US20240218789A1

US20240218789A1 - Water washing estimation and prediction from statistical relationships of gas compositions

Info

Publication number: US20240218789A1
Application number: US18/093,029
Authority: US
Inventors: Assad Hadi Ghazwani; Fatai A. Anifowose; Khaled Arouri
Original assignee: Saudi Arabian Oil Co
Current assignee: Saudi Arabian Oil Co
Priority date: 2023-01-04
Filing date: 2023-01-04
Publication date: 2024-07-04

Abstract

A computer-implemented method for estimating water washing parameters is described. The method includes estimating, using at least one hardware processor, a gas oil ratio (GOR) by deriving statistical relationships between gas composition ratios. The method includes calculating, using the at least one hardware processor, a C7 transformation ratio (Tr1) and present-day reservoir temperature (PDRT) based on the GOR. The method also includes creating, using the at least one hardware processor, a water washing intensity index based on the GOR, Tr1, and PDRT. Additionally, the method includes integrating, using the at least one hardware processor, the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map is used to guide water washing of accumulations.

Description

TECHNICAL FIELD

This disclosure relates generally to water washing estimation in oil and gas accumulations.

BACKGROUND

Oil and gas fields include areas of accumulation of respective hydrocarbons in reservoirs, trapped as it rises by impermeable rock formations. Water washing alters geochemical composition and bulk physical properties of oil and gas accumulations. In particular, water washing strips soluble hydrocarbons from oil or gas accumulations via dissolution.

SUMMARY

An embodiment described herein provides a method for estimating water washing parameters. The method includes estimating, using at least one hardware processor, a gas oil ratio (GOR) by deriving statistical relationships between gas composition ratios. The method includes calculating, using the at least one hardware processor, a C7 transformation ratio (Tr1) and present-day reservoir temperature (PDRT) based on the GOR. The method includes creating, using the at least one hardware processor, a water washing intensity index based on the GOR, Tr1, and PDRT. Additionally, the method includes integrating, using the at least one hardware processor, the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map is used to guide water washing of accumulations.
An embodiment described herein provides an apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations. The operations include estimating a gas oil ratio (GOR) by deriving statistical relationships between gas composition ratios. The operations include calculating a C7 transformation ratio (Tr1) and present-day reservoir temperature (PDRT) based on the GOR. The operations include creating a water washing intensity index based on the GOR, Tr1, and PDRT. Additionally, the operations include integrating the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map is used to guide water washing of accumulations.
An embodiment described herein provides a system. The system comprises one or more memory modules and one or more hardware processors communicably coupled to the one or more memory modules. The one or more hardware processors is configured to execute instructions stored on the one or more memory models to perform operations. The operations include estimating a gas oil ratio (GOR) by deriving statistical relationships between gas composition ratios. The operations include calculating a C7 transformation ratio (Tr1) and present-day reservoir temperature (PDRT) based on the GOR. The operations include creating a water washing intensity index based on the GOR, Tr1, and PDRT. Additionally, the operations include integrating the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map guides water washing of accumulations.
In embodiments, gas compositions are obtained in real time, during drilling.
In embodiments, gas compositions are obtained after drilling by analyzing rock samples obtained during drilling.
In embodiments, integrating the water washing intensity index with the gross depositional map includes an indicated water washing intensity index associated with a corresponding depositional environment of the integrated map.
In embodiments, the statistical relationships are predetermined correlations between gas composition ratios and GOR.
In embodiments, calculating Tr1 and PDRT based on the GOR includes applying predetermined correlations to the GOR.
In embodiments, the water washing intensity index quantifies an intensity of a water washing process as none, very slight, slight, moderate, or severe.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a workflow for estimating water washing parameters.

FIG. 2 shows a plot of C1/C3 and GOR.

FIG. 3 shows a plot of C2/C3 and Log(GOR).

FIG. 4 shows a plot of Log(GOR) and PDRT.

FIG. 5 shows a plot of GOR and Tr1.

FIG. 6A shows a water washing index.

FIG. 6B shows a GDE map.

FIG. 7 shows a workflow for predicting water washing parameters.

FIG. 8 shows the output of serval machine learning methods.

FIG. 9 is a process flow diagram of a method for estimating water washing parameters.

FIG. 10 is a block diagram of a process for predicting water washing parameters.

FIG. 11 is a schematic illustration of an example controller for water washing estimation and prediction from statistical relationships of gas compositions according to the present disclosure.

DETAILED DESCRIPTION

Water washing alters oil and gas accumulations by affecting hydrocarbon types in the accumulations. For example, light alkanes, aromatic hydrocarbons, and other non-hydrocarbons are removed from oil and gas accumulations through contact with moving formation waters in reservoirs, during migration, reservoir storage, or during production. Soluble compounds are removed from the oil and gas accumulations by direct connection of a hydrocarbon reservoir to water. In examples, water washing causes spatial variations in fluid composition within the reservoir, leading to a mismatch between the gas-oil ratio (GOR) and American Petroleum Institute (API) gravity (low GOR accompanied by high API gravity).
Some water washing parameters can be determined from direct lab measurements of heptane (C7) in light hydrocarbon analysis of oil samples. A C7 oil transformation star diagram is created using specialized equipment, such as gas chromatographs, and a geochemistry specialist in a time consuming process. However, water washing parameters can be unavailable when measured in a laboratory setting. For example, water washing parameters are not available for certain wells due to various reasons, such the cost of downhole pressure-volume-temperature (PVT) data or laboratory geochemical measurements.
Embodiments described herein enable water washing estimation and prediction from statistical relationships of gas compositions. In examples, water washing parameters are estimated from gas compositions. Water washing parameters include, for example, a C7 transformation ratio (e.g., the ratio of toluene to 1,1-dimethylcyclopentane (Tr1)), gas/oil ratio (GOR), and present-day reservoir temperature (PDRT). Gas compositions include, for example, methane (C1), ethane (C2), propane (C3), butanes (C4). The water washing parameters (Tr1, GOR, and PDRT) are not always available via lab measurements. To overcome a lack of laboratory measured water washing parameters, the present techniques provide a workflow to estimate the water washing parameters Tr1, GOR and PDRT directly and indirectly from C1, C2, and C3 gas compositions without any extra cost and time. Water washing parameters are estimated through deriving accurate statistical relationships from C1, C2, and C3 gas compositions. In examples, the C1, C2, and C3 gas compositions are obtained in real-time from mud gas data or from a detailed PVT dataset. The complete set of parameters (original and estimated) are then used to estimate a water washing intensity index. This index is then integrated with gross depositional environment (GDE) maps to predict hydrodynamic traps, such as hydrodynamic traps in inverted basins.
FIG. 1 shows a workflow 100 to estimate water washing parameters. In the example of FIG. 1 , GOR (block 102) is estimated from C1/C3 (block 112) and C2/C3 (block 114) gas composition ratios. The C7 transformation ratio Tr1 (block 104) and PDRT (block 106) are derived from the estimated GOR (block 102). Water washing parameters GOR (block 102), Tr1 (block 104), and PDRT (block 106) are used to determine a water washing intensity (block 120). The water washing intensity (block 120) and GDE map (block 108) are used to determine hydrodynamic traps (block 122). In examples, a hydrodynamic trap is formed due lateral pressure variation within a basin aquifer, where the formation water flow laterally causes tilting of oil-water and gas-water contacts (OWC-GWCs). The severe water washing is a strong indicator to hydrodynamic flow.
For example, either while drilling or after drilling, C1, C2, and C3 gas compositions are collected from a drilling rig (e.g., while drilling) or database (e.g., after drilling). The ratios C1/C3 and C2/C3 are calculated. Gas ratio analysis provides a first look evaluation of a formation and its fluids. In examples, gas values are determined at various depths and ratios amongst the gas values are determined. Statistical relationships are derived between the ratios and the water washing parameters consecutively as follows:
$G O R = 0.0 2 6 2 x^{6} - 1.7 9 3 4 x^{5} + 4 3.4 7 3 x^{4} - 4 5 2.2 3 x^{3} + 2 0 0 4.8 x^{2} - 299 8.4 x + 874, where x is the C 1 / C 3 ratio .$ $Log (GO R) = - 0.1 5 6 x^{6} + 0.9 5 8 7 x^{5} - 1.4 2 0 7 x^{4} - 2.6 4 3 7 x^{3} + 1 0.0 0 6 x^{2} - 8.1201 x + 2.8533, where x is the C 2 / C 3 ratio .$
In examples, these statistical relationships are a predetermined correlation between gas composition ratios and GOR.
FIGS. 2 and 3 show the plots of the statistical relationships to estimate the GOR parameter from the gas composition ratios C1/C3 and C2/C3 with an accuracy of 0.86 and 0.85, respectively. In FIG. 2 , the x-axis 202 corresponds to the C1/C3 ratio, the y-axis 204 corresponds to GOR. In FIG. 3 , the x-axis 302 corresponds to the C2/C3 ratio, the y-axis 304 corresponds to Log(GOR).
In embodiments, the estimated GOR from either the C1/C3 or C2/C3 ratios is used to estimate the remaining water washing parameters, Tr1 and PDRT.
In examples, PDRT is derived from the estimated GOR using this equation:
$P D R T = 2.0 6 1 6 x^{6} - 3 2.1 1 6 x^{5} + 1 9 5.9 1 x^{4} - 5 9 6.7 x^{3} + 9 5 4.6 9 x^{2} - 75 1.1 9 x + 294.27, where x is Log (G OR) .$
FIG. 4 shows the plot of this equation giving an R-Squared value of 0.74. This is considered acceptably accurate. In FIG. 4 , the x-axis 402 corresponds to Log(GOR), the y-axis 404 corresponds to PDRT. In examples, the R-squared value is a coefficient of determination representing a statistical measure in a regression model that determines the proportion of variance in the dependent variable (e.g., PDRT) that can be explained by the independent variable (e.g., Log(GOR)).
The C7 transformation ratio Tr1 is derived from the estimated GOR using this equation:
$T R 1 = 3 E - 2 2 x^{6} - 1 E - 1 7 x^{5} + 7 E - 1 4 x^{4} + 3 E - 1 0 x^{3} - 4 E - 0 6 x^{2} + 0.0052 x + 0.4 155, where x is GOR .$
In examples, GOR correlates to PDRT and Tr1 according to the predetermined correlations shown in the above equations.
FIG. 5 shows the plot of this equation giving an R-Squared value of 0.86. This is high accuracy. In FIG. 5 , the x-axis 502 corresponds to GOR, the y-axis 504 corresponds to Tr1. In examples, the R-squared value is a coefficient of determination representing a statistical measure in a regression model that determines the proportion of variance in the dependent variable (e.g., Tr1) that can be explained by the independent variable (e.g., GOR).
The complete set of estimated parameters (GOR, Tr1 and PDRT) is used to create a water washing intensity (WWI) index. The index 600A is shown in FIG. 6A, ranging from 0 (none) to 4 (severe). As shown in FIG. 6A, for parameter Tr1 at reference number 602, greater than 2.50 indicates no water washing (index of 0); 2.00-2.50 indicates very slight water washing (index of 1); 1.50-2.00 indicates slight water washing (index of 2); 1.00-1.50 indicates moderate water washing (index of 3); and 0.001-1.00 indicates severe water washing (index of 4). For parameter GOR at reference number 604, greater than 2000 indicates no water washing (index of 0); 1500-2000 indicates very slight water washing (index of 1); 100-1500 indicates slight water washing (index of 2); 200-1000 indicates moderate water washing (index of 3); and 2-200 indicates severe water washing (index of 4). For parameter PDRT at reference number 606, greater than 90 indicates no water washing (index of 0); 85-90 indicates very slight water washing (index of 1); 80-85 indicates slight water washing (index of 2); 75-80 indicates moderate water washing (index of 3); and 65-75 indicates severe water washing (index of 4).
FIG. 6B shows how the WWI indicators are integrated with a GDE map 600B to identify potential hydrodynamic traps. In examples, the WWI index in the legend at reference number 620 is integrated with GDE maps to predict the potential hydrodynamic traps. A C7 oil transformation star diagram is provided at reference number 622, including Tr1, Tr2, Tr3, Tr4, Tr5, Tr6, Tr7, and Tr8. A legend at reference number 624 indicates formation types, including silty sand flat, playa, muddy silt flat, highland-lowland, sandsheet, dune-interdune, ephemeral channels (conceptual and seismic), and localized tidal deltas (conceptual and seismic).
In examples, the WWI index in the legend at reference number 620 corresponds to the index 600A of FIG. 6A. For example, the WWI index in the legend at reference number 620 is based on criteria as provided in the Table 1 of FIG. 6A, including the set of original and estimated water washing parameters. As shown in FIG. 6B, the legend at reference number 620 indicates heavily water washed (hydrodynamic structure), moderately water washed, slightly water washed, and no water washed. Integration between the water washing geochemical indicator Tr1 and GDE map in FIG. 6B shows variable reservoir facies. The silty sand sheet is most susceptible to water washing as indicated by water washing indicators Tr1 and may therefore potentially contain hydrodynamically trapped accumulations.
The three parameters are used to estimate water washing: Tr1, GOR, and PDRT. In examples, machine learning models are trained to predict the water washing parameters. Machine learning models are trained to make predictions (e.g., outputs). For example, machine learning models are trained using a dataset. The machine learning model makes decisions and learns from the dataset. Once trained, the machine learning model can make decisions in response to unseen data, and make predictions about the unseen data.
FIG. 7 shows a workflow 700 for predicting water washing parameters using machine learning. The present techniques predict the parameters used to estimate water washing (WW) in hydrocarbon reservoirs using gas composition data obtained in real time and the machine learning (ML) methodology. In the example of FIG. 7 , historical gas composition data comprising C1, C2, and C3 is collected and ratios (C1/C2, C2/C3, and C1/C3) are calculated. The ratios are used as input features from offset wells (block 702). In examples, the offset wells are used to guide exploration efforts to find new hydrocarbon opportunities or to steer away from high risk areas. The exploration can target the same reservoir quality or traps as observed in the offset wells. Information such as reservoir rock and properties are mapped and modeled in the offset wells, and have high chance of continuing in the unseen or projected well locations. Based on the available reservoir data such as API, GOR, and water washing information on the current wells, a recommendation can be made on whether to drill at the current well.
The gas composition data from block 702 is integrated with the corresponding historical water washing parameters comprising Tr1, GOR, and PDRT as measured from offset wells (as target feature or output) (block 704) to build a training database (block 706). In some examples, building the training database includes determining water washing parameters from gas compositions as described above. The training database is used to build and optimize a ML model (block 708) to establish a valid relationship between the gas components (C1, C2, C3, C1/C2, C2/C3, and C1/C3) and the water washing parameters (Tr1, GOR, and PDRT).
In a new well being drilled, real-time gas components (e.g., from a drilling rig) and their ratios (block 710) are presented to the optimized, trained ML model while drilling to predict Tr1, GOR, and PDRT in real time (block 712), without laboratory measurements. For example, the water washing intensity index is created in real-time based on gas compositions data, such as C1, C2, and C3, obtained while drilling in real-time. Accordingly, the predicted water washing parameters are used to create a WWI (block 714). Lastly, the WWI is integrated with GDE map (block 716) to estimate/calculate hydrodynamic traps (block 718). The criteria applied on the water washing parameters to create the WWI are presented in FIG. 6A. FIG. 6B shows the results of integrating the WWI with GDE map to estimate the stratigraphic traps.
Accordingly, the present techniques enable a powerful and rapid prediction the extent of water washing based on statistical relationships where one or two parameters are missing. The present techniques quantify the effect of water washing on hydrocarbon reservoir fluids. Additionally, the present techniques predict potential hydrodynamic traps. Additionally, the present techniques provide solutions to hydrocarbon exploration and field development. Predictions of potential hydrodynamic traps associated with water washing are made in real time, since the C1, C2, and C3 gas compositions are obtained from mud gas data or from detailed PVT dataset.
FIG. 8 shows the output of serval machine learning methods respectively including machine learning (ML), artificial neural network (ANN), decision tree (DT), support vector regression (SVR), and random forest (RF) to predict water washing based on input parameters (API, GOR and PDRT). In embodiments, machine learning models predict the parameters used to estimate water washing (WW) using gas composition data obtained in real time and the machine learning (ML) methodology. For example, the ML model predicts the water washing parameters in real time for a new well being drilled. In examples, real time refers to determining the parameters as drilling operations are executed for the new well. The water washing parameters can inform future drill operations to enable efficient production from the new well.
The present techniques use an integrated approach based on machine learning, gas components, and reservoir fluid properties such as Tr1, GOR and reservoir paleotemperature to predict the extent of water washing on oil and gas reservoirs. In examples, the ML model is built and optimized by integrated datasets of historical gas composition and their corresponding water washing parameters (Tr1, GOR, and PDRT). Tr1 and GOR are the most susceptible parameter to water washing, but when integrated with PDRT, offers a more robust result. Combining these parameters gives a high quality prediction of water washing in wells with no water washing measurement. These parameters are used to estimate the water washing intensity index in real time. The integration of the gross depositional environment (GDE) map with the water washing intensity index (WWI) predicts potential hydrodynamic traps.
FIG. 9 is a process flow diagram of a process 900 for estimating water washing parameters using ratios of light hydrocarbons (specifically, C1/C3 and C2/C3), without the use of a C7 oil transformation star diagram. Water washing parameters include C7 transformation ratio (Tr1), gas-oil ratio (GOR) and present day reservoir temperature (PDRT).
At block 902, C1, C2 and C3 gas compositions are obtained from the drilling rig or from a database during or after drilling. In examples, the C1, C2 and C3 gas compositions are collected at multiple depths during drilling. In examples, the gas compositions obtained by drawing rock samples at each depth and then analyzing the samples.
At block 904, GOR is estimated by deriving statistical relationships between the C1/C2 and C2/C3 ratios.
At block 906, using estimated GOR, PDRT and Tr1 are estimated.
At block 908, using estimated GOR, PDRT, and Tr1, a water washing intensity (WWI) index is created. In examples, the water washing parameters and WWI index determined for each depth at which the C1, C2 and C3 gas compositions are determined.
At block 910, the WWI index is integrated with a gross depositional environment (GDE) map showing variable reservoir facies. In examples, the WWI index is selected based on the interaction of the main parameters that are highly affected by water washing such as TR1, GOR, PDRT. As described above, in FIG. 6A, the relationship between these parameters are shown, where at certain values of these parameters water washing can be classified as non, very slight, slight, moderate and severe.
In examples, the GDE map is created at a regional scale (tens or hundreds of kilometers scale). The GDE map is focused specifically on the environments that deposited the rocks at the time period being considering. Example polygons on a GDE map could be any depositional environment, including fluvial channels, deepwater slope channels, lake margin carbonates, salt-water marsh shales, etc. In examples, integrating the WWI index with the GDE map provides additional polygons on the GDE map that indicates the water washing intensity index associated with oil accumulations. In examples, the integrated map is rendered on a display device, such as a computer screen.
At block 912, potential hydrodynamic traps are predicted. In examples, a pathway to inject CO2 into the subsurface is derived based on the predicted hydrodynamic traps. When there is a strong hydrodynamic flow, hydrocarbons are flushed away from the trap. The remaining is water where hydrodynamic traps have low reservoir temperatures and can be used for CO2 storage because the CO2 solubility increases in water at low temperatures. Additionally, in examples, the location of one or more hydrodynamic traps are predicted using the GDE map. Formation fluid pressure data are used for mapping prospective hydrodynamic traps. Combining formation fluid pressures with other data, such as density and subsea elevation, produces maps that help outline areas where hydrodynamic gradients may have created, destroyed, or modified traps.
FIG. 10 is a block diagram of a process 1000 for predicting water washing parameters using ratios of light hydrocarbons (specifically, C1/C3 and C2/C3). At block 1002, C1, C2 and C3 gas compositions are obtained from the drilling rig or from a database during or after drilling. In examples, the C1, C2 and C3 gas compositions are collected at multiple depths during drilling. In examples, the gas compositions are obtained by drawing rock samples at each depth and then analyzing the samples. Additionally, the gas compositions are obtained while drilling through several gas tests suitable to the well conditions and drilling operations. These gas tests include but are not limited to, Drilling-Stem Test (DST), Cased Hole Test (CHT), and Modular Formation Dynamic Test (MDT), and the like.
At block 1004, a training data set is built by integrating the gas composition data determined with corresponding historical water washing parameters (Tr1, GOR, PDRT) measured from offset wells. In some embodiments, the training dataset includes gas composition data and corresponding estimated water washing parameters (Tr1, GOR, PDRT) determined as described with respect to FIG. 9 .
At block 1006, an ML model is built and optimized using the training dataset to establish a valid relationship between the gas components (C1, C2, C3, C1/C3, C2/C3) and the water washing parameters (Tr1, GOR, PDRT). In examples, the machine learning model is trained to predicted water washing parameters based on gas composition input data. In examples, training a model with training samples that include labeled data generated automatically from a gas composition ratios improves the model performance when executed on real-world input data.
At block 1008, for anew well being drilled, measure C1, C2, C3 and determine C1/C3 and C2/C3. At block 1010, using the trained ML model, water washing parameters (Tr1, GOR and PDRT) are determined for the new well. At block 1012, the water washing intensity (WWI) index is created for the new well using the determined water washing parameters for the new well. At block 1014, the WWI index is integrated with a gross depositional environment (GDE) map showing variable reservoir facies. At block 1016, potential hydrodynamic traps are predicted.
FIG. 11 is a schematic illustration of an example controller 1100 (or control system) for water washing estimation and prediction from statistical relationships of gas compositions according to the present disclosure. For example, the controller 1100 may be operable according to the process 1000 of FIG. 10 , using the workflow 100 of FIG. 1 . The controller 1100 is intended to include various forms of digital computers, such as printed circuit boards (PCB), processors, digital circuitry, or otherwise parts of a system for supply chain alert management. Additionally the system can include portable storage media, such as, Universal Serial Bus (USB) flash drives. For example, the USB flash drives may store operating systems and other applications. The USB flash drives can include input/output components, such as a wireless transmitter or USB connector that may be inserted into a USB port of another computing device.
The controller 1100 includes a processor 1110, a memory 1120, a storage device 1130, and an input/output interface 1140 communicatively coupled with input/output devices 1160 (for example, displays, keyboards, measurement devices, sensors, valves, pumps). Each of the components 1110, 1120, 1130, and 1140 are interconnected using a system bus 1150. The processor 1110 is capable of processing instructions for execution within the controller 1100. The processor may be designed using any of a number of architectures. For example, the processor 1110 may be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor.
In one implementation, the processor 1110 is a single-threaded processor. In another implementation, the processor 1110 is a multi-threaded processor. The processor 1110 is capable of processing instructions stored in the memory 1120 or on the storage device 1130 to display graphical information for a user interface on the input/output interface 1140.
The memory 1120 stores information within the controller 1100. In one implementation, the memory 1120 is a computer-readable medium. In one implementation, the memory 1120 is a volatile memory unit. In another implementation, the memory 1120 is a nonvolatile memory unit.
The storage device 1130 is capable of providing mass storage for the controller 1100. In one implementation, the storage device 1130 is a computer-readable medium. In various different implementations, the storage device 1130 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device.
The input/output interface 1140 provides input/output operations for the controller 1100. In one implementation, the input/output devices 1160 includes a keyboard and/or pointing device. In another implementation, the input/output devices 1160 includes a display unit for displaying graphical user interfaces.
There can be any number of controllers 1100 associated with, or external to, a computer system containing controller 1100, with each controller 1100 communicating over a network. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one controller 1100 and one user can use multiple controllers 1100.
Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. The example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.
The terms “data processing apparatus,” “computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatus, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, for example, LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.
A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files storing one or more modules, sub programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes, the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.
The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.
Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory. A computer can also include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive.
Computer readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks. Computer readable media can also include magneto optical disks and optical memory devices and technologies including, for example, digital video disc (DVD), CD ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), and a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touchscreen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback including, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that is used by the user. For example, the computer can send web pages to a web browser on a user's client device in response to requests received from the web browser.
The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.
Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a Web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20 or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.
The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship. Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate.
Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of the present disclosure.
Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system comprising a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.
Particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, some processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results.

Claims

What is claimed is:

1. A computer-implemented method for estimating water washing parameters, the method comprising:

estimating, using at least one hardware processor, a gas oil ratio (GOR) by deriving statistical relationships between gas composition ratios;

calculating, using the at least one hardware processor, a C7 transformation ratio (Tr1) and present-day reservoir temperature (PDRT) based on the GOR;

creating, using the at least one hardware processor, a water washing intensity index based on the GOR, Tr1, and PDRT; and

integrating, using the at least one hardware processor, the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map guides water washing of accumulations.

2. The computer implemented method of claim 1, comprising obtaining gas compositions in real time, during drilling.

3. The computer implemented method of claim 1, comprising obtaining gas compositions after drilling by analyzing rock samples obtained during drilling.

4. The computer implemented method of claim 1, wherein integrating the water washing intensity index with the gross depositional map comprises an indicated water washing intensity index associated with a corresponding depositional environment of the integrated map.

5. The computer implemented method of claim 1, wherein the statistical relationships are predetermined correlations between gas composition ratios and GOR.

6. The computer implemented method of claim 1, wherein calculating Tr1 and PDRT based on the GOR comprises applying predetermined correlations to the GOR.

7. The computer implemented method of claim 1, wherein the water washing intensity index quantifies an intensity of a water washing process as none, very slight, slight, moderate, or severe.

8. An apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

estimating a gas oil ratio (GOR) by deriving statistical relationships between gas composition ratios;

calculating a C7 transformation ratio (Tr1) and present-day reservoir temperature (PDRT) based on the GOR;

creating a water washing intensity index based on the GOR, Tr1, and PDRT; and

integrating the water washing intensity index with a gross depositional environment map, wherein a resulting integrated map guides water washing of accumulations.

9. The apparatus of claim 8, comprising obtaining gas compositions in real time, during drilling.

10. The apparatus of claim 8, comprising obtaining gas compositions after drilling by analyzing rock samples obtained during drilling.

11. The apparatus of claim 8, wherein integrating the water washing intensity index with the gross depositional map comprises an indicated a water washing intensity index associated with a corresponding depositional environment of the integrated map.

12. The apparatus of claim 8, wherein the statistical relationships are predetermined correlations between gas composition ratios and GOR.

13. The apparatus of claim 8, wherein calculating Tr1 and PDRT based on the GOR comprises applying predetermined correlations to the GOR.

14. The apparatus of claim 8, wherein the water washing intensity index quantifies an intensity of a water washing process as none, very slight, slight, moderate, or severe.

15. A system, comprising:

one or more memory modules;

one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory models to perform operations comprising:

creating a water washing intensity index based on the GOR, Tr1, and PDRT; and

16. The system of claim 15, comprising obtaining gas compositions in real time, during drilling.

17. The system of claim 15, comprising obtaining gas compositions after drilling by analyzing rock samples obtained during drilling.

18. The system of claim 15, wherein integrating the water washing intensity index with the gross depositional map comprises an indicated a water washing intensity index associated with a corresponding depositional environment of the integrated map.

19. The system of claim 15, wherein the statistical relationships are predetermined correlations between gas composition ratios and GOR.

20. The system of claim 15, wherein calculating Tr1 and PDRT based on the GOR comprises applying predetermined correlations to the GOR.