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    • 专利摘要:
    The present invention relates to a three-dimensional reservoir simulator used to automatically scale up relative permeability and capillary pressure in multi-porosity systems including disparate rock types. A single coarse scale porosity model incorporating the properties of a multiple porosity model can be derived from a single fine-scale porosity model based, at least in part, on a model simulation. comprising data from one or more regions of interest. Field and laboratory measurements of one or more regions of interest can be provided to the fine-scale single porosity simulation model and the single fine-scale porosity model can be subjected to one or more simulation methods. fractional flow and one or more displacement simulation methods. The properties of the fine-scale model may be modified based, at least in part, on the results of one or more fractional flow simulation methods and one or more displacement simulation methods. The properties of the coarse scale model can be derived from the properties of the unique fine-scale porosity model by scaling up the fine-scale single porosity model. The coarse single porosity simulation model incorporating the properties of the multiple porosity model can be used to improve operational decision-making, especially for drilling operations and reservoir management.
    公开号:FR3062873A1
    申请号:FR1762652
    申请日:2017-12-20
    公开日:2018-08-17
    发明作者:Travis St. George Ramsay；Richard Edward Hinkley
    申请人:Landmark Graphics Corp；
    IPC主号:
    专利说明:

    ® FRENCH REPUBLIC
    NATIONAL INSTITUTE OF INDUSTRIAL PROPERTY
    COURBEVOIE © Publication number:
    (to be used only for reproduction orders)
    ©) National registration number
    062 873
    62652
    ©) Int Cl 8 : E 21 B 21/08 (2017.01), E 21 B 47/00, 47/26, G 06 F 17/50, G 06 Q 50/02
    A1 PATENT APPLICATION
    ©) Date of filing: 20.12.17. © Applicant (s): LANDMARK GRAPHICS CORPORA- (30) Priority: 14.02.17 IB WOUS2017017826. TION —US. ©) Inventor (s): RAMSAY TRAVIS ST. GEORGE and HINKLEY RICHARD EDWARD. (43) Date of public availability of the request: 17.08.18 Bulletin 18/33. (56) List of documents cited in the report preliminary research: The latter was not established on the date of publication of the request. (© References to other national documents ©) Holder (s): LANDMARK GRAPHICS CORPORA- related: TION. ©) Extension request (s): ©) Agent (s): GEVERS & ORES Société anonyme.
    AUTOMATION OF THE UPGRADING OF RELATIVE PERMEABILITY AND HAIR PRESSURE IN MULTIPLE POROSITY SYSTEMS.
    FR 3 062 873 - A1 xfi) The present invention relates to a three-dimensional reservoir simulator used to automatically scale up relative permeability and capillary pressure in systems with multiple porosity comprising disparate types of rock. A coarse-scale single porosity model incorporating the properties of a multiple porosity model can be derived from a single fine-scale porosity model based, at least in part, on a simulation of a model including data from one or more regions of interest. Field and laboratory measurements of one or more regions of interest can be provided to the fine scale single porosity simulation model and the fine scale single porosity model can be subjected to one or more methods of simulating fractional flow and one or more displacement simulation methods. The properties of the fine-scale model can be modified based, at least in part, on the results of the one or more methods of simulating fractional flow and the one or more methods of simulating displacement. The properties of the coarse-scale model can be derived from the properties of the fine-scale single porosity model by scaling the fine-scale single porosity model. The coarse-scale single porosity simulation model incorporating the properties of the multiple porosity model can be used to improve operational decision-making, particularly for drilling operations and reservoir management.

    2016-IPM-099948-U1-FR 1
    AUTOMATION OF UPGRADING RELATIVE PERMEABILITY AND HAIR PRESSURE IN MULTIPLE POROSITY SYSTEMS
    AREA OF DISCLOSURE
    The present disclosure relates generally to the characterization of a reservoir and, more particularly, to systems and methods for automatically scaling up relative permeability in multiple porosity systems comprising disparate rock types using a three-dimensional (3D) reservoir simulator to obtain an amalgamated representation of a single porosity type.
    BACKGROUND OF THE INVENTION
    The identification of rock types, also called petrofacies or electrofacies, as a method of characterizing reservoirs and porous media is essential to accurately predict production of hydrocarbons from subsurface reservoirs. The identification of petrofacies or electrofacies is an essential process for upscaling, which is part of the combined characterization and predictive analysis of reservoirs, or a simulation process. Higher scaling refers to the process of assigning petrophysical and hydraulic conductivity properties determined from small-scale measurements to a larger scale, which will generally be used to describe the types of subsurface rocks in meshes of a reservoir simulation model. Petrofacies or electrofacies are used in conjunction with disparate petrophysical properties, hydraulic properties, or combinations thereof, to spatially characterize flow behavior of multiphasic or fractionated fluid in cells of the 3D geocellular grid.
    One of these hydraulic properties is multiple porosity, which can be measured at smaller scales. However, since the parameters of multiple porosity complicate the analysis of the transfer of a subsurface fluid and therefore increase the difficulty of such an analysis, conventional techniques for scaling up assume that all types of rocks are connected evenly to simplify higher scaling solutions. In reality, porous media often include multiple types of rocks including multiple types of porosity which can be defined by connectivity of inter- and intra-rock type. Consequently, conventional upscaling fails to accurately model the movement of subsurface fluids within porous media.
    2016-IPM-099948-U1-FR 2
    Incorporating the multiple porosity properties of multiple rock types can improve simulation models of porous media, which, in turn, can improve operational decision making, particularly in drilling and management operations. tank.
    BRIEF DESCRIPTION OF THE FIGURES
    The present disclosure is described below with reference to the attached drawings in which identical elements are referenced with identical reference numbers, and in which:
    Figure 1 is a schematic cross-sectional diagram showing an example of a wellbore environment for acquiring an entire core, according to one or more aspects of this disclosure.
    Figure 2 is a diagram illustrating an example of an information handling system, according to one or more aspects of this disclosure.
    Figure 3 is a diagram illustrating a system for collecting subsurface data, according to one or more aspects of this disclosure.
    FIGURE 4 is a diagram of a multiple porosity system containing two types of rocks.
    FIG. 5 is a diagram of a simplified model of fluid inter-transfer in a double porosity and double permeability assembly.
    FIG. 6 is a process diagram illustrating an embodiment of a method for implementing the present disclosure.
    FIG. 7 is a process diagram illustrating an embodiment of a method for calculating an absolute and relative permeability using automated methods of simulation of constant and non-constant flow.
    FIG. 8 is a process diagram illustrating an embodiment of a method for calculating a capillary pressure or a characterization curve using methods of displacement simulation.
    FIG. 9 illustrates a discretized fine-scale model of cores comprising one or more grid blocks for use in displacement simulations, according to one or more aspects of the present disclosure.
    FIG. 10 illustrates a diagram making it possible to generate a capillary pressure curve from the drainage results of a displacement simulation process.
    FIG. 11 illustrates a simplified version of the sequence of the displacement simulation method presented in FIG. 6 and described in FIG. 8.
    2016-IPM-099948-U1-FR 3
    Figure 12 illustrates the results of using three example techniques to calculate the relative water permeability (KRW) and the relative oil permeability (KROW), according to one or more aspects of this disclosure.
    DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
    The present disclosure addresses one or more deficiencies in the prior art by providing systems and methods for automatically scaling up relative permeability in multi-porosity systems comprising disparate rock types using a three-dimensional reservoir simulator ( 3D) to develop an amalgamated representation of a single porosity type.
    A fluid exchange model between each type of porosity within a fine-scale model, comprising relative permeability, absolute permeability, and capillary pressure to express a transport of fluid, makes it possible to create a coarse-scale model. for multiple porosity, so that a complete analysis of the exchange of fluids can be simulated to support operational decision-making. The fluid exchange model can be created, in terms of procedure, using a higher scaling of relative permeability based on fractional flow and displacement procedures. The use of automated constant and non-constant displacement procedures allows the relative permeability of immiscible fluid systems with multiple phases / multiple components to be calculated. The stratification effect on a flow with multiple phases / multiple components in permeable media makes it possible to differentiate the effect of a rock-like volume fraction from the effect of geometry / spatial distribution of pores. The description of the flow rate and capillary pressure may allow the scaling of relative permeability by methods of scaling the viscosity and capillary limit. Multiple phase / multiple component relative permeability can be calculated for disparate natural and composite geometries with multiple rock types, so that the effect of a fluid exchange through independent and non-congruent rock types differentiated by geometry can be modeled.
    The present invention can improve the classical analysis of a fractionated flow by reducing the computation time and the interaction with the user. By incorporating a convergence analysis, the present invention allows the simulation to perform additional fractional flow analyzes as soon as convergence is obtained, eliminates the need for user supervision of the process, and terminates non-simulations. convergent as soon as this determination can be made. The present invention also allows
    2016-IPM-099948-U1-EN 4 increase the accuracy of the simulation results by modeling fractional flow processes by injection into a core that take place in a physical laboratory in order to derive absolute and relative permeability. Similarly, the present invention can improve the simulation of the properties of a core compared to known methods by taking into account the types of porosity and the communication of a fluid between the types of porosity.
    In one or more embodiments, a method of upscaling the properties of a fine-scale model in a multiple porosity system, comprising: selecting a first region of a porous medium to be brought to light the upper scale, in which the first region includes one or more regional subsurface properties; the generation of a first simulation model of single porosity on a fine scale; initialization of the first fine-scale single porosity simulation model based, at least in part, on the first or more regional subsurface properties; the calculation of a first absolute permeability and a first relative permeability for the first fine-scale single porosity simulation model based, at least in part, on a first or more fractional flow simulation methods ; calculating a first capillary pressure for the first fine-scale single porosity simulation model on the basis, at least in part, of a first or more displacement simulation methods; scaling the first single porosity model to fine scale to derive a first single porosity model to coarse scale; and modifying one or more subsurface operations based, at least in part, on the first coarse-scale single porosity simulation model.
    In one or more embodiments, a non-transient computer-readable medium storing one or more instructions which, when executed, cause a processor to: select a first region of a porous medium to scale up, in wherein the first region includes one or more regional subsurface properties; generate a first unique porosity simulation model on a fine scale; initialize the first fine-scale single porosity simulation model based, at least in part, on the first or more regional subsurface properties; calculating a first absolute permeability and a first relative permeability for the first fine-scale single porosity simulation model based, at least in part, on a first or more fractional flow simulation methods; calculating a first capillary pressure for the first fine-scale single porosity simulation model on the basis, at least in part, of a first or more displacement simulation methods; scaling the first single porosity model to fine scale to derive a first single porosity model to coarse scale; and display at least Tune among the first permeability
    2016-IPM-099948-U1-EN 5 absolute and the first relative permeability and the first capillary pressure of the first single porosity simulation model on a coarse scale.
    In one or more embodiments, a method for scaling up a relative permeability in a multiple porosity system, comprising: selecting a region of a porous medium to scale up, wherein the region includes one or more regional subsurface properties, and wherein one or more of the one or more regional subsurface properties are based at least in part on analysis of a carrot from the region; generation of a single fine-scale porosity simulation model; initialization of the single-porosity fine-scale simulation model based, at least in part, on one or more regional subsurface properties; calculating an absolute permeability and a relative permeability of the fine-scale single porosity simulation model based, at least in part, on one or more methods of fractional flow simulation, in which one or more several fractional flow simulation methods include: selecting a stage of pressure accumulation; comparing an injection flow to a production rate, in which the injection flow and the production rate correspond to the stage of pressure accumulation; calculating an absolute permeability based, at least in part, on the injection rate and the production rate; and calculating relative permeability based, at least in part, on absolute permeability; calculating a capillary pressure of the fine-scale single porosity simulation model based, at least in part, on one or more displacement simulation methods, wherein the one or more displacement simulation methods comprise saturation of a discretized fine-scale model of a carrot from the region with a first fluid; the injection of a second fluid above the discretized fine-scale model; the injection of the first fluid below the discretized fine-scale model; and calculating the capillary pressure based at least in part on the saturation of the discretized fine-scale model; the fine-scale single porosity model to derive a coarse-scale single porosity model; and modifying one or more subsurface operations based, at least in part, on the coarse-scale single porosity simulation model, wherein the one or more subsurface operations comprise at least one of an operation drilling and reservoir management operation.
    Illustrative embodiments of the present invention are described in detail in this document. For the sake of clarity, all the characteristics of an actual implementation may not be described in the present specification. It will of course be understood that in the development of one of these real embodiments, many specific decisions related to the implementation can be taken to achieve the objectives.
    2016-IPM-099948-U1-EN 6 specific to the implementation, which may vary from one implementation to another. In addition, it will be appreciated that such a development effort may be complex and time consuming, but will only be a routine endeavor for an ordinary specialist in the field who benefits from this disclosure.
    Various aspects of this disclosure may be implemented in various environments. Figure 1 is a schematic cross-sectional diagram showing an example of a wellbore environment for acquiring subsurface data and samples, including whole cores, and pressure and fluid sample data, in order to analyze one or more subsurface properties according to one or more aspects of this disclosure. An example of a wellbore environment 100 for acquiring subsurface data including pressure change and fluid displacement data, according to one or more aspects of this disclosure, is illustrated. The wellbore environment 100 includes a derrick 102 positioned at a surface 104. The derrick 102 can support components of the wellbore environment 100, including a drill string 106. The drill string 106 may include one or more segmented pipes (not shown) which extend below the surface 104 in a borehole 108. The drill string 106 may transmit a drilling fluid (or a drilling mud) necessary to operate a drill bit 110 positioned at the end of drill string 106. The mud transmitted by drill bit 106 can provide the torque necessary to operate drill bit 110. The weight of drill bit 106 can provide axial force on the drill bit 110 which, in conjunction with the rotation of the drill bit 110, can assist in drilling the wellbore 108 through the subsurface formations 112 in the earth. Pressure, flow, and other production data can be captured using a 128 data acquisition unit. The captured data can be used for simulation or calibration of modeling to guide operations. drilling and production management, or any combination thereof.
    The drill string 106 includes a downhole assembly 114 positioned on the drill string 106 above the drill bit 110. The downhole assembly 114 includes a combination of various components, such as one or more drill rods 116, a seismic tool 118, and a downhole motor assembly 120 for housing a motor for the drill bit 110. In some aspects, the measuring devices may include an array of sensors 122, which may include pressure and flow sensors. Although Figure 1 illustrates an onshore subsurface environment at surface 104, the present disclosure also contemplates an offshore environment (not shown).
    Any of the embodiments of this disclosure may be
    2016-IPM-099948-U1-EN 7 implemented using an instruction program executable by a computer, such as program modules, generally known as software applications or application programs executed by a computer. A software application can include, for example, routines, programs, objects, components, data structures, any other executable instruction, or any combination thereof, that perform particular tasks or that implement specific abstract types of data. The software application forms an interface to allow a computer to respond to an input source. For example, Nexus Desktop ™ from Landmark Graphics Corporation can be used as an interface application to implement any of the embodiments of this disclosure. The software application may also cooperate with other applications or code segments to initiate various tasks based, at least in part, on the data received, a data source, or any combination thereof. this. Other applications or code segments can provide optimization components such as, but not limited to, neural networks, terrestrial modeling, matching of histories, optimization, visualization, management data, and economic data. The software application can be stored, placed, or both on any type of memory, such as CD-ROM, magnetic disk, optical disk, bubble memory, and semiconductor memory (for example, various types of RAM or ROM). In addition, the software application and one or more inputs or outputs can be transmitted by various media such as, but not limited to, wireless, cable, optical fiber, wire, telemetry, one or more networks (such as Internet), or any combination thereof.
    In addition, the specialist in the field will understand that one or more of the embodiments may include various configurations of computer systems, in particular portable devices, multiprocessor systems, programmable or microprocessor-based consumer electronic systems, minicomputers, mainframe computers, and any combination thereof. Any number of computer systems and computer networks is acceptable for use with this disclosure. Disclosure can be practiced in distributed computing environments where tasks are performed by remote processing devices which are linked via a communication network. In a distributed computing environment, program modules can be located on both local and remote computer storage media, including memory storage devices. This disclosure may therefore be implemented in connection with various hardware, software, or any combination thereof, in a computer system, an information manipulation system, or another
    2016-IPM-099948-U1-FR 8 treatment system.
    Referring now to Figure 2, a block diagram illustrates an embodiment of a system for implementing one or more embodiments of this disclosure on a computer. The system includes a computing device 200, sometimes called a computing system or information manipulation system, which includes a memory such as a random access memory (RAM) 203, application programs (not shown here), an interface user 208 comprising a mouse 210 and a keyboard 209, a video interface 204, and a central processing unit (CPU) 201. The CPU 201, the video interface 204 and the RAM 203 can be connected to a memory controller ( MCH) 202. The system may also include one or more storage devices, such as a read only memory (ROM) element containing instructions for a basic input / output system (BIOS) 206 and a drive hard drive 207. ROM 206, hard drive 207, and user interface 208 can be connected to another input / output controller (ICH) 205. MCH 202 and ICH 205 can be connected l to each other. The computing device is only an example of a suitable IT environment and is not intended to suggest any limitation with respect to the scope of use or functionality of the disclosure.
    A memory or a storage device mainly stores one or more software applications or one or more programs, which can also be described as program modules containing instructions executable by a computer, which can be executed by the computing unit for implement one or more embodiments of this disclosure. Therefore, the memory can include an upper scaling module for multiple porosities, which can activate one or more of the processors or sub-processors illustrated in Figures 6 to 8. The scaling module for multiples porosités may integrate functionality from additional application programs or third parties, such as Nexus Desktop ™, from system files stored in memory or on a storage device. For example, Nexus Desktop ™ can be used as an interface application to carry out some of the steps shown in Figures 6 to 8. System files, such as an ASCII text file, can be used to store instructions, input from data, or both for the tank simulator, as may be required, for example, in step 601 in Figure 6, step 701 in Figure 7, or step 801 in Figure 8 mentioned in the present document. Although Nexus Desktop ™ can be used as an interface application, other interface applications can be used, or the scaling module for relative permeability can be used as a standalone application.
    Although, as shown, the computing device 200 has one or more
    2016-IPM-099948-U1-EN 9 several generalized memories, the computing device 200 generally comprises a variety of non-transient supports readable by computer. For example, but not limited to, computer-readable non-transient media may include computer storage media and communication media. The memory may include a computer storage medium, such as ROM and RAM, the form of volatile memory, non-volatile memory, or both. A basic input / output system (BIOS), containing the basic routines that help transfer information between elements within the computing unit, for example during startup, is generally stored in a ROM. RAM generally contains data, program modules, other executable instructions, or any combination of these that are immediately accessible to, while running on, the processing unit, or both. By way of example, but not limited to, the computing device 200 may include an operating system, application programs, other program modules, and program data.
    The components presented in the memory can also be included in other non-transient removable / non-removable, volatile / non-volatile computer storage media or the components can be implemented in the computing device 200 via a application programming ("API") or cloud computing, which may reside on a separate computing device connected via a computer system or network (not shown). For example only, a hard disk drive can read or write from non-volatile, non-removable magnetic media, a magnetic disk drive can read or write from a non-volatile, removable magnetic disk, and a disk drive Optical can read or write from a non-volatile, removable optical disk, such as a CD-ROM or other optical media. Other removable / non-removable, volatile / non-volatile computer storage media that can be used in the example operating environment may include, but is not limited to, magnetic tape cassettes, memory cards flash, universal digital discs, digital videotape, solid state RAM, solid state ROM or equivalent. The readers and their associated computer storage medium mentioned above provide storage of computer readable instructions, data structures, program modules and other data for the computing unit.
    The computing device 200 can receive commands or information from a user through the user interface 208 and associated input devices, such as a keyboard 209 and a mouse 210. The input devices can include a microphone, a joystick, a satellite dish, a scanner, voice or gesture recognition, and the like (not shown). These input devices and others are
    2016-IPM-099948-U1-EN 10 often connected to the processing unit via the user interface 208 which is coupled to the ICH 205, but they can be connected via another interface and other bus structures, such as a parallel port or a universal serial bus (USB) (not shown).
    A monitor or other type of display device (not shown) can be connected to the MCH 202 through an interface, such as a video interface 204. A graphical user interface ("GUI") can also be used with the video interface 204 to receive instructions from the user interface 208 and transmit the instructions to the central processing unit 201. A GUI can be used to display the outputs of the processors described in FIGS. 6, 7 and 8, and can be used to refresh or display a modification of subsurface operations or production activities. In addition to the video interface 204, the computing device 200 may also include other peripheral output devices, such as speakers, a printer, external memory, any other device, or any what combination of these (not shown), which can be connected via a peripheral output interface (not shown).
    Although many other internal components of the computing device 200 are not shown, those skilled in the art will understand that these components and their interconnection are well known.
    Referring to Figure 3, a set 300 can be used to capture subsurface data 304 which can be supplied back to a simulator. A subsurface detection tool 118 including sensors 122 can detect changes in one or more subsurface conditions, as depicted in Figure 1, including pressure, flow rates, and other data. The information acquired by the subsurface detection tool 118 can be supplied to the data acquisition unit 128. The data acquisition unit 128, comprising a storage device 302, can store subsurface data 304 on the storage device 302. The storage device 302 may include various storage media, such as a CD-ROM, magnetic disk, optical disk, bubble memory, semiconductor memory (for example, various types RAM or ROM), and any combination of these. The subsurface data 304 can be shared with a communication device 310A comprising an antenna 312A. The communication device 310A can communicate with a network 308 via the antenna 312A, which can transmit sub-surface data 304 to a second communication device 310B, comprising an antenna 312B. The second communication device 312B can then supply the subsurface data 304 to an information manipulation system 200, as described in FIG. 2. The information manipulation system 200
    2016-IPM-099948-U1-EN 11 can store the subsurface data 304 received in a memory device 318, further comprising instructions 320 for manipulating the subsurface data 304. The instructions 320 can request the subsurface data 304 from pass through the bus 316, which may include the ICH 205 or the MCH 202 in Figure 2, to reach the UC 201 for processing. The CPU 201 can return an output of the instructions 320 via the bus 316 so that it is stored in the memory device 318 or for display purposes on a display unit 322. In certain embodiments , subsurface data 304 can be provided to a real-time simulator to allow modeling of subsurface conditions and operational decision-making. In other embodiments, subsurface data 304 can be stored for further processing and delivery to a simulator to allow modeling of subsurface conditions and operational decision making.
    The following description includes automated methods for fine scale properties, including upscaling absolute permeability, relative permeability, and capillary pressure, using fractional flow of liquid in systems comprising disparate rock types described by a multiple porosity type classification. In general, the single permeability and single porosity models ignore inter-rock type fluid communication. By including fine-scale multiple porosity information for a plurality of rock types in a scaling process, a single permeability and single porosity model can be obtained which is better than traditional models by taking take into account the fluid communication between the types of porosity, which traditional models ignore. Although dual porosity and dual permeability assemblies are discussed, the present disclosure contemplates that any of the embodiments are equally applicable to the analysis of higher dimensional models.
    Referring now to Figures 4 and 5, a subsurface material can include multiple types of rock including multiple porosities. The double permeability and double porosity models make it possible to efficiently model a flow of fluid within a fractured porous medium and can be calibrated on the basis of a plurality of parameters to simulate a particular reservoir of a formation, for example a reservoir of the subsurface formation 112 in FIG. 1. FIG. 4 illustrates an example of simplification of a modeling of subsurface fluid transfers in which a fine-scale grid block comprising assemblies of double permeability and double porosity 401 can be modeled as two types of rock with different porosities and permeabilities 402, 403. In more complex assemblies with three or more than three types of porosity, fluid communication can occur only between selected pairs
    2016-IPM-099948-U1-EN 12 of porosity types and cannot occur between other pairs of porosity types. FIG. 5 illustrates an example of a fluid transfer model comprising intra and inter-porosity fluid transfers. The transfers within the grid blocks of the same porosity 501, 502 illustrate a transfer of intra-porosity fluid and the transfers between the grid blocks of different porosities 503 represent a transfer of inter-porosity fluid. Separate processing of different rock types or different inter- or intra-porosity fluid transfers before aggregating the results simplifies the modeling calculations, and allows faster simulation results and far-field characterization of mineral constituents sampled constituting the porous medium.
    Referring now to Figure 6, a process diagram of one embodiment of a method 600 for implementing this disclosure is illustrated. Method 600 illustrates the development of the properties of a single coarse-scale porosity model applicable to a larger-scale analysis based on the convergence of simulations of absolute and relative permeability and the convergence of capillary pressure simulations. The method includes the benefits of multiple porosity models, including more precise fluid transfer modeling, and the benefit of single fine-scale porosity models of a porous medium while performing the required simulations using an automated modeling process. effective scale. Process outputs can include relative and absolute permeability, porosity, capillary pressure, and permeability tensors.
    In step 601, an area of interest of the reservoir to be scaled up can be selected based on mineralogical and petrophysical properties. One or more characteristics or properties of the porous medium of the selected region can be received as inputs. The one or more properties of the porous medium may include, but are not limited to, the distribution of the multiple types of porosity, as well as measured values of absolute permeability, relative permeability, capillary pressure, and porosity of the multiple types of porosity. Regional subsurface rock data, including porosity, can be acquired using computed tomography (CT), magnetic resonance imaging (MRI) analyzes, or similar analyzes in conjunction with the equipment identified in Figures 1 and 3 or similar equipment. In one or more embodiments, a user selects the region through one or more input devices such as, but not limited to, a display device 204, a mouse 209, and a keyboard 210 in FIG. 2.
    In step 602, a single fine-scale porosity simulation model, including the properties of a fine-scale model, is initialized with the values provided in step 601. The initialization values can include, but do not limit yourself, the distribution
    2016-IPM-099948-U1-EN 13 multiple types of porosity, as well as measured values of absolute permeability, relative permeability, capillary pressure, and porosity of the multiple types of porosity. The digital flow simulation model includes a description of the porous medium to be modeled in terms of petrophysical properties; multiple porosity relationships; description of the fluid model, in particular the phases of the fluid and a characterization of pressure-volume-temperature; rock-fluid interaction, including relative permeability; capillary pressure; and contacts / fluid balance. The simulation can model the injection and production of a fluid in, from or both in and from the porous medium described. The initialization of the simulation model can include the definition of inter- and intraporosity relationships between fine-scale rock and the multiple types of porosity. The spatial arrangement of the types of porosity to take into account the types which can exchange fluids can be taken into account in the fine grid.
    In step 606, one or more methods of fractional flow simulation can be implemented involving the unique fine-scale porosity simulation model created in step 602 to calculate the absolute and relative permeability of one or more of several types of rocks including the selected region. Calculations for the automated constant and non-constant flow simulation process including disparate rock types are described in more detail in Figure 7. In general, one or more fractional flow simulation processes may include injection. of a mixture of two or more of two materials in permeable rock types. This can be characterized by a two-phase injection when two materials are injected. Injection materials can include fluids, gases, vapors, solids, and any combination thereof. This disclosure contemplates that any injection fluid can be replaced by one or more other types of injection material. The fractional flow for a given liquid component is the ratio between the injection rate of the given liquid and the total injection rate for the entire flow of the fluid. For example, a fractional flow of liquid can consist of 82% water and 18% oil. The fractional flow of an injected liquid can be compared to the fractionated flow of a production fluid, the production fluid being the fluid returned above the surface or from the sea. A difference between the proportion of the fluid production and the proportion of the injected flow can indicate an interaction with one or more subsurface fluid reservoirs. Similarly, a difference between the total injection rate and the total production rate can signal a change in pressure of the fluid injected inside the porous medium of the model. A change in pressure may indicate a fluid interaction with a fracture, a druse, or an occlusion; a fluid interaction between rock types with different permeability, porosity and wettability within the
    2016-IPM-099948-U1-FR 14 porous medium; and any combination of these.
    The outputs of the fractional flow simulation process can be evaluated to determine if the absolute and relative permeability values converge during the fractional flow simulation processes or if the processes must be stopped for not having converged. If the permeability values converge to correspond within a predefined tolerance of expected values of absolute and relative permeability, then the process 600 can continue. However, if the automated fractional flow simulation methods are unable to converge to the predefined tolerance of expected values of absolute and relative permeability, the entire simulation method 600 may terminate (not shown).
    One or more of the properties of the fine-scale model can be modified based, at least in part, on changes in pressure and fractional fluid flow in the fractional flow simulation processes. The modification of one or more properties of the fine-scale model may require validation. For example, an operator or automated process may attempt to validate laboratory data by comparing the results of a simulation to an injection into a core made in a laboratory.
    In step 610, one or more displacement simulation methods are implemented using the single fine-scale porosity model. The displacement simulation methods are described in greater detail in FIGS. 8 and 11. Unlike the fractional flow simulation methods of step 606 in which two materials are injected, a displacement simulation method can involve the injection of a single material to measure the displacement of materials passing through the porous medium. The one or more displacement simulations can be compared to a measured capillary pressure value of a core or to a capillary pressure characterization curve to determine if the simulation process converges to a realistic value. These outputs from the capillary pressure simulation can represent an improvement compared to the traditional capillary pressure calculation methods, since the displacement simulations can take into account multiple types of porosity, unlike the traditional models. Other means of assessing capillary pressure may include multiple interporosity realizations, representative series, and parallel and random assemblies of rock types.
    The results of the displacement simulation process can be evaluated in order to determine whether the capillary pressure values converge during the displacement simulation processes or if the processes must be stopped for not having converged. If the capillary pressure values converge towards a predefined tolerance of expected capillary pressure values, then the process 600 can continue. However, if automated processes
    2016-IPM-099948-U1-EN 15 of simulated fractional flow are unable to converge to a predefined tolerance of expected capillary pressure values, the entire simulation process 600 can be terminated (see Figure 8).
    The properties of the fine-scale model such as, for example, capillary pressure or a capillary pressure curve, can be changed manually or automatically based, at least in part, on the results of the displacement simulation processes carried out. The results of the displacement simulation processes can be confirmed by physical laboratory comparisons and a simulation of fine-scale models of a sample of porous medium. The fractional flow simulation methods may require manual adjustment, including convergence analysis and flow adjustment. Additional displacement simulation methods can be performed iteratively using an increasingly finely discretized grid until the grid is unresponsive to further refinement (not shown).
    In step 614, the resulting higher scaled single porosity model describing the porous medium may itself require higher scaling. An operator or automated process can stipulate scaling up to describe a larger area of interest, create a scaled up model that can be integrated with similar scaling patterns, reduce resource and computational needs to assess the area of interest, or for other reasons. The method can repeatedly carry out steps 602 to 610 if additional upper scaling is required to obtain an increasingly coarser scale model. As soon as the model reaches the desired level of higher scaling based on one or more of the reasons identified above, the method 600 can proceed to step 618.
    In step 618, the area of interest selected in 601 is compared to a list of areas of interest identified for evaluation. If an identified area of interest has not been evaluated, the method can repeat steps 601 to 614 described above for this area of interest. This process may involve the evaluation, simulation, or both evaluation and simulation of one or more cores, so that multiple areas of interest can be modeled and scaled up. If all the areas of interest have been evaluated, the method can go to step 620.
    In step 620, the coarsely scaled single porosity properties scaled up can be used to modify one or more subsurface operations. The following examples are illustrative and are not intended to be a limitation. For example, an operator can use the relative permeability and capillary pressure values scaled to the top of the model to assess an area of interest in the context of
    2016-IPM-099948-U1-EN 16 preparing for drilling and future production, and for developing reservoir management strategies. Modifying or altering drilling operations and reservoir management strategies may include identifying new drilling sites and opportunities for existing drilling sites, modifying existing drilling operations, modifying flow rates and / or proportions of new and existing stimulation fluids, modification of flow rates and / or proportions of new and existing surfactants, modification of flow rates and / or proportions of other fluids and materials, modification of speeds pumping and / or the introduction of new pumping equipment and / or the removal of existing pumping equipment, the application of enhanced oil recovery techniques, and any combination thereof. In one or more embodiments, altering or modifying drilling operations and reservoir management strategies can modify the wettability of a porous medium to displace trapped fluids within the medium. In one or more embodiments, the alteration or modification of a subsurface operation may include modification of a drilling depth, a drilling location, a drilling speed, modification of a drilling angle, drilling stop, and any combination thereof.
    In one or more embodiments, operators can use the outputs of the present invention to determine whether to continue operations at a particular well and if and where to drill additional wells. The operator can modify the one or more subsurface operations based on a display of the outputs of the simulation process on a GUI or on printed documents. In one or more embodiments, the process outputs can be displayed in graphical form. Alternatively, a method receiving real-time data from subsurface operations can directly modify operations based on the coarse-scale single porosity properties scaled up.
    FIG. 7 is a process diagram illustrating an embodiment of the method of calculating an absolute and relative permeability using automated methods of simulation of constant and non-constant flow, as described in step 606 of FIG. 6 A user can launch a process calculation with a constant or non-constant flow, at his discretion, or the calculation can be automatic. In step 701, porous medium / core simulator data and instructions may be provided to a reservoir simulator, said data being obtainable using methods and estimates which are well known in the art. The simulator data provided may include rock properties derived from a fine scale such as, but not limited to, porosity, absolute permeability, relative permeability, and capillary pressure. Data and estimates from
    2016-IPM-099948-U1-EN 17 simulator provided may include subsurface data acquired with subsurface drilling equipment identified in Figures 1 and 3. The simulator can generate core simulator instructions containing at least one model / fluid type, automated fractionated flows, balance descriptions, output simulator controls, injector / producer locations, and pressure-volume-temperature (PVT) timings and descriptions. These outputs can be used to formulate a digital model of the core for simulation purposes.
    In step 702, the digital model of step 701 can be supplied to a preprocessing algorithm of the carrot simulator. The preprocessing algorithm can designate and assign the regions of relative permeability and capillary pressure based on instructions provided to the simulator by the user in step 701. The preprocessing algorithm can output an initialized model of the core for purposes modeling by the simulator.
    In step 704, the carrot simulator can use the numerical model to determine a step of moment of pressure accumulation. The simulator can advance a moment of dynamic pressure accumulation moment, the moment step being able to depend on at least one model / type of fluid, automated fractional flows, equilibrium descriptions, output simulator commands, injector / producer locations, and pressure-volume-temperature (PVT) timings and descriptions provided in step 701.
    In step 706, the pressure build-up stage of the simulation can be initiated using the simulator to increase the flow for a period of time corresponding to the pressure build-up. Increasing the flow can gradually increase the pressure in the model and can also maintain a gentle pressure solution for the digital model. A soft pressure solution can avoid numerical instabilities that could cause the model to saturate until it oscillates. When the pressure build-up stage is initialized, the simulation can progress to step 710.
    In step 710, an iteration counter of the digital model is evaluated to determine if a minimum number of pressure buildup moment steps have occurred. If the number of pressure build-up steps is less than or equal to the pressure build-up control parameter set in step 701, the simulator proceeds to step 712.
    In step 712, the simulation increments the accumulation phase iteration counter and an additional pressure accumulation is simulated, before returning to step 710 where the evaluation of the iteration counter for the stage of pressure buildup can
    2016-IPM-099948-U1-FR 18 be reassessed. As soon as the iteration counter exceeds the pressure build-up moment control parameter, the simulation can continue at step 714.
    In step 714, the simulator can initialize a fractionated fluid stage for the digital model based on the completion of the pressure build-up phase in steps 706 to 712 using user control parameters provided in the instructions for carrot simulator in step 701. The simulation can track the number of simulated split flow stages that have been simulated by initializing a split flow stage counter in step 714 and incrementing the counter in step 723 after the simulation of each step of fractional flow moments by the simulator. The simulator can produce an injection flow based on an injection flow. The injection flow must be representative of a section of the fluid flow in the porous medium. Production rates are generated using the simulator for a time increment corresponding to a predetermined fractional fluid flow moment step based on injection rate data captured in the field.
    In step 720, the simulation can assess whether the production rate produced by the simulator in step 714 or step 723 converges with an injection rate. If the production rate produced by the simulator in step 714 or step 723 converges with the injection rate supplied to the simulator, the simulation can continue in step 722 in order to quantify the convergence. If the rate and the flow do not converge, the simulation can end.
    In step 722, the simulation can quantify the convergence of step 720 so that the quantized convergence can be compared to a predetermined tolerance. The predetermined tolerance can be provided by a user in step 701. If the injection rate and the production rate have not converged to satisfy the predetermined tolerance, the simulation can continue in step 723. However, if the predetermined tolerance is satisfied, the simulation can continue at step 724, where the simulation can increase a fluid comprising the fractionated flow and decrease another fluid comprising the fractionated flow.
    In step 723, the simulation can run a flow simulation for the split flow stage moment step determined in step 714. After running the flow simulation, the simulation can return to step 720 to evaluate the convergence of the production rate and the injection rate.
    In step 724, the simulation can determine whether all stages of fractional fluid flow have been evaluated based on the data provided in step 701 and the fractional flow stage counter calculated in step 714. If additional fractional fluid flow stages have not yet been evaluated, the simulation can continue
    2016-IPM-099948-U1-FR 19 at step 726 to select another stage of fluid flow. If all the stages of fractionated flow have been evaluated, the simulation can continue at step 728 in order to calculate an absolute permeability.
    In step 726, the simulation can select the next unevaluated fractional flow stage for evaluation. Steps 714 to 724 can be repeated for the selected unassessed fractional flow stage. This process can be repeated until all stages of fractional flow have been evaluated by the simulation.
    In step 728, the simulation can calculate an absolute permeability of the simulated core on the basis of an initial component flow of 100%, that is to say on the basis of a single fluid flow. Absolute permeability calculations use the injection rate and production rate applied in the fractional flow stage starting in step 714. As soon as the absolute permeability calculations are completed, the simulation can proceed to step 730.
    In step 730, the simulation can use the collection of fractional flow results generated in step 723 and the absolute permeability value calculated in step 728 in order to calculate the relative permeability of the simulated core and the method of fractional flow simulation ends. The process outputs, in particular the absolute permeability and the relative permeability, can be used in automated methods for simulating constant and non-constant flow and supplied to other simulations, as described in step 606 of FIG. 6. In one or more embodiments, these outputs can be used to modify one or more subsurface operations.
    We now refer to FIG. 8, which further illustrates the one or more displacement simulation methods carried out in step 610 of FIG. 6. The displacement simulation methods are used to derive a capillary pressure, or a single curve of capillary drainage pressure, representing the composite effects of a carrot comprising different types of porosity and rock. The capillary pressure curve can generally describe capillary pressures and saturation values of a carrot and the corresponding types of porosity and rock. The curve can describe the interaction between capillary pressure and saturation for various injection materials. Injection materials can include fluids, gases, vapors, solids, and any combination thereof. This disclosure contemplates that any injection fluid can be replaced with one or more other types of injection material such as, but not limited to, water, saline, oil, gas, and their mixtures. Displacement simulation methods can beneficially reduce the amount and associated expense of laboratory capillary pressure measurements.
    2016-IPM-099948-U1-FR 20
    A displacement simulation process may require a core. For example, a carrot can have a diameter of about several inches and a length of one or more feet and can be acquired with equipment identified in Figure 1 or similar subsurface drilling equipment. The process may require fine-scale distribution of rock types, porosity types, or both within the core, which can be obtained by computerized tomography (CT), magnetic resonance imaging (MRI) analyzes ), or similar analyzes. The displacement simulation process may also require capillary pressure curves for each type of rock and type of porosity found in the core. As an alternative to the capillary pressure curves of rock types including the core, a generic representation of capillary pressure can be used across rock types with a J-type approach. Porosity and permeability values for rock types and porosity contained in the sample may be required. However, it should be noted that the permeability values can be estimated in order to avoid the costs of an actual measurement of permeability. Finally, a simulator capable of modeling a fine-scale displacement in cores may be necessary.
    The displacement simulation can be initialized in step 802 by constructing a discretized fine-scale model of a carrot. The discretized fine-scale model may include one or more grid blocks, as shown in FIG. 9. A moderately fine-scale model, comprising approximately 100,000 blocks configured in a 30 x 30 x 100 configuration, may be initially used. A model with more blocks can reduce sensitivity, but can also increase computation times, and the size of grid blocks can be selected accordingly. Insensitivity to further reducing the size of a grid block may indicate a sufficiently fine-scale model that is suitable for simulation.
    In step 804, a conservative estimate of the maximum capillary pressure is made from correlations such as, but not limited to, functions J. In order to generate the total capillary pressure curve for the carrot, one or more physical experiments may be required, which may provide estimates for the curve. These estimates and the maximum capillary pressure can be provided to the simulation model during initialization.
    In state 806, the maximum capillary pressure established in step 804 can be used to increase the density of the aqueous phase of the process, which may include scaling the density of water by a factor digital. The objective is to set a value for the density of the aqueous phase so that the maximum estimated capillary pressure is equal to
    2016-IPM-099948-U1-FR 21 (pw - Po} * g * h where h is the height of the simulated core, pw is the density of water, po is the density of oil, and g is the acceleration due to gravity The extension factor can be called the vertical scaling factor (VSF).
    In step 808, the model of the carrot can be saturated with a first fluid, for example water. The simulator can set the initial saturation, as part of a standard procedure for digital flow processes.
    In step 810, a volume of a second fluid, for example an oil, can be injected from above the saturated carrot model, while a volume of a first fluid is produced uniformly along the bottom of the model . The volume size of the second fluid may be large enough to produce a small vertical displacement of the first fluid downward from the first layer of discrete grid blocks of the carrot pattern. As soon as the necessary displacement has occurred, the carrot pattern can be left to balance.
    In step 812, the capillary pressure can be calculated using the average saturation in each layer of the carrot model and the corresponding capillary pressure from the equation p c = (Pw ~ Po) * g * Ax where Pc is the capillary pressure at saturation of residual wetting phase and is the maximum capillary pressure of interest, pw is the density of water, po is the density of oil, g is the acceleration due to gravity, and Jx is the height of the model layer as measured above the underside and toward the center of the grid block for a given layer. The calculation of a capillary pressure curve is described in more detail in Figure 10.
    In step 814, the method can assess whether the second fluid has completely displaced the first fluid from the saturated carrot model. If the first fluid has not yet been fully displaced from the core model, the process can return to step 810 for injecting additional volumes of the second fluid. We can have a certain tolerance so that part of the first fluid is maintained by capillary forces. An assessment of whether the first fluid has been completely displaced can be made by measuring the pressure at the bottom of the model. An arbitrary reference pressure measured at the bottom of the model can be interpreted as a total displacement of the first fluid and the process can continue at step 816.
    In step 816, the method can evaluate the sensitivity of the grid of the carrot model. As indicated above, a grid can be sensitive enough when the simulation is insensitive to further reductions in the size of the grid. This can be
    2016-IPM-099948-U1-FR 22 evaluated by comparing the improvement in simulation data between a first simulation at a given grid size and a subsequent simulation using a finer grid size against a predefined sensitivity threshold. If, for a given grid size, the capillary pressure curve has changed significantly from the previous coarser grid, the refining process should continue at step 818. If it is determined that the analysis becomes insensitive to ripening, as indicated by the above improvement in the sensitivity threshold, then the process can continue at step 820.
    In step 818, the initial grid can be refined by increasing the number of blocks defining the carrot pattern before steps 802 through 816 can be repeated using the refined grid, until the grid is unresponsive to a additional refining.
    In step 820, a second sensitivity study of VSF can be performed. In the second VSF study, the VSF factor can be cut in half and steps 802 through 814 can be performed using a new model that includes two identical versions of the original model stacked one on top of the other. By comparing a calculated capillary pressure value using subsequent displacement simulations while halving the VSF and doubling the height of the model, the process can converge to a derived curve describing the capillary pressure of the model core. This information can be used in step 610 of the method 600 described in FIG. 6. In one or more embodiments, the capillary pressure curve can be used to modify one or more subsurface operations.
    Fa figure 10 illustrates a capillary pressure calculation for a given layer. S w is the volume fraction of the pore space occupied by water and PCmax is the capillary pressure at the saturation of residual wetting phase and is the maximum capillary pressure of interest. Fe WOC is the vertical location where a transition from one fluid phase to another fluid phase begins, for example, from an aqueous phase to an oil phase. In such an example, the WOC can be where an oil column begins and a water column ends. The average saturation of a layer can be calculated by summing the saturation in each block of this layer and dividing by the total number of blocks in this layer. Figure 10 shows how a capillary pressure curve can be constructed by incrementally decreasing the water-oil contact through the vertical space of the core. In Figure 10, the interconnectivity between multiple types of porosity is not taken into account.
    FIG. 11 illustrates a simplified version of the sequence of the displacement simulation method presented in step 610 and described by the method of FIG. 8. An example of a carrot saturated with water is described in the following. a displacement oil,
    2016-IPM-099948-U1-FR 23 but this is not intended to represent a limit. Figure 11 illustrates an idealization of a hypothetical carrot in which one finds types of matrix, fracture and porosity of druse and supposes moreover that all the types of porosity have from one end to the other a uniform capillary pressure . Therefore, Figure 11 does not show grid blocks at a resolution suitable for simulation, but is intended to show that a displacement experiment can provide better accuracy when capillary pressure is calculated using the equation used in the method 800, compared to an ordinary initialization at equilibrium. In particular, a displacement experiment can more accurately simulate water retention due to porosity than ordinary equilibrium initialization.
    Figure 11 (a) illustrates the start of a displacement sequence in which all types of porosity are initially completely saturated with water. Figure 11 (b) illustrates a time when the displacement of the oil in the fracture reached the bottom edge of the two isolated isolated matrix / druse regions. At this time, there is a saturation of free water as indicated above the level of fracture fluid identified in Figure 11 l (c) due to the capillary pressure curve of the matrix regions. As the movement continues, the amount of water in the two isolated regions of Figure 11 (b) may not be further reduced, as shown in Figures 1 l (c) to 1 l (f ). A similar phenomenon is repeated for the other regions isolated in FIGS. 1 l (c) to 1 l (f) when water is still removed from the system by an additional displacement of oil. It should be noted that as soon as the water level is at or below the porosity regions of the druses within the matrix regions, the water coming from the druses can be immediately drained towards the surrounding matrix, as illustrated on the figures 1 l (c) to 1 l (f). Figure 1 l (f) illustrates the total volume of water retained by performing the movement sequence illustrated in Figures 1 l (a) to 1 l (f), which represents a significant improvement compared to an ordinary initialization at balance and can therefore improve capillary pressure calculations. For comparison purposes, the region surrounded in Figure 11 (f) illustrates the volume of water used to calculate capillary pressure when simulating a displacement with ordinary initialization at equilibrium.
    Figure 12 illustrates the results of using three example techniques to calculate relative water permeability (KRW) and relative oil permeability (KROW). According to one or more aspects of the present disclosure, a first technique called “Auto” and comprising an automatic submission, a convergence analysis, and experiments of mimed carrot flooding is determined and illustrated in FIG. 12. It is also illustrated a second technique called “Man, Convg” which requires longer execution times to guarantee convergence and which does not mimic the fractionated flow procedure carried out during carrot floods described in step 606. A
    2016-IPM-099948-U1-EN 24 third technique called MAn, Divrg illustrates the scaling up of relative permeability where fractional flow experiences can quickly generate a divergent solution. As shown in Figure 12, the first technique and the second technique give similar results because convergence occurs in the two evaluations. However, the first technique is more precise than the second technique because the first technique models the carrot flooding procedure described in step 606 of Figure 6. On the contrary, the second technique is initiated when the carrot is in a state saturated and only calculates a fractional pseudo-flow because the initial saturation does not depend on the previous constant fractional flow step.
    The representation of a single amalgamated improved porosity type of a porous medium can be used to manage subsurface reservoirs more effectively than with current representations. The enhanced representation can be used to improve drilling and subsurface production operations, including managing operations, modifying a production and well schedule, and can be used with enhanced oil recovery services. Representation of a single improved amalgamated porosity type can be used to identify the location of subsurface fluids, to assess volumetrics, to understand how fluids move below the surface, and to reduce early experimental drilling operations.
    Although the present disclosure has been described in connection with currently preferred embodiments, those skilled in the art will understand that this is not intended to limit the disclosure to these embodiments. It is therefore contemplated that various alternative embodiments and various modifications may be made to the disclosed embodiments without departing from the spirit and scope of the disclosure defined by the appended claims and their equivalents. In particular, with respect to the disclosed methods, one or more steps may not be necessary in all of the embodiments of the methods and the steps disclosed in the methods may be performed in a different order from that described.
    2016-IPM-099948-U1-FR 25
    权利要求:
    Claims (2)
    [1" id="c-fr-0001]
    1. A method for scaling the properties of a fine-scale model in a multiple porosity system, comprising:
    selecting a first region of a porous medium, wherein the first region includes one or more regional subsurface properties;
    the generation of a first simulation model of single porosity on a fine scale;
    initialization of the first fine-scale single porosity simulation model based, at least in part, on the first or more regional subsurface properties;
    the calculation of a first absolute permeability and a first relative permeability for the first fine-scale single porosity simulation model based, at least in part, on a first or more fractional flow simulation methods ;
    calculating a first capillary pressure for the first fine-scale single porosity simulation model on the basis, at least in part, of a first or more displacement simulation methods; and modifying one or more subsurface operations based, at least in part, on the first coarse-scale single porosity simulation model.
    2. Method according to claim 1, further comprising:
    selecting a second region of the porous medium to scale up, in which the second region includes a second or more regional subsurface properties;
    the generation of a second single porosity simulation model on a fine scale;
    initialization of the second single porosity simulation model on a fine scale based, at least in part, on the second or more regional subsurface properties;
    the calculation of a second absolute permeability and a second relative permeability for the second model of simulation of single porosity on a fine scale on the basis, at least in part, of a second or more methods of simulating fractional flow ;
    2016-IPM-099948-U1-EN 26 the calculation of a second capillary pressure for the second simulation model of single porosity on a coarse scale on the basis, at least in part, of a second or more simulation methods of displacement;
    approximation of the first coarse-scale single porosity simulation model with the second coarse-scale single porosity simulation model.
    3. Method according to any one of claims 1 and 2, in which the calculation of the first capillary pressure for the first simulation model of single porosity on a fine scale on the basis, at least in part, of the first or more methods displacement simulation also includes:
    saturation of a discrete fine-scale model of a carrot from the first region with a first fluid;
    the injection of a second fluid above the discretized fine-scale model; the injection of the first fluid below the discretized fine-scale model; and calculating the first capillary pressure based, at least in part, on the saturation of the discretized fine-scale model.
    The method of claim 3, wherein calculating the first capillary pressure for the fine-scale single porosity simulation model based, at least in part, on the first or more displacement simulation methods further comprises refining the discretized fine-scale model of the carrot.
    5. Method according to any one of claims 1 to 3, in which the calculation of the first absolute permeability and of the first relative permeability for the first simulation model of single porosity on a fine scale based, at least in part, of the first or more fractional flow simulation methods further comprises:
    the selection of a pressure build-up stage;
    comparing an injection flow to a production rate, in which the injection flow and the production rate correspond to the stage of pressure accumulation;
    the calculation of the first absolute permeability on the basis, at least in part, of the injection rate and the production rate; and calculating the first relative permeability based, at least in part, on the first absolute permeability.
    2016-IPM-099948-U1-FR 27
    6. Method according to any one of claims 1 to 4 and 5, wherein at least one of the one or more subsurface operations comprises at least one of a drilling operation and a reservoir management operation. ;
    the first one or more of the regional subsurface properties are based at least in part on an analysis of a carrot from the first region; and the first one or more regional subsurface properties include at least one of absolute permeability, porosity, relative inter-porosity permeability, relative intra-porosity permeability, inter-porosity capillary pressure, and intraporosity capillary pressure .
    7. The method of claim 6, wherein the analysis of the carrot of the first region comprises one or more of a computerized tomographic scan and a magnetic resonance imaging scan of the carrot.
    8. Method according to any one of claims 6 to 7, in which at least one of the reservoir management operation comprises at least one of the modification of a pumping rate of one or more stimulation fluids and changing a ratio of a surfactant; and the drilling operation includes at least one of modifying a drilling depth, a drilling location, and a drilling speed.
    9. Non-transient computer readable medium storing one or more instructions which, when executed, cause a processor to:
    selecting a first region of a porous medium, in which the first region includes one or more regional subsurface properties;
    generate a first unique porosity simulation model on a fine scale; initialize the first fine-scale single porosity simulation model based, at least in part, on the first or more regional subsurface properties;
    calculating a first absolute permeability and a first relative permeability for the first fine-scale single porosity simulation model based, at least in part, on a first or more fractional flow simulation methods;
    2016-IPM-099948-U1-FR 28 calculate a first capillary pressure for the first fine-scale single porosity simulation model based, at least in part, on a first or more displacement simulation methods;
    deriving a first coarse-scale single porosity model based, at least in part, on the first fine-scale single porosity simulation model to derive a first coarse-scale single porosity model; and modify one or more subsurface operations based, at least in part, on the first coarse-scale single porosity simulation model.
    10. A non-transient computer-readable medium according to claim 9, wherein the one or more instructions, when executed, further cause the processor to:
    selecting a second region of the porous medium to scale up, wherein the second region includes a second or more regional subsurface properties;
    generate a second single porosity simulation model on a fine scale; initializing the second fine-scale single porosity simulation model based, at least in part, on the second or more regional subsurface properties;
    calculating a second absolute permeability and a second relative permeability for the second fine-scale single porosity simulation model based, at least in part, on a second or more methods of fractional flow simulation;
    calculating a second capillary pressure for the second single porosity simulation model on a fine scale based, at least in part, on a second or more displacement simulation methods;
    approximate the first coarse-scale single porosity simulation model with the second coarse-scale single porosity simulation model.
    11. A non-transient computer-readable medium according to any one of claims 9 and 10, in which the one or more instructions, when executed, further cause the processor to:
    saturate a discretized fine-scale model of a carrot from the first region with a first material;
    inject a second material above the discretized fine-scale model;
    inject the first material below the discretized fine-scale model; and
    2016-IPM-099948-U1-EN 29 calculate the first capillary pressure on the basis, at least in part, of the saturation of the discretized fine-scale model.
    The non-transient computer-readable medium of claim 11, wherein the one or more instructions, when executed, further cause the processor to refine the discretized fine-scale model of a carrot from the first region.
    13. A non-transient computer-readable medium according to any one of claims 11 and 12, wherein at least one of the first material is a fluid and the second material is a fluid; and the fluid of the first material comprises at least one of water, a salt solution, a gas, and an oil.
    14. A non-transient computer-readable medium according to any one of claims 9 to 11, wherein the one or more instructions, when executed, further cause the processor to at least one of 1):
    select a pressure build-up stage;
    compare an injection flow to a production rate, in which the injection flow and the production rate correspond to the stage of pressure accumulation;
    calculate the first absolute permeability based, at least in part, on the injection rate and the production rate; and calculating the first relative permeability based, at least in part, on the first absolute permeability; and
    [2" id="c-fr-0002]
    2): display at least one of the first absolute permeability and the first relative permeability and the first capillary pressure of the first coarse-scale single porosity simulation model.
    15. Method for scaling up relative permeability in a multiple porosity system, comprising:
    selecting a region of a porous medium, in which the region includes one or more regional subsurface properties, and in which one or more of the one or more regional subsurface properties are based at least in part on the analysis a carrot from the region;
    generation of a single fine-scale porosity simulation model;
    2016-IPM-099948-U1-FR 30 the initialization of the single-porosity fine-scale simulation model based, at least in part, on one or more regional subsurface properties;
    calculating an absolute permeability and a relative permeability of the fine-scale single porosity simulation model based, at least in part, on one or more methods of fractional flow simulation, in which one or more several fractional flow simulation methods include:
    the selection of a pressure build-up stage; comparing an injection flow to a production rate, in which the injection flow and the production rate correspond to the stage of pressure accumulation;
    calculating an absolute permeability based, at least in part, on the injection rate and the production rate; and calculating relative permeability based, at least in part, on absolute permeability;
    calculating a capillary pressure of the fine-scale single porosity simulation model based, at least in part, on one or more displacement simulation methods, wherein the one or more displacement simulation methods comprise :
    saturation of a discretized fine-scale model of a carrot from the region with a first fluid;
    the injection of a second fluid above the discretized fine-scale model;
    the injection of the first fluid below the discretized fine-scale model; and calculating the capillary pressure based at least in part on the saturation of the discretized fine-scale model;
    deriving a coarse-scale single porosity model based, at least in part, on the fine-scale single porosity simulation model; and modifying one or more subsurface operations based, at least in part, on the coarse-scale single porosity simulation model, wherein the one or more subsurface operations comprise at least one of an operation drilling and reservoir management operation.
    20164PM-099948-U1 -EN mo
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    US11163923B2|2021-11-02|
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    GB201909318D0|2019-08-14|
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    法律状态:
    2018-09-28| PLFP| Fee payment|Year of fee payment: 2 |
    2019-12-30| PLFP| Fee payment|Year of fee payment: 3 |
    2020-05-08| PLSC| Publication of the preliminary search report|Effective date: 20200508 |
    2021-05-07| RX| Complete rejection|Effective date: 20210331 |
    优先权:
    申请号 | 申请日 | 专利标题
    PCT/US2017/017826|WO2018151707A1|2017-02-14|2017-02-14|Automated upscaling of relative permeability and capillary pressure in multi-porosity systems|
    IBWOUS2017017826|2017-02-14|
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