an example of a system 1600 that can include one or more computing systems 1601-1 , 1601 -2, 1601 -3 and 1601 -4, which may be operatively coupled via one or more networks 1609, which may include wired and/or wireless networks.

[00173] As an example, a system can include an individual computer system or an arrangement of distributed computer systems. In the example of Fig. 16, the computer system 1601 -1 can include one or more modules 1602, 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.).

[00174] As an example, a module may be executed independently, or in coordination with, one or more processors 1604, which is (or are) operatively coupled to one or more storage media 1606 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1604 can be operatively coupled to at least one of one or more network interfaces 1607; noting that one or more other components 1608 may also be included. In such an example, the computer system 1601 -1 can transmit and/or receive information, for example, via the one or more networks 1609 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.).

[00175] As an example, the computer system 1601 -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 1601-2, etc. A device may be located in a physical location that differs from that of the computer system 1601 -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.

[00176] 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.

[00177] As an example, the storage media 806 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.

[00178] 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.

[00179] 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. 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.

[00180] 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.

[00181] 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.

[00182] 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).

[00183] 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.).

[00184] Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments. 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 drilling fluid input data during operations performed at a field site; predicting a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and outputting the predicted drilling fluid test result.

2. The method of claim 1 , wherein the predicting the drilling fluid test result is generated for a drilling fluid at the field site using a previous drilling fluid test result from an actual drilling fluid test as an input to the trained machine learning model.

3. The method of claim 2, wherein the actual drilling fluid test comprises a performance time in excess of 10 minutes and wherein the predicting occurs in less than 10 minutes.

4. The method of claim 2, wherein the actual drilling fluid test comprises a 30 minute gel test.

5. The method of claim 2, wherein the actual drilling fluid test comprises adjusting the temperature of the drilling fluid at the field site to a specified temperature.

6. The method of claim 1 , wherein the trained machine learning model comprises decision trees.

7. The method of claim 1 , wherein the trained machine learning model is trained using historical data from drilling fluid reports in a drilling fluid reports database.

8. The method of claim 1 , comprising generating the trained machine learning model.

9. The method of claim 8, wherein the generating comprises implementing gradient boosting.

10. The method of claim 9, wherein the gradient boosting comprises ordered boosting.

11. The method of claim 1 , wherein the predicting the drilling fluid test result is generated for a drilling fluid at the field site and further comprising altering the drilling fluid at the site responsive to the predicting.

12. The method of claim 1 , wherein the predicting the drilling fluid test result is generated for a drilling fluid at the field site using a local instance of the trained machine learning model.

13. The method of claim 1 , wherein the predicting the drilling fluid test result is generated for a drilling fluid at the field site using a remote instance of the trained machine learning model.

14. The method of claim 1 , comprising automatically generating an instruction to alter a drilling fluid at the field site based on the predicted drilling fluid test result.

15. The method of claim 14, comprising automatically altering the drilling fluid using the instruction.

16. The method of claim 14, wherein the instruction comprises a viscosity adjustment instruction.

17. The method of claim 14, wherein the instruction comprises a density adjustment instruction.

18. The method of claim 14, wherein the instruction comprises a solids adjustment instruction.

19. A system comprising: one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive drilling fluid input data during operations performed at a field site; predict a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and output the predicted drilling fluid test result.

20. One or more computer-readable storage media comprising processorexecutable instructions to instruct a computing system to: receive drilling fluid input data during operations performed at a field site; predict a drilling fluid test result using the drilling fluid input data and a trained machine learning model; and output the predicted drilling fluid test result.