A method and system for simulating dynamic stress-strain elasticity of continental shale oil across scales

Abstract

The application discloses a kind of terrestrial shale oil cross-scale dynamic stress-strain elastic simulation method and system, the elastic simulation method includes: constructing multiple lamination type digital rock physics model;Simulate dynamic stress-strain value;Characterize the elastic anisotropic response characteristics;Based on the lamination type identification of well logging, seismic data.More accurately simulate the elastic behavior of rock under different scales, and provide an important tool and perspective for cross-scale rock physics research.

Description

Technical Field

[0001] This invention relates to the field of rock physical analysis in oil and gas exploration, and in particular to a method and system for cross-scale dynamic stress-strain elastic simulation of continental shale oil. Background Technology

[0002] Laminar structures are the most prevalent structural feature in shale. Although core thin sections are considered the most direct means of laminar structure identification, their practical application is limited by high costs and the inability to obtain cores of all target layers in every well. In recent years, many scholars have attempted to continuously identify and classify laminar structures vertically in a single well using well logging data. Microresistivity imaging logging, due to its high vertical resolution, has become a commonly used tool for identifying laminar sedimentary structures. The proposed image slicing technique has further improved the accuracy of imaging logging in identifying laminar structures. However, this method has strong scale limitations and high costs. Resistivity imaging logging images are usually unavailable in conventional logging data, so some scholars have attempted to identify laminar structures based on conventional logging data, such as using GR logging curves, sonic transit time, and density curves. High-frequency and low-frequency laminar structures are distinguished by analyzing the separation degree between sonic and density curves. In summary, current methods for identifying laminar structures are mainly based on image recognition, making it difficult to comprehensively and intuitively analyze the elastic response characteristics of laminar structures, and accurately identifying laminar structure types based on conventional logging and seismic data still faces significant challenges. With the continuous development of image processing and numerical simulation technologies, digital rock physics (DRP) has become an important tool for studying rock physical properties, overcoming many shortcomings of traditional rock physics experiments. It has made significant progress in studying the physical properties of both conventional and special-structure rocks. Conventional DRP mainly constructs digital cores through physical experiments and numerical reconstruction, and is widely used in the construction of digital cores for unconventional oil and gas reservoirs such as shale and tight sandstone. For cores with special structures, digital cores containing fractures and layered structures have been constructed using the stacking method and an improved process method, respectively. Multi-scale, multi-component digital cores of sandstone have been constructed based on multi-resolution image fusion technology. However, current DRP technologies cannot accurately characterize the complex lithological and structural heterogeneity of shale. Multi-scale fusion methods still have significant scale limitations, mainly confined to the core scale. Furthermore, current technologies have not yet adequately described the elastic anisotropy characteristics of shale reservoirs, resulting in unclear elastic response mechanisms in different reservoirs. Summary of the Invention

[0003] In view of the above problems, the present invention is proposed to provide a method and system for cross-scale dynamic stress-strain elastic simulation of continental shale oil to overcome or at least partially solve the above problems.

[0004] According to one aspect of the present invention, a multi-scale dynamic stress-strain elastic simulation method for continental shale oil is provided, the elastic simulation method comprising:

[0005] Constructing digital rock physical models with multiple lamination types;

[0006] Simulate dynamic stress-strain values;

[0007] Characterizing the elastic anisotropic response features;

[0008] Layer type identification based on well logging and seismic data.

[0009] Optionally, the construction of digital rock physical models with multiple lamination types specifically includes:

[0010] The construction of the cross-scale laminar model starts from the microscale and is based on actual scanned electron microscope images;

[0011] Four lithological microscale lamination models were constructed as four REV models for the next scale;

[0012] Optionally, the construction of digital rock physics models with multiple lamination types also includes: constructing two types of lamination structure models considering the distribution law of lamination thickness and the law of lithofacies combination.

[0013] Optionally, the four lithological microscale lamination models specifically include:

[0014] Microscale lamination models of organic matter, clay, quartz, and calcite.

[0015] Optionally, the two types of laminar structures specifically include: laminar and blocky.

[0016] Optionally, the thickness distribution patterns of the various texture types include:

[0017] Layered structures correspond to millimeter-level layered structures, meaning that the thickness of a single layer is less than 1 cm.

[0018] Blocky structures correspond to single layers that are very thick or whose layers are almost indistinguishable.

[0019] The digital rock model is set to a size of 200*200 pixels. In the vertical direction, the model is composed of layers of different thicknesses stacked together. Each layer is a transversely isotropic medium, and the size of each layer is the layer thickness * 200 pixels.

[0020] Randomly generate models that conform to different laminar structure types, and set the thickness distribution of each layer to follow a power-law distribution. The formula for the layer thickness distribution of the laminar model is as follows:

[0021] F=0.015+1.322 exp(-1.6548H), (1)

[0022] The formula for thickness distribution in a block model is as follows:

[0023] F=0.015+0.0002 exp(0.8411H), (2)

[0024] Where F represents the probability density distribution and H represents the relative thickness.

[0025] Optionally, the lithofacies distribution patterns of the various lamellar types include:

[0026] In the digital rock physics model, each pixel is a number, and the same number represents the same phase. Since each individual layer model is homogeneous, the same layer model is assigned the same number.

[0027] The model has four phases.

[0028] To make the lithofacies distribution of the laminated model more consistent with actual geological conditions, Gaussian distribution law is used to control the lithofacies distribution of the laminated model and the blocky model;

[0029] Based on actual geological data, lamellar structures are often rich in organic lamellars, while massive structures have a higher content of argillaceous or muddy matter. The probability density distribution of the two types of lamellar structures is shown in the following formula:

[0030]

[0031] Where μ represents the mean of the logarithmic values ​​and σ represents the standard deviation of the logarithmic values;

[0032] In the layered digital rock model, μ = 0.3, σ = 1; in the massive digital rock physical model, μ = 2.7, σ = 1.

[0033] Following the pattern, using random number seeds, 200 sets of lamellar and massive digital rock physics models with different lithofacies distributions and different lamellar thicknesses were constructed.

[0034] Optionally, the model is configured with four phases, specifically including: organic-rich lamellar units, clay-rich lamellar units, calcite lamellar units, and dolomite lamellar units.

[0035] Optionally, the features characterizing the elastic anisotropic response specifically include:

[0036] Dynamic stress-strain simulations were performed on multiple batches of blocky and lamellar models to obtain the velocity and anisotropic distribution values ​​of the two types of lamellar structure digital models.

[0037] Optionally, the layer type identification based on well logging and seismic data specifically includes:

[0038] Joint simulation of velocity-anisotropy values ​​clarifies the elastic anisotropic response mechanism of various laminar structures and identifies laminar types.

[0039] This invention also provides a multi-scale dynamic stress-strain elastic simulation system for continental shale oil, which applies the above-described multi-scale dynamic stress-strain elastic simulation method for continental shale oil. The simulation system includes:

[0040] The digital rock physics model building module is used to construct digital rock physics models with various lamination types.

[0041] The stress-strain numerical simulation module is used to simulate dynamic stress-strain values.

[0042] The anisotropic response feature characterization module is used to characterize the elastic anisotropic response features;

[0043] The laminar type identification module is used for laminar type identification based on well logging and seismic data.

[0044] This invention provides a method and system for cross-scale dynamic stress-strain elastic simulation of continental shale oil. The elastic simulation method includes: constructing digital rock physics models with multiple lamination types; simulating dynamic stress-strain values; characterizing elastic anisotropic response features; and identifying lamination types based on well logging and seismic data. This method more accurately simulates the elastic behavior of rocks at different scales, providing an important tool and perspective for cross-scale rock physics research.

[0045] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0047] Figure 1 A flowchart of a multi-scale dynamic stress-strain elastic simulation method for continental shale oil provided in this embodiment of the invention;

[0048] Figure 2 This is a schematic diagram of homogenization of a multi-scale texture model provided in an embodiment of the present invention, with a small-scale texture model used as a REV schematic diagram.

[0049] Figure 3 Schematic diagrams of four digital rock physics models at different lithological microscales provided in this embodiment of the invention;

[0050] Figure 4 This is a schematic diagram of the probability density distribution of layer thickness for different texture structure types provided in embodiments of the present invention;

[0051] Figure 5 This is a schematic diagram illustrating the different lithofacies distribution patterns of the laminated model and the blocky model provided in the embodiments of the present invention;

[0052] Figure 6 Schematic diagrams of a layered digital rock physics model (left) and a blocky digital rock physics model (right) that take into account the distribution of lithofacies and thickness distribution, provided for embodiments of the present invention;

[0053] Figure 7 The diagram shows the a) P-wave velocity, b) S-wave velocity, c) P-wave anisotropy value ε, and d) S-wave anisotropy γ obtained from simulations based on the constructed layered and blocky digital rock physics models provided in this embodiment of the invention. Detailed Implementation

[0054] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0055] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0056] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0057] This invention employs digital rock models to construct digital rock physical models with different lithological characteristics and different lamination structures. By simulating the elastic anisotropic response characteristics of different lamination structures using the dynamic stress-strain method, it provides technical support for identifying shale oil reservoir lamination types from well logging and seismic data. The method of this invention offers a new approach to more effectively understand and utilize the geological characteristics of shale lamination structures.

[0058] like Figure 1 As shown, a method for identifying shale oil reservoir lamination types based on digital rock physical elastic simulation includes:

[0059] Construct digital rock physics models with various lamination types; simulate dynamic stress and strain values; characterize elastic anisotropic response features; and identify lamination types based on well logging and seismic data.

[0060] (1) A method for simulating dynamic stress and strain in cross-scale layered shale oil

[0061] To characterize the multi-scale rock physical response mechanism of shale reservoirs, this invention proposes a method for progressively upgrading and constructing a multi-scale digital rock physical model, and for characterizing the elastic anisotropic response mechanism of the multi-scale laminar model based on the dynamic stress-strain method.

[0062] Homogenization methods are effective for constructing multi-scale models, and REV (Realistic Reflection and Variation) is one of the most common homogenization methods. Starting from the microscale, digital rock physics models of different lithologies are constructed. Microscale laminar models of different lithologies are used as REVs for core-scale laminar models. Core-scale laminar models of different lithologies are constructed according to the lithofacies assemblage and thickness distribution patterns at the core scale. The elastic input of the core-scale laminar model is determined by the dynamic stress-strain simulation results of the microscale laminar model, and so on, the elastic input of the next scale depends on the dynamic stress-strain simulation results of the previous scale laminar model. Similarly, core-scale laminar models of different lithologies are used as REVs for the next scale, and well-logging-scale laminar models of different lithologies are constructed according to the lithofacies assemblage and thickness distribution patterns at the well-logging scale. By progressively increasing the scale from small to large, the equivalent elastic response characteristics of the reservoir are systematically constructed, such as... Figure 2 As shown.

[0063] (2) Construct digital rock physical models for different lamination types; the original first step is divided into two parts (1) and (2), and the specific model building methods are supplemented in detail.

[0064] The construction of the cross-scale lamination model starts from the microscale. Based on actual scanning electron microscope images, four microscale lamination models of different lithologies are constructed, such as... Figure 3 As shown, there are four REV models for the next scale.

[0065] In order to construct digital rock physics models of layered shale with different sedimentary structures and lithological characteristics, this invention proposes for the first time to quantitatively consider the distribution law of laminar thickness and the law of lithofacies combination to construct two types of laminar structure models: laminar and massive.

[0066] a) Thickness distribution patterns of different texture types:

[0067] Laminated structures correspond to millimeter-level laminar structures, meaning a single laminar layer is less than 1 cm thick. Blocky structures, on the other hand, correspond to single layers with very large thicknesses or layers that are almost indistinguishable. Therefore, the layer thickness distribution patterns for laminated and blocky models are inconsistent. The digital rock model is set to a size of 200*200 pixels. Vertically, the model is composed of stacked layers of varying thicknesses. Each laminar layer is a laterally isotropic medium, therefore the size of each layer is layer thickness * 200 pixels. To randomly generate models that conform to different laminar structure types, the thickness distribution of each layer is set to follow a power-law distribution. The formula for the layer thickness distribution of the laminated model is as follows:

[0068] F=0.015+1.322 exp(-1.6548H),(1)

[0069] The formula for thickness distribution in a block model is as follows:

[0070] F=0.015+0.0002 exp(0.8411H),(2)

[0071] Where F represents the probability density distribution and H represents the relative thickness.

[0072] like Figure 4 As shown, the thin-layer thickness distribution probability density is high in the lamellar model, while the thick-layer thickness distribution probability density is high in the block model.

[0073] b) Distribution patterns of lithofacies with different lamination types

[0074] Each pixel in the digital rock physics model is a number, with the same number representing the same facies. Since each individual layer model is homogeneous, the same layer model is assigned the same number. The model includes four facies: organic-rich lamellar units, clay-rich lamellar units, calcite lamellar units, and dolomite lamellar units. To ensure the facies distribution in the lamellar models more closely reflects actual geological conditions, a Gaussian distribution law is used to control the facies distribution in the lamellar and massive models. Statistical analysis of actual geological data shows that lamellar structures are often rich in organic matter, while massive structures have higher argillaceous or muddy content. The probability density distribution of the two types of lamellar structures is as follows: Figure 4 As shown, the formula is as follows:

[0075]

[0076] Where μ represents the mean of the logarithmic values ​​and σ represents the standard deviation of the logarithmic values. In the layered digital rock model, μ = 0.3 and σ = 1; in the massive digital rock physical model, μ = 2.7 and σ = 1.

[0077] Based on these two principles, 200 sets of digital rock physics models with different lithofacies distributions and different laminar thicknesses were constructed using random number seeds.

[0078] like Figure 5 The diagram shows the distribution patterns of different lithofacies in the lamellar and massive models.

[0079] Elastic characteristic simulation of digital rock physics model based on dynamic stress-strain method includes:

[0080] The specific steps are supplemented. Simply put, simulating longitudinal and transverse waves requires setting different boundary conditions. Both the wave equation and the two-dimensional motion equation are processes of setting boundary conditions. Formula (11) is used to calculate longitudinal and transverse waves using the boundary conditions calculated by the aforementioned formulas (4)-(10).

[0081] Considering the strong heterogeneity of shale, the dynamic stress-strain method was chosen to perform elastic simulations on the digital rock physical model. The boundary conditions of the digital rock model determine the simulation direction; different boundary conditions were set for simulating P-waves and S-waves in the two-dimensional digital rock model. When simulating P-waves, the right edge of the model is compressed or stretched along the y-direction, its opposite edge is fixed, and periodic boundary conditions are applied to the other two edges. When simulating S-waves, the right edge of the model is dragged along the x-direction, while its opposite edge is fixed, and periodic boundary conditions are applied to the other two edges.

[0082] Wave equation:

[0083] The solid matrix is ​​considered to be homogeneous, isotropic, and linearly elastic.

[0084] The two-dimensional constitutive relation of solids is:

[0085] σ ij =λe kk δ ij +2μe ij ,i,j,k=1,2. (4)

[0086] In the formula, λ and μ are Lamé constants, and σ ij Let e ​​be the stress tensor. kk and e ij For the strain tensor, δ ij It is a Kronek function.

[0087] Assuming the pore fluid is a uniform Newtonian fluid, the constitutive relation of a two-dimensional Newtonian fluid is:

[0088]

[0089] Where, η λ and η μ Viscosity, and Let λ be the strain rate, λ be the Lamé constant, and δ be the strain rate. ij It is a Kronek function.

[0090] The two-dimensional equation of motion is:

[0091]

[0092] in, ρ is the second time derivative of the displacement; g is the volume density; ρ is the second time derivative of the displacement; ρ is the volume density; g is the second time derivative of the displacement. ij It refers to physical strength, here g i =0.

[0093] With the model boundaries fixed, a wave perpendicular to the bedding direction is applied to force the model to vibrate. The wave equations on the digital rock are then solved using the rotated staggered mesh finite difference method. This numerical method is stable in wave propagation simulations on highly heterogeneous digital rocks.

[0094] After simulating the wave field on digital rock using the finite difference method, the average stress and strain curves of the longitudinal wave simulation along the x-direction are calculated using the following formula:

[0095]

[0096] In the formula, σ xx (t) and ε xx (t) represents the average normal stress and strain of the entire digital rock, respectively; , i and j represent the normal stress and strain of each grid, respectively; i and j represent the indices of the grid center in the x and y directions, respectively; and <·> is the volume average operator.

[0097] The average stress-strain curve along the x-direction in transverse wave simulation is calculated using the formula:

[0098]

[0099] σ xy (t) and ε xx (t) represents the average shear stress and strain of the entire digital rock, respectively; and These represent the shear stress and strain for each grid cell.

[0100] Finally, an average strain curve and an average stress curve are obtained, and the strain rate and stress rate are calculated after the simulation is completed.

[0101] The complex P-wave and S-wave moduli of digital rock cores are the ratios of the Fourier transforms of stress rate and strain rate, while the P-wave and S-wave velocities are calculated from the complex P-wave and S-wave moduli and density, as shown in the following formulas:

[0102] The stress-strain rate curve is transformed to the frequency domain using Fourier transform:

[0103]

[0104]

[0105] σ xx and ε xx σ represents the average normal stress and normal strain in the x-direction of the digital rock core. xy and ε xy This represents the average shear stress and strain of the digital rock core. and These represent normal stress rate and normal strain rate, respectively. and ...

[0106] The complex longitudinal wave modulus M(ω) and the complex longitudinal wave modulus G(ω) are calculated by the following formula:

[0107]

[0108] Longitudinal wave velocity V P With transverse wave velocity V s Calculated by the following formula:

[0109]

[0110] Joint velocity-anisotropic feature identification of different laminar structures;

[0111] The name of this step has been modified, and a formula for identifying different lamellar structures has been added. The aforementioned third step calculates the P-wave and S-wave velocities. Based on this method, the P-wave and S-wave velocities perpendicular to and parallel to the bedding are calculated respectively, and the specific anisotropy values ​​are obtained based on formula (12). The range of anisotropy values ​​for different lamellar structures is different, so different lamellar structures can be identified by the magnitude of this value.

[0112] Because shale exhibits VTI anisotropy, the P and S wave velocities perpendicular to the bedding planes can be obtained using formula (11). By rotating the digital rock physics model by 90° and setting the same boundary conditions, the P and S wave velocities parallel to the bedding planes can be obtained by following the steps described above. The ratio of the velocity difference between the parallel and perpendicular bedding plane directions to the velocity perpendicular to the bedding planes is the anisotropy value of the P and S wave velocities. The formula is as follows:

[0113]

[0114] Dynamic stress-strain simulations were performed on the multiple sets of blocky and lamellar models generated in step 3 to obtain the velocity and anisotropy distribution values ​​of the two types of lamellar structure digital models. The joint simulation of velocity-anisotropy values ​​clarified the elastic anisotropic response mechanism of different lamellar structures.

[0115] According to (1), 100 sets of digital rock physics models for lamellar and layered rock were constructed, each considering the distribution patterns of lithofacies and thickness. One set of models is as follows: Figure 6 As shown.

[0116] Based on (2), the P-wave and S-wave velocities in the parallel and perpendicular bedding directions of 200 sets of digital rock physics models were calculated using the dynamic stress-strain method. The anisotropy values ​​were further obtained, and the simulation results are as follows: Figure 7 As shown, the lamellar model is dominated by organic lamellar units, while the massive model is dominated by calcite lamellar units. Therefore, the lamellar model has a lower velocity than the massive model. Furthermore, lamellar models exhibit stronger heterogeneity and higher anisotropy values. This provides valuable technical guidance for identifying lamellar types from conventional well logging and seismic data.

[0117] Beneficial effects:

[0118] First, the proposed cross-scale digital rock elasticity simulation utilizes a multi-scale homogenization method, using a small-scale laminar model as the REV of the next-scale laminar model, and gradually constructs a large-scale laminar model. This breaks through the scale limitations of traditional digital rock physics simulation, and more accurately simulates the elastic behavior of rocks at different scales, providing an important tool and perspective for cross-scale rock physics research.

[0119] Secondly, overcoming the limitations of traditional methods that rely on the scarcity and difficulty in obtaining samples of different laminar types, the proposed cross-scale digital rock construction method for laminar shale oil quantifies the distribution patterns of laminar thickness and lithofacies assemblages to generate large-scale laminar models with different lithological characteristics and sedimentary structures that conform to geological laws. This expands the research sample library and is more conducive to revealing the rock physical response mechanisms of different laminar types.

[0120] Third, based on digital rock physics quantification, the elastic anisotropic response mechanism of different lamination types was established, and a combined P-wave and S-wave elastic-anisotropic shale lamination identification method was proposed. Compared with massive structures, lamination structures exhibit higher anisotropy and lower velocities. This has significant guiding implications for identifying lamination types from well logging and seismic data.

[0121] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are 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 should be included within the scope of protection of the present invention.

Claims

1. A method for multi-scale dynamic stress-strain elastic simulation of continental shale oil, characterized in that, The elasticity simulation method includes: Constructing digital rock physical models with multiple lamination types; Simulate dynamic stress-strain values; Characterizing the elastic anisotropic response features; Layer type identification based on well logging and seismic data.

2. The method for multi-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 1, characterized in that, The construction of digital rock physics models with multiple texture types specifically includes: The construction of the cross-scale laminar model starts from the microscale and is based on actual scanned electron microscope images; Four lithological microscale lamination models were constructed as four REV models for the next scale.

3. The method for multi-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 1, characterized in that, The construction of digital rock physics models with multiple lamination types also includes: constructing two types of lamination structure models considering the distribution law of lamination thickness and the law of rock facies combination.

4. The method for multi-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 2, characterized in that, The four lithological microscale lamination models specifically include: Microscale lamination models of organic matter, clay, quartz, and calcite.

5. The method for cross-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 3, characterized in that, The two types of laminar structures specifically include laminar and blocky structures.

6. The method for multi-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 1, characterized in that, The thickness distribution patterns of the various texture types include: Layered structures correspond to millimeter-level layered structures, meaning that the thickness of a single layer is less than 1 cm. Blocky structures correspond to single layers that are very thick or whose layers are almost indistinguishable. The digital rock model is set to a size of 200*200 pixels. In the vertical direction, the model is composed of layers of different thicknesses stacked together. Each layer is a transversely isotropic medium, and the size of each layer is the layer thickness * 200 pixels. Randomly generate models that conform to different laminar structure types, and set the thickness distribution of each layer to follow a power-law distribution. The formula for the layer thickness distribution of the laminar model is as follows: F=0.015+1.322 exp(-1.6548H), (1) The formula for thickness distribution in a block model is as follows: F=0.015+0.0002 exp(0.8411H), (2) Where F represents the probability density distribution and H represents the relative thickness.

7. The method for cross-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 1, characterized in that, The distribution patterns of the various laminar types include: In the digital rock physics model, each pixel is a number, and the same number represents the same phase. Since each individual layer model is homogeneous, the same layer model is assigned the same number. The model has four phases. To make the lithofacies distribution of the laminated model more consistent with actual geological conditions, Gaussian distribution law is used to control the lithofacies distribution of the laminated model and the blocky model; Based on actual geological data, lamellar structures are often rich in organic lamellars, while massive structures have a higher content of argillaceous or muddy matter. The probability density distribution of the two types of lamellar structures is shown in the following formula: Where μ represents the mean of the logarithmic values ​​and σ represents the standard deviation of the logarithmic values; In the layered digital rock model, μ = 0.3, σ = 1; in the massive digital rock physical model, μ = 2.7, σ = 1. Following the pattern, using random number seeds, 200 sets of lamellar and massive digital rock physics models with different lithofacies distributions and different lamellar thicknesses were constructed.

8. The method for multi-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 7, characterized in that, The model has four phases, specifically including: organic-rich lamellar units, clay-rich lamellar units, calcite lamellar units, and dolomite lamellar units.

9. The method for multi-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 1, characterized in that, The specific features describing the elastic anisotropic response include: Dynamic stress-strain simulations were performed on multiple batches of blocky and lamellar models to obtain the velocity and anisotropic distribution values ​​of the two types of lamellar structure digital models.

10. The method for multi-scale dynamic stress-strain elastic simulation of continental shale oil according to claim 1, characterized in that, The laminar type identification based on well logging and seismic data specifically includes: Joint simulation of velocity-anisotropy values ​​clarifies the elastic anisotropic response mechanism of various laminar structures and identifies laminar types.

11. A multi-scale dynamic stress-strain elastic simulation system for continental shale oil, employing the multi-scale dynamic stress-strain elastic simulation method for continental shale oil as described in any one of claims 1-10, characterized in that, The simulation system includes: The digital rock physics model building module is used to construct digital rock physics models with various lamination types. The stress-strain numerical simulation module is used to simulate dynamic stress-strain values. The anisotropic response feature characterization module is used to characterize the elastic anisotropic response features; The laminar type identification module is used for laminar type identification based on well logging and seismic data.

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