ABASS HAZIM H (US)
DUSTERHOFT RONALD GLEN (US)
WO2019098988A1 | 2019-05-23 |
US20100191511A1 | 2010-07-29 | |||
US7603265B2 | 2009-10-13 | |||
US20180355707A1 | 2018-12-13 | |||
US20100088076A1 | 2010-04-08 |
WHAT IS CLAIMED IS: 1. A method comprising: generating a geomechanical model based on a mechanical earth model that represents a subsurface area, wherein the geomechanical model indicates a division of the mechanical earth model into a plurality of grid cells that each correspond to a different volume of the subsurface area; based on a first virtual compaction experiment with the geomechanical model, generating compaction curves, wherein the compaction curves represent porosity as a function of stress; converting the compaction curves from representing porosity as a function of stress to representing porosity as a function of pore pressure; and partially coupling the geomechanical model to a reservoir simulation model using the converted compaction curves. 2. The method of claim 1, wherein partially coupling the geomechanical model to the reservoir simulation model using the converted compaction curves comprises providing the converted compaction curves as input to the reservoir simulation model. 3. The method of claim 1, wherein generating the compaction curves comprises generating one or more compaction curves for different ones of the grid cells. 4. The method of claim 1, further comprising calibrating results from the first virtual compaction experiment against rock lab results. 5. The method of claim 1, further comprising creating the mechanical earth model. 6. The method of claim 5, wherein creating the mechanical earth model comprises performing a second virtual compaction experiment with rock and/or soil data of the subsurface area. 7. The method of claim 5, wherein creating the mechanical earth model comprises creating the mechanical earth model with data corresponding to different geologic scales for the subsurface area. 8. The method of claim 7, wherein creating the mechanical earth model with data of different geologic scales comprises creating the mechanical earth model with data from well logs, rock lab experiments on rock cores, and nano-imaging techniques. 9. The method of claim 1, further comprising predicting strain behavior for the subsurface area during production and injection processes using results from the reservoir simulation model after the partial coupling. 10. The method of claim 1, wherein generating the compaction curves comprises generating the compaction curves based, at least in part, on a true stress-strain curve that is based on information generated from the first virtual compaction experiment. 11. The method of claim 10 further comprising extracting a force-displacement curve from the information generated from the first virtual compaction experiment, wherein the true stress-strain curve is based, at least in part, on the force-displacement curve. 12. The method of claim 11 further comprising calculating an engineering stress-strain curve from the force-displacement curve, wherein the true stress-strain curve is calculated based, at least in part, on the engineering stress-strain curve. 13. The method of claim 1, wherein converting the compaction curves is based, at least in part, on an inversely proportional relationship between stress and porosity. 14. One or more non-transitory machine-readable media having program code, the program code comprising instructions to: generate a first plurality of compaction curves that represent porosity as a function of stress with compaction simulations on different cells of a geomechanical model that divides a mechanical earth model, wherein the mechanical earth model represents a subsurface area at multiple geologic scales; convert the first plurality of compaction curves that represent porosity as a function of stress to a second plurality of compaction curves that represent porosity as a function of pore pressure; and input the second plurality of compaction curves to a reservoir simulation model to predict strain behavior for the subsurface area. 15. The non-transitory machine-readable media of claim 14, wherein the program code further comprises instructions to: generate the mechanical earth model with data of different geologic scales for the subsurface area. 16. The non-transitory machine-readable media of claim 15, wherein the program code further comprises instructions to divide the mechanical earth model into grid cells to generate the geomechanical model. 17. The non-transitory machine-readable media of claim 14, wherein the instructions to generate the first plurality of compaction curves comprise instructions to: for each of the compaction simulations, extract a force-displacement curve from results of the compaction simulation; calculate an engineering stress-strain curve from the force-displacement curve; and determine a true stress-strain curve from the engineering stress-strain curve, wherein one or more of the first plurality of compaction curves for the cell corresponding to the compaction simulation is based on the true stress- strain curve. 18. The non-transitory machine-readable media of claim 14, wherein the instructions to convert the first plurality of compaction curves to the second plurality of compaction curves are based, at least in part, on an inversely proportional relationship between stress and porosity. 19. An apparatus comprising: a processor; and a machine-readable medium having program code executable by the processor to cause the apparatus to, generate a first plurality of compaction curves that represent porosity as a function of stress with compaction simulations on different cells of a geomechanical model that divides a mechanical earth model, wherein the mechanical earth model represents a subsurface area at multiple geologic scales; convert the first plurality of compaction curves that represent porosity as a function of stress to a second plurality of compaction curves that represent porosity as a function of pore pressure; and input the second plurality of compaction curves to a reservoir simulation model to predict strain behavior for the subsurface area 20. The apparatus of claim 19, wherein the instructions to convert the first plurality of compaction curves to the second plurality of compaction curves comprise instructions to convert based on wherein dp’ is effective stress, a is Biot’s constant, ap is Biot’s constant for a soil type, p is pressure, sv is overburden stress, v is Poisson’s ratio, E is young’s modulus, and e is strain. |
Equation (1) yields the following relationship:
The relationship in Equation (2) shows that effective stress and pore pressure are inversely proportional. This relationship allows compaction curves to be converted from porosity as a function of effective stress to porosity as a function of pore pressure.
At block 602, the compaction curves are input into the reservoir simulator. The reservoir simulator processes the converted compaction curves and can account for changes in relative permeability due to the change in porosity and the fluid saturations present within the reservoir. In some cases, reservoir compaction can help maintain pressure stability in the reservoir. The pore pressure within the rock is maintained by the shifting or failure of rock grains allowing the pore volume to be compacted by the overburden weight during production. The compaction forces fluid out of the reservoir until the pressure is stabilized and further compacti on cannot easily occur. In cases like this, the bulk volume water within the rock will often remain constant because it is the wetting surface on the rock face. In these cases, the relative fluid saturations will change as the reservoir is compacted and the relative permeability will therefore also change significantly as water saturation tends to increase and oil saturation tends to decrease. Changes in the relative permeability curves can be calculated and implemented by the reservoir simulator through the converted compaction curves for permeability as a function of pore pressure to account for these complex effects. The converted compaction curves are used as look-up tables or other standard inputs for the reservoir simulator. This allows the reservoir simulator to run without first manually solving for stress and/or strain data.
[0046] At block 603, the reservoir simulation is adjusted to match physical reservoir expectations. The compaction curves are adjusted based on the stress changes during the simulated period for which conversion between stress and pore-pressure occurred. An unstructured grid or an approximation of dual continuum can be used to fully capture the geometry of the natural and hydraulic fractures. [0047] At block 604, the effects captured through the partially coupled simulation are interpreted or quantified. The quantified effects are used to analyze reservoir properties. Different porous components in the reservoir simulation are quantified separately. Separate calibration of porous components allows each component to be treated individually when analyzing the reservoir properties. For example, depletion of reservoir pressure can significantly distort the regional stress field. The partially coupled simulators capture this effect. In ultralow permeability reservoirs, the partial coupling methodology described herein quantifies the effect of interference between two or more horizontal wells. The effect of the sequence and timing of drilling infill wells can also be quantified using the partial coupling method. [0048] FIG. 7 depicts an example of a simulated output result. Output 700 shows a simulation result of flow with fracture geometry explicitly gridded into an unstructured grid. Output 700 is a prediction of pressure in a reservoir around a well as predicted by the simulator. Grid lines (such as grid line 702) represent an unstructured geometry around fractures (such as fracture 701). A well 703 runs diagonally along a portion of the reservoir. The shading represents various pressure levels with lighter colors representing low pressure and darker colors representing higher pressures. Output 700 predicts lower pressure in the areas adjacent to the well 703. Fractures connected, or in close proximity, to the well 703 also experience a pressure loss. Fractures further away from the well 703 are not impacted by the well 703 and do not exhibit the same pressure loss. Variations [0049] While FIGS. 1-7 depict example embodiments of methods for partially coupling geomechanical and reservoir simulators, variations upon these methods may be applied without changing the scope of the technology. Various elements can be used by themselves or in combination with the basic embodiments shown above. For example, tuning and/or calibrating of the compaction curves can be based on rock lab results. This could be used in combination with the operations of FIG. 5. As another example variation, validation of nano-imaging can be done with rock lab results by upscaling nano-imaging data to core scale. Further variation includes history matching of the coupled reservoir model with geomechanical parameters which can be used in combination with the operations of FIG. 5. Example System [0050] FIG. 8 depicts an example system that partially couples a geomechanical model or mechanical earth model with a reservoir simulation model. The system includes a processor 801 (possibly including multiple processors, multiple cores, multiple nodes, and/or implementing multi-threading, etc.). The system includes memory 807. The memory 807 may be system memory or any one or more of the above already described possible realizations of machine- readable media. The system also includes a bus 803 and a network interface 805. [0051] The system also includes a geomechanical simulator 811 and a reservoir simulator 813. The simulator 811 can perform operation of geomechanical simulations, as described above. The reservoir simulator 813 can perform operations of reservoir simulations, as described above. The controller 815 can control the different operations that can occur in the response to results from the simulations. Any one of the previously described functionalities may be partially (or entirely) implemented in hardware and/or on the processor 801. For example, the functionality may be implemented with an application specific integrated circuit, in logic implemented in the processor 801, in a co-processor on a peripheral device or card, etc. Further, realizations may include fewer or additional components not illustrated in Figure 8 (e.g., video cards, audio cards, additional network interfaces, peripheral devices, etc.). The processor 801 and the network interface 805 are coupled to the bus 803. Although illustrated as being coupled to the bus 803, the memory 807 may be coupled to the processor 801. [0052] The flowcharts are provided to aid in understanding the illustrations and are not to be used to limit scope of the claims. The flowcharts depict example operations that can vary within the scope of the claims. Additional operations may be performed; fewer operations may be performed; the operations may be performed in parallel; and the operations may be performed in a different order. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by program code. The program code may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable machine or apparatus. [0053] It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by program code. The program code may be provided to a processor of a general purpose computer, special purpose computer, or other programmable machine or apparatus. [0054] As will be appreciated, aspects of the disclosure may be embodied as a system, method or program code/instructions stored in one or more machine-readable media. Accordingly, aspects may take the form of hardware, software (including firmware, resident software, micro-code, etc.), or a combination of software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” The functionality presented as individual modules/units in the example illustrations can be organized differently in accordance with any one of platform (operating system and/or hardware), application ecosystem, interfaces, programmer preferences, programming language, administrator preferences, etc. [0055] Any combination of one or more machine readable medium(s) may be utilized. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable storage medium may be, for example, but not limited to, a system, apparatus, or device, that employs any one of or combination of electronic, magnetic, optical, electromagnetic, infrared, or semiconductor technology to store program code. More specific examples (a non-exhaustive list) of the machine-readable storage medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a machine-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable storage medium is not a machine-readable signal medium. [0056] A machine-readable signal medium may include a propagated data signal with machine readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A machine-readable signal medium may be any machine-readable medium that is not a machine-readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. [0057] Program code embodied on a machine-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. [0058] Computer program code for carrying out operations for aspects of the disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as the Java® programming language, C++ or the like; a dynamic programming language such as Python; a scripting language such as Perl programming language or PowerShell script language; and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on a stand-alone machine, may execute in a distributed manner across multiple machines, and may execute on one machine while providing results and or accepting input on another machine. [0059] The program code/instructions may also be stored in a machine-readable medium that can direct a machine to function in a particular manner, such that the instructions stored in the machine-readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. [0060] Plural instances may be provided for components, operations or structures described herein as a single instance. Finally, boundaries between various components, operations and data stores are somewhat arbitrary, and particular operations are illustrated in the context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within the scope of the disclosure. In general, structures and functionality presented as separate components in the example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure. [0061] As used herein, the term “or” is inclusive unless otherwise explicitly noted. Thus, the phrase “at least one of A, B, or C” is satisfied by any element from the set {A, B, C} or any combination thereof, including multiples of any element. Example Embodiments [0062] Example embodiments include the following: [0063] A method comprises generating a geomechanical model based on a mechanical earth model that represents a subsurface area. The geomechanical model indicates a division of the mechanical earth model into a plurality of grid cells that each correspond to a different volume of the subsurface area. Based on a first virtual compaction experiment with the geomechanical model, compaction curves are generated. The compaction curves represent porosity as a function of stress. The compaction curves are converted from porosity as a function of stress to porosity as a function of pore pressure. The geomechanical model is partially coupled to a reservoir simulation model using the converted compaction curves. [0064] Partially coupling the geomechanical model to the reservoir simulation model using the converted compaction curves comprises providing the converted compaction curves as input to the reservoir simulation model. [0065] Generating the compaction curves comprises generating one or more compaction curves for different ones of the grid cells. [0066] The method further comprises calibrating results from the first virtual compaction experiment against rock lab results. [0067] The method further comprises creating the mechanical earth model. Creating the mechanical earth model comprises performing a second virtual compaction experiment with rock and/or soil data of the subsurface area. The mechanical earth model is created with data corresponding to different geologic scales for the subsurface area. Data of different geologic scales comprises data from well logs, rock lab experiments on rock cores, and nano-imaging techniques. [0068] The method further comprises predicting strain behavior for the subsurface area during production and injection processes using results from the reservoir simulation model after the partial coupling. [0069] Generating the compaction curves comprises generating the compaction curves based, at least in part, on a true stress-strain curve that is based on information generated from the first virtual compaction experiment. Generating the compaction curves further comprises extracting a force-displacement curve from the information generated from the first virtual compaction experiment. The true stress-strain curve is based, at least in part, on the force-displacement curve. An engineering stress-strain curve is calculated from the force-displacement curve. The true stress-strain curve is calculated based, at least in part, on the engineering stress-strain curve. [0070] Converting the compaction curves is based, at least in part, on an inversely proportional relationship between stress and porosity. [0071] One or more non-transitory machine-readable media comprises program code to generate a first plurality of compaction curves that represent porosity as a function of stress with compaction simulations on different cells of a geomechanical model that divides a mechanical earth model. The mechanical earth model represents a subsurface area at multiple geologic scales. The first plurality of compaction curves that represent porosity as a function of stress are converted to a second plurality of compaction curves that represent porosity as a function of pore pressure. The second plurality of compaction curves are input into to a reservoir simulation model to predict strain behavior for the subsurface area. [0072] The program code further comprises instructions to generate the mechanical earth model with data of different geologic scales for the subsurface area. [0073] The program code further comprises instructions to divide the mechanical earth model into grid cells to generate the geomechanical model. [0074] The instructions to generate the first plurality of compaction curves comprise instructions to, for each of the compaction simulations, extract a force-displacement curve from results of the compaction simulation, calculate an engineering stress-strain curve from the force- displacement curve, and determine a true stress-strain curve from the engineering stress-strain curve. One or more of the first plurality of compaction curves for the cell corresponding to the compaction simulation is based on the true stress-strain curve.
[0075] The instructions to convert the first plurality of compaction curves to the second plurality of compaction curves are based, at least in part, on an inversely proportional relationship between stress and porosity.
[0076] An apparatus comprises a processor and a machine-readable medium having program code executable by the processor to cause the apparatus to generate a first plurality of compaction curves that represent porosity as a function of stress with compaction simulations on different cells of a geomechanical model that divides a mechanical earth model. The mechanical earth model represents a subsurface area at multiple geologic scales. The first plurality of compaction curves that represent porosity as a function of stress are converted to a second plurality of compaction curves that represent porosity as a function of pore pressure. The second plurality of compaction curves are input into a reservoir simulation model to predict strain behavior for the subsurface area.
[0077] The instructions to convert the first plurality of compaction curves to the second plurality of compaction curves comprise instructions to convert based on wherein s p is effective stress, a is Biot’s constant, a p is Biot’s constant for a soil type, p is pressure, s v is overburden stress, v is Poisson’s ratio, E is young’s modulus, and e is strain.