Computer simulation of multiphase, multicomponent fluid flow including sub-resolved porous structure physics.

Abstract

To provide a computerized technique for performing a fluid simulation of a porous medium.SOLUTION: Techniques includes: searching for representations of three-dimensional porous media, in which the representations include pore space corresponding to the porous medium, and the representations include at least part of a low-resolution pore structure in the porous medium; defining a representative flow model that includes the low-resolution pore structure in the representations; and constructing a fluid force curve corresponding to fluid force in the low resolution pore structure in the representations by a computer system.SELECTED DRAWING: Figure 3

Description

[Background technology]

[0001] Multi-component fluid flow through porous regions is an important property of hydrocarbon reservoir rocks and is a vital input to the oil and gas industry as well as other industries.

[0002] Numerical simulation of multi-component fluid flow in porous domains with complex solid structures is of great importance in many industrial applications, such as Enhanced Oil Recovery (EOR) and Personal Protective Equipment (PPE). To obtain accurate simulation results, it is crucial to capture relevant data from porous structures at all scales.

[0003] In many porous materials, the range of scales often spans many orders of magnitude in size. Furthermore, the range of scales is often unevenly distributed in space. Fully scale-resolved simulations of such porous structures are computationally prohibitively expensive, both in terms of data size and computational time and resources. Therefore, such simulations are often limited to samples that are a much smaller fraction of the size of the original porous material of interest. The latter simulations may sacrifice statistical representation or limit the types of porous materials investigated. Summary of the Invention

[0004] According to one aspect, a computer-implemented method for performing a fluid flow simulation of a porous medium includes retrieving, by a computing system, a representation of a three-dimensional porous medium, the representation including a pore space corresponding to the porous medium, and Sub-resolutionand a computing system for searching the representation, including at least a portion of the under-resolved pore structure. Sub-resolution A representative flow model including a pore structure is defined, and the model is represented by a computer system. Sub-resolution and constructing a fluid force curve corresponding to the fluid force in the pore structure.

[0005] Embodiments of the method can include any one or more of the following features or other features disclosed herein.

[0006] A representation of the three-dimensional porous medium is obtained from the three-dimensional image to which three-dimensional segmentation is applied. Sub-resolution The pore structure is also called PM type Sub-resolution The method comprises simulating a fluid flow through a porous medium, the porous medium belonging to a class of pore structures, the pore structure being represented by a description common to all members of the class; Sub-resolution When detecting pore structures, Sub-resolution Accessing a model of the pore structure; Sub-resolution and applying a fluid force curve to the pore structure.

[0007] The applying further includes applying the fluid force curve according to local pore space data, including local porosity, local orientation / spatial gradient of local porosity, obtained from a representation of the three-dimensional porous medium.

[0008] The method includes, by a computing system, generating a representation based on the local pore space data. Sub-resolution Further comprising calculating a hydraulic force curve from pre-computed physical properties including absolute permeability, relative permeability, and capillary pressure curve of the pore structure.

[0009] Calculating the local pore space data based representation Sub-resolutionand calculating physical properties including absolute permeability, relative permeability, and capillary pressure curves through simulation of a set of highest resolution pore-scale subregions of the rock mass.

[0010] The method further includes storing, in a repository or library, parametric models for the physical properties with parameters fitted to the simulation data or adjusted according to subject matter expert knowledge.

[0011] The representation is of a physical rock sample, and the representation includes pore space and grain space data corresponding to physical porous rock samples, porous particle filters, and similar physical porous media.

[0012] According to an additional aspect, a computer system includes one or more processor devices including: a memory coupled to the one or more processor devices; and a storage storing executable computer instructions for performing a fluid flow simulation of a porous medium, the instructions causing the one or more processors to retrieve a representation of the three-dimensional porous medium, the representation including a pore space corresponding to the porous medium, the representation including a pore space corresponding to the porous medium, and the representation including a pore space corresponding to the porous medium. Sub-resolution At least a portion of the pore structure is included, Sub-resolution Define a representative flow model including pore structure and express Sub-resolution It is configured to construct a fluid force curve corresponding to the fluid forces in the pore structure.

[0013] Embodiments of the computer system may include any one or more of the following features or other features disclosed herein.

[0014] A representation of the three-dimensional porous medium is obtained from the three-dimensional image to which three-dimensional segmentation is applied.

[0015] The system simulates fluid flow through a porous medium, Sub-resolution When detecting pore structures, Sub-resolutionAccess the pore structure model, Sub-resolution It is further configured to apply a fluid force curve to the pore structure.

[0016] Local pore space data includes local porosity, local orientation / spatial gradient of local porosity, obtained from a representation of the three-dimensional porous medium.

[0017] According to an additional aspect, a computer program product tangibly stored on a computer-readable non-transitory storage device storing executable computer instructions for performing a fluid flow simulation of a porous medium, the instructions causing a computing system to retrieve a representation of a three-dimensional porous medium, the representation including a pore space corresponding to the porous medium, and the representation including a pore space corresponding to the porous medium. Sub-resolution At least a portion of the pore structure is included, Sub-resolution Define a representative flow model including pore structure and Sub-resolution A computer program product that constructs a fluid force curve corresponding to the fluid forces in the pore structure.

[0018] Implementations of the computer program product may include any one or more of the following features or other features disclosed herein.

[0019] The computer program product is a representation based on local pore space data. Sub-resolution It further includes instructions for calculating a hydraulic force curve from pre-computed physical properties including absolute permeability, relative permeability, and capillary pressure curve of the pore structure.

[0020] The computer program product is a representation based on local pore space data. Sub-resolution The method further includes instructions for calculating physical properties including absolute permeability, relative permeability, and capillary pressure curves through simulation of a set of highest resolution pore-scale subregions of the rock mass.

[0021] The computer program product further includes instructions for storing, in a repository or library, parametric models for the physical properties, with parameters fitted to the simulation data or adjusted according to subject matter expertise.

[0022] The computer program product simulates fluid flow through a porous medium; Sub-resolution When detecting pore structures, Sub-resolution Access the pore structure model, Sub-resolution It further includes instructions for applying a fluid force curve to the pore structure.

[0023] One or more of the above aspects may provide one or more of the following advantages.

[0024] The numerical approach described herein enables the simulation of multi-component fluid flow in multi-scale porous structures while avoiding the prohibitive computational costs associated with full-scale resolution simulations of multi-scale porous structures. Sub-resolution A complete set of pre-computed physical properties applicable to the region is used to locally introduce appropriate forces acting on the fluid components. The use of physical properties of a representative porous structure generally results in significant savings in computational cost while maintaining reasonable accuracy.

[0025] The proposed approach is applicable to the lattice Boltzmann method and other computational fluid dynamics methods, including finite volume methods, finite element methods, etc. The stored physical properties of the representative solid structures can be broadly applied to any spatial scale and various fluid conditions, such as viscosity, surface tension, and surface wettability, because the properties can be stored in a phenomenological, dimensionless form.

[0026] Other features and advantages of the invention will become apparent from the following description, and from the claims. [Brief explanation of the drawings]

[0027] [Figure 1] FIG. 1 is a diagram of a system for simulating multiscale porous structures using a model with appropriate forces locally applicable to sub-resolved regions of the multiscale porous structure. [Figure 2A-B] Cross-section of carbonate rock with original scanned image (Figure 2A) and segmented image showing small-scale porous regions marked in gray (Figure 2B). [Figure 3] 1 is a flowchart illustrating the operation of a multi-component fluid flow simulation for a sub-resolved porous structure. [Figure 4] 1 is an image showing various types of sub-resolved porous structures. [Figure 5] FIG. 1 is a diagram of the relationship between permeability and porosity. [Figure 6] 1 is a model curve of discharge / intake capillary pressure versus water saturation. DETAILED DESCRIPTION OF THE INVENTION

[0028] Referring to Figure 1, the multiscale porous material Sub-resolution A system 10 for performing simulations of multiscale porous materials having domains. The purpose of the simulations can be varied, such as for simulation of "wetting recovery" or "aging" processes representative of subsurface reservoir conditions, i.e., for "numerical aging." Other simulations can include the effect of steam flow on PPE, etc.

[0029] The focus of the discussion herein is Sub-resolutionThe present invention relates to the simulation of multiscale porous materials having domains. Generally, the system 10 in this embodiment is based on a client-server or cloud-based architecture and includes a server system 12 implemented as a massively parallel computing system 12 (standalone or cloud-based) and a client system 14. The server system 12 includes a memory 18, a bus system 11, an interface 20 (e.g., a user interface / network interface / display or monitor interface, etc.), and a processing device 24.

[0030] As an example of wettability variation, the memory 18 includes a numerical aging engine 32 that manipulates a digital representation of a physical material, e.g., a physical rock sample (digital rock sample), which digitally represents the pore space and grain space of the digital representation of the physical material, e.g., a physical rock sample. Sub-resolution There is also a simulation engine 34 that simulates wettability changes using a simulation of a multi-scale porous material with domains.

[0031] In some embodiments, the simulation of multiphase flow behavior comprises: Sub-resolution Simulations using multiscale porous materials with regions are used to conduct multiphase flow through reservoir rock adjacent to a gas or oil well (e.g., drilling rig 37). Determining multiphase flow behavior involves determining changes in the wettability of a physical rock sample.

[0032] The digital representation of the physical rock sample may be a third-party application running on a different system than server 12. The digital representation of the physical rock sample 32' is all that is needed for system 10 to digitally prepare the digital representation of the physical rock sample for the numerical aging engine. One approach to providing the digital representation 32' of the rock sample is to obtain the representation 32' from a 3D image generated, for example, from a micro-CT scan of the rock sample.

[0033] For complete details of the simulation process, see commonly assigned U.S. patent application Ser. No. 15 / 880,867, entitled "Multi-Phase Flow Visualizations Based On Fluid Occupation Time," filed Jan. 26, 2018. For other exemplary cases, see commonly assigned U.S. patent application Ser. No. 16 / 243,285, entitled "Determining Fluid Flow Characteristics Of Porous Mediums," filed Jan. 9, 2019, or U.S. patent application Ser. No. 16 / 545,387, entitled "Determination Of Oil Removed By Gas Via Miscible Displacement In Reservoir Rock," filed Aug. 20, 2019.

[0034] The memory 18 may store parameters used by the engine 32. In particular, the parameters used in the case entitled "Determination of Oil Removed by Gas Via Miscible Displacement in Reservoir Rock" may include grain surface characteristics, as well as surface texture and roughness characteristics, obtained by assigning mineral types 33a to grains and determining surface characteristics for each of the mineral types, for one or more of the above-mentioned applications of the disclosed subject matter. The memory 18 may also store fluid properties 33b of each expected fluid (e.g., two or more of water, gas, and oil), such as fluid density and viscosity, and fluid-fluid interfacial tension characteristics. The memory 18 also stores parameters such as fluid chemical composition data 33c and fluid component affinity data 33d for specific mineral types. The memory 18 also stores the dissociation pressure 33e for each mineral type and fluid combination and the selected aging time 33f used by the aging engine 32. In addition, reservoir pressure and temperature data are also stored. The mineral types evaluated may be those found or expected at the actual location of the reservoir.

[0035] The simulation engine 34 includes a module for setting up a simulation environment for a rock sample, a module for performing an exhaust / intake simulation, and a module for calculating the local curvature of surfaces in the pore space. Sub-resolution Also included is a module 50 for performing the processing of multi-scale porous materials having regions.

[0036] The system 10 has access to a data repository 38 that stores 2D and / or 3D meshes, coordinate systems, and libraries that can be used for exhaust / intake simulations using any well-known computational technique, such as computational fluid dynamics or the so-called lattice Boltzmann method. Sub-resolution A library 50a containing a model 50b representing the domain is accessed.

[0037] 2A and 2B are taken from subsurface porous media for oil and gas applications. Sub-resolution 2A illustrates an example of a porous medium with regions. Figure 2A is an original scanned image. Figure 2A shows a typical cross section of a carbonate rock sample 40 with solid structures at multiple different scales, showing small-scale porous structures, three of which are labeled 42.

[0038] Figure 2B is the corresponding segmented image 40' of carbonate rock sample 40 (Figure 2A), which shows small-scale porous structures, three of which are shown at 42' and marked in gray. The difference in length scale between the black and gray structures is approximately 10 times. The gray small-scale porous regions can significantly affect flow behavior, so their contributions must be properly considered. A portion of Figure 2B is shown by phantom line 44', which is further discussed in Figure 4.

[0039] However, resolving all such small-scale details requires extremely fine resolution, resulting in extremely expensive computational simulations. The cost of such simulations can increase by tens of thousands of times compared to the unresolved case due to the increased number of 3D cells and reduced time increments caused by simulating gray regions. Therefore, such highest-resolution simulations are not practical for most industrial applications.

[0040] Process 50 is Sub-resolution Simulate the original porous material with various sizes, including small scales, but included in the original porous material Sub-resolution It is not necessary to resolve all small scales. Sub-resolution Small scale physical features are appropriately incorporated in establishing the processes described below.

[0041] Sub-resolutionThe process 50 for addressing the above-mentioned challenges of multi-component fluid flow simulation of porous structures in a porous sample includes a novel workflow in which the fluid flow simulation only resolves the porous sample up to a certain scale level. Sub-resolution The data contribution from the porous region 42' (gray region in Figure 2B) is taken into account. Sub-resolution The key to understanding this is to recognize that the effect of the porous region of a reservoir is a set of forces, including viscous, pressure, and capillary forces. Numerical models reproduce these forces acting on the resolved fluid by using locally representative physical properties such as absolute permeability, relative permeability, and capillary pressure saturation curves.

[0042] These physical properties are obtained through simulation of much smaller sub-regions of the original porous material at full resolution, representing small-scale unresolved gray porous regions, or are generated by user-calibrated parametric functional forms. These sets of physical properties are stored in a library. Each set of physical properties corresponds to a specific Sub-resolution represents the type of flow associated with the porous structure. Sub-resolution During the simulation of a porous structure, a set of physical properties for that porous type from the library is selected and assigned to the model. By taking into account local pore geometry information, including the porosity (ratio of pore space to total volume) and orientation of the structure, the local Sub-resolution The flow behavior can be adequately reproduced.

[0043] Now referring to Figure 3, Sub-resolution A process 50 for performing a multi-component fluid flow simulation for a porous structure is shown. The process 50 uses a simulation of a porous rock sample as an example. The process 50 performs a multi-component fluid flow simulation for a porous structure under investigation. Sub-resolution The method includes using the scanned image to perform a geometric analysis 52 of typical porous structures to identify different types of porous structures. Sub-resolutionThe types of porous structures in the gray area are used to define a model for the representative flow in each gray area 54. Once the model is defined, it is compared to existing models stored in a library of models. Essentially, a model is defined as including a set of physical properties. If a corresponding set of physical properties already exists in the library 56, a model including the set of physical properties is retrieved from the library 58.

[0044] If no set of properties exists, the process performs a full-resolution simulation 62 in a representative subregion of the gray region 60 to compute a new set of physical properties, including absolute permeability, relative permeability, and capillary pressure saturation curves, for the particular type of porous structure being modeled 64. These sets of physical properties describe the effective multiphase flow behavior of the porous media structure. Computing a new set of physical properties adds the computed new model to the library 66, and the specific model being modeled is added. Sub-resolution Labeled for simulation.

[0045] Process 50 uses local geometry information and physical properties to Sub-resolution Determine whether the part is finished (completed)66. These appropriate fluid forces are Sub-resolution These correspond to viscous, pressure, and capillary forces from the solid structure of the liquid.

[0046] Segmentation Now referring to Figure 4, Sub-resolution There are various methods to define a numerical model for the fluid forces in a domain. By image analysis (segmentation), 3D image analysis can be performed. Sub-resolution Various types of porous regions can be defined. 3D image analysis (segmentation) is applied to the 3D image. After segmentation, as shown in Figure 4, the part of Figure 2B Sub-resolution The porous regions 42a' to 42d' are shown.

[0047] Different materials have different x-ray attenuation coefficients. The grayscale value can be used to classify pixels according to material type (e.g., mineral, fluid, etc.). If a pixel contains sub-resolution porosity, the x-ray attenuation is the average of the solid and pore portions in that pixel (partial volume effect).

[0048] Additional texture features can be used to segment pixels into different porous media (PM) types, for example: Cellular structure (42a') Fibrous structure (42b') Microporous clay (42c') Microporous dolomite (42d')

[0049] Absolute Penetration Rate Absolute permeability is simulated, for example, by a Lattice Boltzmann (LB) solver for single-phase flow. Generally, the simulation output for a macroscopic 3D model is a permeability tensor with 3x3 values. The similarity established between the macroscopic output and the microscopic PM pixel input should generally also be a tensor. However, in the majority of cases, a single permeability vs. porosity function is used. You can approximate the tensor by TIFF0007748278000001.tif6170, In some cases, this can be supplemented by a local orientation that can be computed from the image itself.

[0050] For example, in the "microporous dolomite" PM type (42d'), the pores are small compared to the pixel size, and the internal structure is homogeneous and isotropic. In this case, there is no preferred direction, and the PM permeability can be completely described by a single curve. The PM permeability tensor for this pixel is then expressed as an identity matrix. This is the result of multiplying TIFF0007748278000002.tif6170.

[0051] If nanoscale models of PM types of “microporous clay” are available, for example from focused ion beam (FIB) images, permeability simulations for various subvolumes can be used to determine the permeability versus porosity relationship shown in Figure 5 TIFF0007748278000003.tif6170 can be generated.

[0052] As another example, in the "single channel" PM type, there is a strong directional dependence of the permeability: the preferred direction tends to vary locally depending on the local orientation of the sub-resolution channel in the image. The local orientation can be computed from the image itself, for example from the local derivative of the grayscale values.

[0053] Again, a single permeability curve can be used as input, and local orientation information of the image will be used to adjust the value in a given direction.

[0054] To describe the flow for any orientation, the LB flow solver needs to calculate all contributions to the flow. To implement the permeability tensor effect at a PM pixel, local resistivity coefficients related to the velocity and pressure gradients in various directions can be used. For example, for flow in the y direction induced by a pressure gradient in the x direction, the resistivity coefficient Ryx is used, which is directly related to the corresponding permeability tensor element kyx. Here, ρ is the fluid density and ν is the mechanical viscosity.

[0055]

number

[0056]

number

[0057] The process 50 in turn uses the set of physics for the PM domain to simulate the physics of the multi-scale model.

[0058] viscous force One possible example is the absolute permeability K0 and the relative permeability K1. Using TIFF0007748278000007.tif6150,

number

[0059] The solver can be seen as an extension of Brinkman's formula.

[0060] Capillary pressure Mercury intrusion capillary pressure (MICP) measurements are usually used to determine the capillary pressure behavior of porous media independent of wettability effects.

[0061] In general, when reservoir fluid (oil / water) is used instead of mercury and wettability is considered, there will be an irreducible water saturation (Swi) for drainage and some residual oil saturation (Sor) for intake. The amount of water / oil fluid (Sw+So) present in a PM pixel for various pressure gradients (Po-Pw=Pc(Sw)) is given by the capillary radius r c The size distribution of the particles and the wetting fluid-solid properties are dependent on the

[0062] In the model curve shown in Figure 6, Pc* represents the ejection threshold pressure and ΔPc* represents the width of the pore size distribution. A second curve with a different threshold pressure and corresponding width is included to model the inhalation behavior.

[0063] There are several capillary pressure-saturation models in the literature, such as the Thomeer model, the Brooks and Corey model, and the Bentsen and Anli model. For example, in the Thomeer model, the relationship between capillary pressure and saturation is:

number

[0064] The capillary pressure behavior of each PM type can be represented by such a parameterized curve, which, like the absolute permeability example, may be based on partial volume capillary simulation results of the highest resolution model of that PM type.

[0065] To model the variability of porosity within a given PM type (similar to permeability), the Leverett J function (Sw) can be used, which combines the capillary curves for different samples from the same rock type into a single curve:

number

[0066] In general, the J function (Sw) should be given for each PM type. In that case, the sub-resolution porosity For each individual PM pixel with TIFF0007748278000014.tif6170, the corresponding penetration rate is Using these two values, the individual Pc can be estimated from the J function for this PM type at the required saturation level Sw.

[0067] Furthermore, in the anisotropic case, the anisotropic (P(Sw)) can be calculated for each direction by using the corresponding element of the permeability tensor in the calculation of the J function, similar to the "single channel" PM type.

[0068] Thus, for pores smaller than the pixel size, the capillary pressure behavior is related to the P relationship for a particular PM type. c (S w ) can be modeled by a calibrated J function using

[0069] The solver embodiment calculates the oil / water saturation ratios So and Sw at each PM pixel in terms of discharge. P o -P w =P c (S w ) S w +S o =1 and P(S w ) can include an additional force in the flow direction proportional to

[0070] capillary force For example, the definition of TIFF0007748278000016.tif6150 is:

number

number

[0071] The switch function "G" is used in diffusive multicomponent models because their non-zero interfacial thicknesses can lead to excessive artificial forces. Furthermore, this definition assumes that the component fluids Sub-resolution To mitigate this issue, additional models using the Leverett J function and local pressure can be implemented.

[0072] Relative permeability Following the same approach as for absolute permeability and capillary pressure, relative permeability curves for PM type can be calculated from the nanoscale model and used as library input for PM pixel behavior.

[0073] The Corey model is a common parameterization of the relative permeability curve.

number

number

[0074] The physical properties of the representative porous structure are Sub-resolution When applied to a site, the local preferred orientation of the structure can be taken into account in the following manner.

[0075] The orientation of local solid structures can be calculated from the image, e.g., from the local derivative of the grayscale values. By comparing such structure orientation with the orientation of the major axes of permeability, physical properties are adjusted in the appropriate direction. In the following discussion, the case of absolute permeability is presented.

[0076] Absolute permeability (K) in adjusted tensor form in three dimensions 0,αβ )teeth,

number

number

[0077] Here, the equation is written in Einstein notation, using the spatial subscripts α and β, rather than vector notation. The matrix in the denominator denotes the inverse matrix.

[0078] The numerical approach described herein enables multi-component fluid flow simulations in multi-scale porous structures while avoiding the prohibitive computational costs associated with full-scale resolution simulations of such structures. Sub-resolution Using a complete set of pre-computed physical properties applicable to the region, appropriate forces acting on the fluid components are locally introduced. Multi-scale simulations can be performed without significantly sacrificing computational efficiency, while achieving relative accuracy when compared to either full-scale resolution simulations or avoiding multi-scale simulations in such multi-scale porous structures.

[0079] Wide range Sub-resolutionUsing the physical properties of a few representative porous structures for a region generally provides a more effective means of achieving large savings in computational cost while maintaining reasonable accuracy, since the numerical approaches described here all take into account the local variations in individual structures through adjustments based on local porosity and orientation, which are estimated directly from the images.

[0080] At coarse resolution Sub-resolution Both the simulation and the preceding full-resolution simulation at fine resolution in the non-gray region to capture the physical properties can be performed using a single fluid dynamics solver. The use of a single fluid dynamics solver is very beneficial in terms of consistency in the simulation process, since any computational method exhibits more or less unique numerical aspects that can cause problems when combined with other methods. Using a consistent solver in the simulation process can effectively avoid such problems.

[0081] The proposed approach is applicable to the lattice Boltzmann method and other computational fluid dynamics methods, including the finite volume method, the finite element method, etc.

[0082] The stored physical properties of representative solid structures can be broadly applied to any spatial scale and various fluid conditions, such as viscosity, surface tension, and surface wettability, because the properties can be stored in a phenomenological, dimensionless form. Phenomenological means that the properties are constructed from the results of experiments and / or simulations. The simulated physical properties of a particular multiscale solid structure can be used as input for even larger scale cases. Therefore, by detecting hierarchical structures and classifying structural types, this method can be broadly applied to very complex geometries with structures at various scales.

[0083] Embodiments and functional operations of the subject matter described herein can be implemented in digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware (including the structures disclosed herein and their structural equivalents), or a combination of one or more of these. Embodiments of the subject matter described herein can be implemented as one or more computer programs (i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by or to control the operation of a data processing apparatus). A 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 one or more of these.

[0084] A computer program may also be referred to or described as a program, software, software application, module, software module, script, or code, and may be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may correspond to a file in a file system, but this is not required. A program may be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple associated files (e.g., files storing one or more modules, subprograms, or code portions). A computer program may be deployed so that the program is executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a data communications network.

[0085] A computer suitable for running a computer program may utilize a general-purpose microprocessor or a special-purpose microprocessor, or both, or any other type of central processing unit. Generally, the central processing unit will receive instructions and data from a read-only memory or a random-access memory, or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include one or more mass storage devices (e.g., magnetic, magneto-optical, or optical disks) for storing data, or be operatively coupled to receive data from them or to transfer data to them, or both, although such devices are not required for a computer.

[0086] Computer-readable media suitable for storing computer program instructions and data include, by way of example, any form of non-volatile memory on a medium or memory device, including semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

Claims

1. 1. A computer-implemented method for performing a fluid flow simulation of a porous medium, comprising: accessing from a memory a three-dimensional digital image of the three-dimensional porous medium; performing image processing on the accessed three-dimensional digital image by a computer system; generating, by the computer system, a representation of the three-dimensional porous medium based on performing the image processing, the representation including pore space corresponding to the porous medium; determining, by the computer system, that the representation includes at least a portion of a pore structure in the porous medium having a pore size smaller than a resolution of the fluid simulation; constructing, by the computer system, a fluid force curve corresponding to the fluid forces in the pore structure in the representation; simulating fluid flow through the porous medium with the computer system; applying, by the computer system, the fluid force curve to the pore structure in the representation when detecting at least a portion of the pore structure having a pore size smaller than the resolution of the fluid simulation; A method comprising:

2. The method of claim 1 , wherein performing image processing on the accessed three-dimensional digital image comprises applying a three-dimensional segmentation to the three-dimensional digital image.

3. 2. The method of claim 1, wherein the pore structure belongs to a class of pore structures, also called PM types, and the pore structure is represented by a description common to all members of the class. accessing a model of said pore structure from a library of models; The method of claim 1 further comprising:

5. To apply, The method of claim 4 further comprising applying the fluid force curve according to local pore space data.

6. The method of claim 5 , wherein the local pore space data includes local porosity, local orientation / spatial gradients of the local porosity obtained from the representation of the three-dimensional porous medium.

7. calculating, by said computing system, hydraulic force curves from pre-computed physical properties including absolute permeability, relative permeability, and capillary pressure curves of said pore structure in said representation based on local pore space data; The method of claim 6 further comprising:

8. 8. The method of claim 7, wherein calculating further comprises calculating physical properties including absolute permeability, relative permeability, and capillary pressure curves via simulation of a set of highest resolution pore-scale subregions in the representation based on local pore space data.

9. storing, in a repository or library, parametric models for said physical properties, with parameters fitted to simulation data or adjusted according to subject matter expertise; The method of claim 7 further comprising:

10. The method of claim 1 , wherein the representation is of a physical rock sample, and the representation includes pore space and grain space data corresponding to the physical rock sample, porous particulate filters, and similar physical porous media.

11. one or more processor devices; a memory coupled to the one or more processor devices; and storage storing executable computer instructions for performing a porous media fluid simulation, the instructions causing the one or more processors to: accessing from the memory a three-dimensional digital image of the three-dimensional porous medium; performing image processing on the accessed three-dimensional digital image; generating a representation of the three-dimensional porous medium based on performing the image processing, the representation including a pore space corresponding to the porous medium; determining that the representation includes at least a portion of a pore structure in the porous medium having a pore size smaller than the resolution of the fluid simulation; constructing a fluid force curve corresponding to the fluid forces in the pore structure in the representation; simulating fluid flow through the porous medium; applying the fluid force curve to the pore structure in the representation when detecting at least a portion of the pore structure having a pore size smaller than a resolution of the fluid simulation. Computer system.

12. The system of claim 11 , wherein the instructions to perform image processing on the accessed three-dimensional digital image include applying a three-dimensional segmentation to the three-dimensional digital image. accessing a model of said pore structure from a library of models; The system of claim 11 further configured to:

14. 14. The system of claim 13, wherein applying further comprises applying the fluid force curve according to local pore space data, the local pore space data including local porosity, local orientation / spatial gradient of the local porosity, obtained from the representation of the three-dimensional porous medium.

15. 1. A computer program product tangibly stored on a computer-readable non-transitory storage device storing executable computer instructions for performing a porous media fluid simulation, the instructions comprising: accessing from the memory a three-dimensional digital image of the three-dimensional porous medium; performing image processing on the accessed three-dimensional digital image; generating a representation of the three-dimensional porous medium based on performing the image processing, the representation including a pore space corresponding to the porous medium; determining that the representation includes at least a portion of a pore structure in the porous medium having a pore size smaller than a resolution of the fluid simulation; constructing a fluid force curve corresponding to the fluid forces in the pore structure in the representation; simulating fluid flow through the porous medium; applying the fluid force curve to the pore structure in the representation upon detecting at least a portion of the pore structure having a pore size smaller than the resolution of the fluid simulation. Computer program products.

16. Calculating fluid force curves from pre-computed physical properties including absolute permeability, relative permeability, and capillary pressure curves of the pore structure in the representation based on local pore space data.

16. The computer program product of claim 15, further comprising instructions for:

17. Calculating physical properties including absolute permeability, relative permeability, and capillary pressure curves through simulation of a set of highest resolution pore-scale subregions in said representation based on local pore space data.

16. The computer program product of claim 15, further comprising instructions for:

18. storing, in a repository or library, parametric models for said physical properties, with parameters fitted to simulation data or adjusted according to subject matter expertise; 20. The computer program product of claim 17, further comprising instructions for:

19. Accessing a model of the pore structure from a library of models.

16. The computer program product of claim 15, further comprising instructions for:

20. The computer-implemented method of claim 1 , further comprising: defining, by the computer system, a representative flow model of the pore structure that includes one or more physical properties of the pore structure in the representation.

Images