A method for predicting compressional and shear wave velocities in shale reservoirs

Abstract

The present application provides a kind of shale reservoir longitudinal wave and transverse wave prediction method, device, computer readable storage medium and electronic equipment.The method includes analyzing shale logging core mineral components, determining the relationship between porosity and mineral components and the relationship between porosity and speed, and dividing porosity into three types of organic pore, inorganic pore and fissure;Organic pore, inorganic pore and fissure parameters are used as input parameters of the model, and a shale petrophysical model is established based on differential equivalent medium theory model and self-consistent approximation theory model and equivalent embedded body stress average theory model;Estimate shale longitudinal wave and transverse wave speed based on the shale petrophysical model.The present application comprehensively considers micro factors such as porosity and mineral components, and constructs a petrophysical model suitable for the target area by combining SAC, DEM and EIAS equivalent medium theory model, so as to determine longitudinal wave and transverse wave speed, which can ensure the accuracy of longitudinal wave and transverse wave speed prediction.

Description

Technical Field

[0001] This invention relates to the field of oil and gas seismic exploration technology, and in particular to a method, apparatus, computer-readable storage medium, and electronic device for predicting P-waves and S-waves in shale reservoirs based on well logging data and establishing a rock physical model. Background Technology

[0002] Rock physics models are the physical foundation for establishing prediction tools and interpreting inversion results. They serve as the link between well logging data, seismic attributes, and reservoir parameters. Predicting shear wave velocity based on rock physics models is currently one of the most important methods, and the accuracy of the rock physics model directly affects the prediction results of reservoir shear wave velocity. Due to the complexity of shale gas reservoirs (mineral composition, microstructure), a universally applicable rock physics model that can accurately describe their main reservoir properties and velocity relationships has not yet been established.

[0003] Many scholars both domestically and internationally have also conducted corresponding rock physics analysis and theoretical research. Xu and White (1995) based their work on... The theoretical and differential equivalent medium model (DEM) classifies rock pores into sandstone pores and mudstone pores, and proposes using aspect ratio to describe the geometry of pores, ultimately forming the Xu-White model suitable for sandstone and mudstone reservoirs. Zhang Guangzhi et al. (2012), based on the Xu-White model, accurately predicted the shear wave velocity in carbonate rocks after modification and improvement. Yin Xingyao et al. (2015), based on rock pore structure analysis, proposed a method for inverting P-wave and S-wave velocities based on a rock physics model using Raymer formula and an improved Xu-White model. Xiong Xiaojun et al. (2017) proposed a method for predicting shear wave velocity based on a self-compatible model of pore classification theory, and experimental results show that the model has certain generalization significance. Liu Zhishui et al. (2018) introduced the differential equivalence idea into the KT model and proposed a modified model suitable for high concentration inclusions. A rock physics model was used to predict shear waves in organic-rich rock samples in experiments. Compared with the original KT model, this model had a smaller prediction error. Zhang Kefei et al. (2019) constructed a rock physics model suitable for shale by integrating SCA, DEM, Backus averaging, and the Brown-Korringa equation. The shear wave velocity estimated by the model matched the measured data very well. The complex nature of tight shale reservoirs is affected by both macroscopic (mineral composition, porosity) and microscopic structures. Therefore, it is necessary to comprehensively consider macroscopic and microscopic factors to help establish a suitable shale rock physics model, thereby enabling accurate prediction of reservoir shear wave velocity. Summary of the Invention

[0004] To address the aforementioned problems, embodiments of the present invention provide a method, apparatus, computer-readable storage medium, and electronic device for predicting P-waves and S-waves in shale reservoirs.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting P-waves and S-waves in shale reservoirs, including:

[0006] S100 analyzes the mineral composition of shale logging cores, determines the relationship between porosity and mineral composition, as well as the relationship between porosity and velocity, and classifies porosity into three types: organic pores, inorganic pores, and fractures.

[0007] S200 uses the parameters of organic pores, inorganic pores and fractures as input parameters of the model, and establishes a shale petrophysical model based on the differential equivalent medium theory model, the self-compatible approximation theory model and the equivalent embedded stress averaging theory model.

[0008] S300, Estimate the longitudinal and transverse wave velocities of shale based on the shale rock physics model.

[0009] According to an embodiment of the present invention, step S200 above includes the following sub-steps:

[0010] S210, based on the differential equivalent medium DEM theoretical model, adds spherical organic matter pores with a fixed aspect ratio into the organic matter to obtain the bulk modulus and shear modulus of porous organic matter.

[0011] S220, based on the differential equivalent medium DEM theoretical model, adds porous organic matter with a fixed aspect ratio to quartz minerals to obtain the bulk modulus and shear modulus of the first mixed mineral;

[0012] S230, based on the self-compatible approximation SCA theoretical model, the mineral to be mixed and the first mixed mineral obtained in step S220 are mixed to obtain the bulk modulus and shear modulus of the second mixed mineral;

[0013] S240, based on the EIAS theoretical model of equivalent embedded stress, mixes fluid, second mixed mineral, and inorganic pores and fractures to obtain the bulk modulus and shear modulus of saturated fluid shale, thereby obtaining the shale petrophysical model.

[0014] According to an embodiment of the present invention, in step S230 above, the minerals to be mixed include feldspar minerals, carbonate rock minerals and clay minerals.

[0015] According to an embodiment of the present invention, the above method further includes the following steps:

[0016] S400 compares the estimated P-wave and S-wave velocity values ​​with the actual P-wave and S-wave velocity values ​​from the well logging.

[0017] According to an embodiment of the present invention, step S300 includes the following steps:

[0018] Based on the shale rock physics model, the equivalent elastic modulus of saturated fluid shale is calculated;

[0019] Estimate the P-wave and S-wave velocities of shale based on the equivalent elastic modulus of saturated fluid shale and the bulk density of shale.

[0020] According to an embodiment of the present invention, in step S100 above, the mineral composition of shale logging cores is analyzed using logging data.

[0021] Secondly, the present invention also provides a shale reservoir P-wave and S-wave prediction device, characterized in that it comprises:

[0022] The analysis module is used to analyze the mineral composition of shale logging cores, determine the relationship between porosity and mineral composition, as well as the relationship between porosity and velocity, and classify porosity into three types: organic pores, inorganic pores, and fractures.

[0023] The modeling module is used to take the parameters of organic pores, inorganic pores and fractures as input parameters of the model, and establish a shale rock physics model based on the differential equivalent medium theory model, the self-compatible approximation theory model and the equivalent embedded stress averaging theory model.

[0024] The calculation module is used to estimate the longitudinal and transverse wave velocities of shale based on the shale rock physics model.

[0025] According to an embodiment of the present invention, the above-mentioned modeling module includes the following sub-modules:

[0026] The first submodule is used to add spherical organic matter pores with a fixed aspect ratio into the organic matter based on the differential equivalent medium DEM theoretical model, so as to obtain the bulk modulus and shear modulus of the porous organic matter.

[0027] The second submodule is used to add porous organic matter with a fixed aspect ratio to quartz minerals based on the differential equivalent medium DEM theoretical model to obtain the bulk modulus and shear modulus of the first mixed mineral.

[0028] The third submodule is used to mix the mineral to be mixed with the first mixed mineral obtained from the second submodule based on the self-compatible approximation SCA theoretical model, and to obtain the bulk modulus and shear modulus of the second mixed mineral.

[0029] The fourth submodule is used to mix fluids, second mixed minerals, and inorganic pores and fractures based on the EIAS theoretical model of equivalent embedded stress to obtain the bulk modulus and shear modulus of saturated fluid shale, thereby obtaining the shale petrophysical model.

[0030] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a shale reservoir P-wave and S-wave prediction method as described in the first aspect above.

[0031] Fourthly, embodiments of the present invention provide an electronic device comprising:

[0032] processor;

[0033] Memory used to store the processor's executable instructions;

[0034] The processor is configured to execute the instructions to implement a shale reservoir P-wave and S-wave prediction method as described in the first aspect above.

[0035] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0036] The embodiments of this invention provide a method for predicting P-wave and S-wave velocities in shale reservoirs. This method analyzes the mineral composition and microstructure of shale based on well logging data, and the predicted S-wave velocities show good results. The rock physics model fully considers the influence of different porosities and fractures, ensuring the accuracy of the P-wave and S-wave velocity predictions. The estimated S-wave velocities of shale by the constructed rock physics model agree well with the measured data, thus improving data processing and interpretation to a certain extent. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This invention provides a flowchart illustrating the steps of a method for predicting P-waves and S-waves in shale reservoirs according to an embodiment of the present invention.

[0039] Figure 2 A flowchart of the shale rock physics model according to an embodiment of the present invention is shown;

[0040] Figure 3 This displays a well logging curve data graph from an embodiment of the present invention;

[0041] Figure 4 This invention displays a well logging mineral composition map according to an embodiment of the invention;

[0042] Figure 5 The image shows a comparison between the P-wave velocity predicted by the model and the logging data in an embodiment of the present invention.

[0043] Figure 6 The image shows a comparison between the model predictions and well logging data of an embodiment of the present invention for shear wave velocity;

[0044] Figure 7 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1

[0047] This invention relates to the field of rock physics technology, specifically a method for predicting shear wave velocity based on a rock physics model constructed from well logging data. Rock physics models bridge the gap between reservoir properties and seismic attributes. Shale reservoirs are characterized by complex properties, low porosity and permeability, high organic matter content, and diverse mineral compositions. Currently, there is no widely applicable rock physics model. Therefore, this invention utilizes well logging data, comprehensively considering factors such as porosity and mineral composition, and combines SAC, DEM, and EIAS equivalent medium theory models to construct a rock physics model suitable for the target area. Finally, the model's predicted shear wave velocity shows good consistency with the actual well logging data, verifying the model's accuracy and reliability.

[0048] 1. This invention considers the influence of the macroscopic (mineral composition, porosity) and microscopic structure of shale reservoirs, and combines well logging data to establish a suitable shale petrophysical model using the Self-Compatible Approximation (SCA), Differential Equivalent Medium (DEM), and Equivalent Inset Stress Averaging (EIAS) theoretical models. The aim is to accurately predict the shear wave velocity of shale reservoirs. The specific implementation steps are as follows: It consists of four parts: the first part constructs a core matrix model; the second part constructs a core skeleton model; the third part constructs a fluid-bearing model; and the final part predicts elastic parameters.

[0049] Obtain core data from the well, conduct mineral composition analysis and electron microscopy tests on the core, analyze the basic logging petrophysical characteristics of the core, and obtain input parameters for the petrophysical model;

[0050] 2. Based on the microstructural characteristics of the core, the core porosity is first divided into organic pores, inorganic pores, and fractures. Using the differential equivalent medium theory (DEM), spherical organic pores with a fixed aspect ratio are added to the organic matter to obtain the bulk modulus and shear modulus of the porous organic matter. The expression for the DEM (Berryman, 1992) is:

[0051]

[0052]

[0053] The initial condition is K * (0) = K1, μ * (0) = μ1, K1 and μ1 are the bulk modulus and shear modulus of the initial main phase (phase 1), K2 and μ2 are the bulk modulus and shear modulus of the gradually added inclusions (phase 2), y is the content of phase 2, and the geometric factors P and Q are shape factors that control the shape of the inclusions and are determined by the morphology of the pores.

[0054] 3. Using the differential equivalent medium theory (DEM), spherical porous organic matter with a fixed aspect ratio was added to the quartz mineral, and the bulk modulus and shear modulus of the mixed mineral 1 were calculated.

[0055] 4. Using the self-compatibility model (SCA), feldspar minerals, clay minerals, carbonate minerals, and the mixed mineral 1 obtained in step 2 are mixed in a specific ratio to obtain the bulk modulus and shear modulus of mixed mineral 2. The SCA expression used (Berryman, 1998) is as follows:

[0056] Based on the elastic wave dispersion theory, the equivalent bulk modulus K for calculating elliptical inclusion rocks is derived. * SC and shear modulus μ * SC The specific formula is as follows:

[0057]

[0058]

[0059] Where, χ i K is the volume content of the i-th medium. i and μ i P represents the bulk modulus and shear modulus of the i-th medium. *i and Q *i The SCA model represents the geometry of the i-th medium. It has been extended to account for the influence of inclusions of different shapes on the elastic parameters of rocks, with the P and Q coefficients remaining the same. The SCA model assumes a continuous distribution of multiple mineral components and porosity, making it suitable for situations where multiple matrices serve as the background matrix of rocks.

[0060] 5. Using the Equivalent Inset Stress Averaging (EIAS) theoretical model, the fluid, mixed minerals 2, and inorganic pores and fractures were mixed to obtain the bulk modulus and shear modulus of the saturated fluid shale. The expression of the Equivalent Inset Stress Averaging (EIAS) theoretical model (Endres and Knight, 1997) is as follows:

[0061] Assuming the porous rock contains spherical pores and fractures, the high-frequency bulk modulus and shear modulus of the saturated rock are:

[0062]

[0063]

[0064] Among them (Berryman, 1980a; Mavko et al., 2009),

[0065]

[0066]

[0067]

[0068]

[0069] Here, P1 and Q1 correspond to spherical pores, while P2 and Q2 approximately correspond to coin-shaped fissures. These parameters have more precise expressions applicable to flattened spheres of any aspect ratio, including spherical pores and fissures.

[0070]

[0071] Among them, T iijj and T ijij The tensor representing the relationship between strain and uniform far-field strain within an ellipsoidal embedding is found in Berryman (1980b) and Mavko et al. (2009). Porosity φ is divided into hard porosity φ. s (Hard pores) and soft porosity φ c (Soft porosity). The bulk modulus and shear modulus of the background solid matrix are K, respectively. s and μ s The pore space can be filled with dry or fluid, and the fluid bulk modulus is K. f The aspect ratio of the fracture is α, and the volume ratio of the fracture is... This parameter is related to the properties of soft and hard pores.

[0072] 6. Using formula (5-6), the equivalent elastic modulus of saturated fluid rock can be calculated. Combined with the rock's bulk density ρ, the longitudinal wave velocity V of the saturated rock can then be calculated. p and transverse wave velocity V s A comparative analysis was conducted by combining the longitudinal and transverse wave velocities from well logging data with the predicted velocities:

[0073]

[0074]

[0075] The following is a rock physics model for the target area, constructed based on well logging data from a shale formation in the Sichuan Basin. This rock physics model can predict well logging shear wave velocities relatively well.

[0076] Figure 1 The technical process of this invention

[0077] 1) Using well logging data, mineral composition analysis was conducted on the well core samples, including the relationship between porosity and mineral composition, and the relationship between porosity and velocity. Based on scanning electron microscopy results, rock porosity was classified into organic pores, inorganic pores, and fractures, which were used as input parameters for model building.

[0078] 2) This invention utilizes the self-compatible approximation (SCA), the differential equivalent medium (DEM), and the equivalent embedded stress averaging (EIAS) theoretical model to establish a suitable shale rock physical model.

[0079] 3) The P-wave and S-wave velocities of well A in the block were estimated using the constructed shale rock physics model, and compared with the measured P-wave and S-wave logging curves of well A.

[0080] Figure 2 This is a method for predicting shear wave velocity in well logging of shale reservoirs in the Sichuan Basin. Based on the well logging curves, SCA, DEM, and EIAS are combined to establish a rock physics model. The key assumptions of the model are: (1) Organic matter and quartz content show a positive correlation trend, and it is assumed that the organic matter in the rock is mainly filled in quartz minerals; (2) Based on the scanning electron microscopy results of the rock, the rock porosity is divided into organic matter pores, inorganic matter pores, and fractures. The steps include:

[0081] 1. Based on the microstructural characteristics of the core, the core porosity is first divided into organic pores, inorganic pores, and fractures. Using the differential equivalent medium theory (DEM), spherical organic pores with a fixed aspect ratio are added to the organic matter to obtain the bulk modulus and shear modulus of the porous organic matter. The expression for the DEM (Berryman, 1992) is:

[0082]

[0083]

[0084] The initial condition is K * (0) = K1, μ * (0) = μ1, K1 and μ1 are the bulk modulus and shear modulus of the initial main phase (phase 1), K2 and μ2 are the bulk modulus and shear modulus of the gradually added inclusions (phase 2), y is the content of phase 2, and the geometric factors P and Q are shape factors that control the shape of the inclusions and are determined by the morphology of the pores.

[0085] 2. Using the differential equivalent medium theory (DEM), spherical porous organic matter with a fixed aspect ratio was added to the quartz mineral, and the bulk modulus and shear modulus of the mixed mineral 1 were calculated.

[0086] 3. Using the self-compatibility model (SCA), feldspar minerals, clay minerals, carbonate minerals, and the mixed mineral 1 obtained in step 2 are mixed in a specific ratio to obtain the bulk modulus and shear modulus of mixed mineral 2. The SCA expression used (Berryman, 1998) is as follows:

[0087] Based on the elastic wave dispersion theory, the equivalent bulk modulus K for calculating elliptical inclusion rocks is derived. * SC and shear modulus μ * SC The specific formula is as follows:

[0088]

[0089]

[0090] Where, χ i K is the volume content of the i-th medium. i and μ i P represents the bulk modulus and shear modulus of the i-th medium. *i and Q *i The SCA model represents the geometry of the i-th medium. It has been extended to account for the influence of inclusions of different shapes on the elastic parameters of rocks, with the P and Q coefficients remaining the same. The SCA model assumes a continuous distribution of multiple mineral components and porosity, making it suitable for situations where multiple matrices serve as the background matrix of rocks.

[0091] 4. Using the Equivalent Inset Stress Average (EIAS) theoretical model, the fluid, mixed minerals 2, and inorganic pores and fractures were mixed to obtain the bulk modulus and shear modulus of the saturated fluid shale. The expression of the Equivalent Inset Stress Average (EIAS) theoretical model (Endres and Knight, 1997) is as follows:

[0092] Assuming the porous rock contains spherical pores and fractures, the high-frequency bulk modulus and shear modulus of the saturated rock are:

[0093]

[0094]

[0095] Among them (Berryman, 1980a; Mavko et al., 2009),

[0096]

[0097]

[0098]

[0099]

[0100] Here, P1 and Q1 correspond to spherical pores, while P2 and Q2 approximately correspond to coin-shaped fissures. These parameters have more precise expressions applicable to flattened spheres of any aspect ratio, including spherical pores and fissures.

[0101]

[0102] Among them, T iijj and T ijij The tensor representing the relationship between strain and uniform far-field strain within an ellipsoidal embedding is found in Berryman (1980b) and Mavko et al. (2009). Porosity φ is divided into hard porosity φ. s (Hard pores) and soft porosity φ c (Soft porosity). The bulk modulus and shear modulus of the background solid matrix are K, respectively. s and μ s The pore space can be filled with dry or fluid, and the fluid bulk modulus is K. f The aspect ratio of the fracture is α, and the volume ratio of the fracture is... This parameter is related to the properties of soft and hard pores.

[0103] 5. Using formula (5-6), the equivalent elastic modulus of saturated fluid rock can be calculated. Combined with the rock's bulk density ρ, the longitudinal wave velocity V of the saturated rock can then be calculated. p and transverse wave velocity V s A comparative analysis was conducted by combining the longitudinal and transverse wave velocities from well logging data with the predicted velocities:

[0104]

[0105]

[0106] Figure 3 For well logging data of well A, Figure 4 The logging data for Well A is from the Sichuan Basin, primarily the Longmaxi Formation, with a target depth range of 3700-3730 meters. The logging curves include gamma ray curves, P-wave and S-wave velocities, density, porosity, and mineral composition information.

[0107] Figure 5 and Figure 6 This is a comparison chart of the velocities predicted by the constructed rock physics model and the velocities of the target layer in Well A. The organic matter pores are set to 40% of the total porosity, the fracture content is 1.5% of the total porosity, and the fracture aspect ratio ranges from 0.0001 to 0.0004. The blue curves are the P-wave and S-wave velocities predicted by the rock physics model, and the black curves are the velocity curves measured in Well A. It can be seen from the figure that the velocity predicted by the model is highly consistent with the measured data, and the overall trend is basically the same.

[0108] The above method utilizes well logging data, comprehensively considers factors such as porosity and mineral composition, and combines SAC, DEM and EIAS equivalent medium theory models to construct a rock physics model suitable for the target area. Finally, the model prediction results show good consistency with the actual well logging shear wave velocity, verifying the accuracy and reliability of the model.

[0109] Example 2

[0110] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.

[0111] This embodiment provides a shale reservoir P-wave and S-wave prediction device based on well logging data to establish a rock physical model, characterized in that it includes:

[0112] The analysis module is used to analyze the mineral composition of shale logging cores, determine the relationship between porosity and mineral composition, as well as the relationship between porosity and velocity, and classify porosity into three types: organic pores, inorganic pores, and fractures.

[0113] The modeling module is used to take the parameters of organic pores, inorganic pores and fractures as input parameters of the model, and establish a shale rock physics model based on the differential equivalent medium theory model, the self-compatible approximation theory model and the equivalent embedded stress averaging theory model.

[0114] The calculation module is used to estimate the longitudinal and transverse wave velocities of shale based on the shale rock physics model.

[0115] Furthermore, the above modeling module includes the following sub-modules:

[0116] The first submodule is used to add spherical organic matter pores with a fixed aspect ratio into the organic matter based on the differential equivalent medium DEM theoretical model, so as to obtain the bulk modulus and shear modulus of the porous organic matter.

[0117] The second submodule is used to add porous organic matter with a fixed aspect ratio to quartz minerals based on the differential equivalent medium DEM theoretical model to obtain the bulk modulus and shear modulus of the first mixed mineral.

[0118] The third submodule is used to mix the mineral to be mixed with the first mixed mineral obtained from the second submodule based on the self-compatible approximation SCA theoretical model, and to obtain the bulk modulus and shear modulus of the second mixed mineral.

[0119] The fourth submodule is used to mix fluids, second mixed minerals, and inorganic pores and fractures based on the EIAS theoretical model of equivalent embedded stress to obtain the bulk modulus and shear modulus of saturated fluid shale, thereby obtaining the shale petrophysical model.

[0120] Example 3

[0121] This embodiment provides a computer-readable medium storing a computer program that, when executed by a processor, implements the various steps of a shale reservoir P-wave and S-wave prediction method based on well logging data to establish a rock physical model, as described in the above embodiment.

[0122] It should be noted that all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Of course, there are other readable storage media, such as quantum memories, graphene memories, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0123] Example 4

[0124] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 7 As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0125] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only line segments are used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0126] A memory is used to store programs. Specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into main memory and then runs it. The processor executes the program stored in the memory to perform all the steps in the aforementioned method for predicting P-waves and S-waves in shale reservoirs based on well logging data and establishing a rock physical model.

[0127] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the above electronic devices and other devices.

[0128] A bus, including hardware, software, or both, is used to couple the aforementioned components together. For example, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. Although specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0129] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0130] The memory may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, the memory may include removable or non-removable (or fixed) media. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where suitable, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0131] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0132] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0133] The apparatus, device, system, module, or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0134] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual devices or terminal products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0135] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0139] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0140] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for predicting P-wave and S-wave patterns in shale reservoirs, characterized in that, Includes the following steps: S100 analyzes the mineral composition of shale logging cores, determines the relationship between porosity and mineral composition, as well as the relationship between porosity and velocity, and classifies porosity into three types: organic pores, inorganic pores, and fractures. S200 uses the parameters of organic pores, inorganic pores and fractures as input parameters of the model, and establishes a shale petrophysical model based on the differential equivalent medium theory model, the self-compatible approximation theory model and the equivalent embedded stress averaging theory model. S300, Estimate the longitudinal and transverse wave velocities of shale based on the shale rock physics model; Step S200 includes the following steps: S210, based on the differential equivalent medium DEM theoretical model, adds spherical organic matter pores with a fixed aspect ratio into the organic matter to obtain the bulk modulus and shear modulus of porous organic matter. S220, based on the differential equivalent medium DEM theoretical model, adds porous organic matter with a fixed aspect ratio to quartz minerals to obtain the bulk modulus and shear modulus of the first mixed mineral; S230, based on the self-compatible approximation SCA theoretical model, the mineral to be mixed and the first mixed mineral obtained in step S220 are mixed to obtain the bulk modulus and shear modulus of the second mixed mineral; S240, based on the EIAS theoretical model of equivalent embedded stress, mixes fluid, second mixed mineral, and inorganic pores and fractures to obtain the bulk modulus and shear modulus of saturated fluid shale, thereby obtaining the shale petrophysical model.

2. The shale reservoir P-wave and S-wave prediction method as described in claim 1, characterized in that, In step S230, the minerals to be mixed include feldspar minerals, carbonate rock minerals, and clay minerals.

3. The shale reservoir P-wave and S-wave prediction method as described in claim 1, characterized in that, The method further includes the following steps: S400 compares the estimated P-wave and S-wave velocity values ​​with the actual P-wave and S-wave velocity values ​​from the well logging.

4. The shale reservoir P-wave and S-wave prediction method as described in claim 1, characterized in that, Step S300 includes the following steps: Based on the shale rock physics model, the equivalent elastic modulus of saturated fluid shale is calculated; Estimate the P-wave and S-wave velocities of shale based on the equivalent elastic modulus of saturated fluid shale and the bulk density of shale.

5. The shale reservoir P-wave and S-wave prediction method as described in claim 1, characterized in that, In step S100, the mineral composition of shale logging cores is analyzed using well logging data.

6. A shale reservoir P-wave and S-wave prediction device, characterized in that, include: The analysis module is used to analyze the mineral composition of shale logging cores, determine the relationship between porosity and mineral composition, as well as the relationship between porosity and velocity, and classify porosity into three types: organic pores, inorganic pores, and fractures. The modeling module is used to take the parameters of organic pores, inorganic pores and fractures as input parameters of the model, and establish a shale rock physics model based on the differential equivalent medium theory model, the self-compatible approximation theory model and the equivalent embedded stress averaging theory model. The calculation module is used to estimate the P-wave and S-wave velocities of shale based on the shale rock physics model; The modeling module includes: The first module is used to add spherical organic matter pores with a fixed aspect ratio into the organic matter based on the differential equivalent medium DEM theoretical model, so as to obtain the bulk modulus and shear modulus of the porous organic matter. The second module is used to add porous organic matter with a fixed aspect ratio to quartz minerals based on the differential equivalent medium DEM theoretical model, and obtain the bulk modulus and shear modulus of the first mixed mineral. The third module is used to mix the mineral to be mixed with the first mixed mineral obtained in step S220 based on the self-compatible approximation SCA theoretical model to obtain the bulk modulus and shear modulus of the second mixed mineral. The fourth module is used to mix fluids, second mixed minerals, and inorganic pores and fractures based on the EIAS theoretical model of equivalent embedded stress to obtain the bulk modulus and shear modulus of saturated fluid shale, thereby obtaining the shale petrophysical model.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements a method for predicting P-waves and S-waves in shale reservoirs as described in any one of claims 1 to 5.

8. An electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement a shale reservoir P-wave and S-wave prediction method as described in any one of claims 1 to 5.

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