Method for quantifying seepage contribution degree of shale oil reservoir lamellar fracture and matrix pore

Abstract

The invention provides a shale oil reservoir lamellar fracture and matrix pore seepage contribution degree quantification method, and belongs to the technical field of oil-gas field development, and the method comprises the following steps: carrying out micron-scale and nano-scale computed tomography on the same shale core sample, and carrying out image fusion and three-dimensional reconstruction to obtain the permeability contribution degree of the shale oil reservoir lamellar fracture and matrix pore seepage contribution degree. Constructing a three-dimensional digital rock core model containing matrix pores and lamellar fracture structures; and based on the three-dimensional digital core model, calculating the absolute permeability in an original state, a state of only retaining pores and a state of only retaining cracks through numerical simulation, and quantitatively calculating the contribution ratio of a crack system and a matrix pore system to the total seepage capacity according to the absolute permeability. According to the shale oil reservoir lamellar fracture and matrix pore seepage contribution degree quantification method, the technical problems that in a traditional method, due to fuzzy quantification, the reservoir assessment deviation is large, and a development scheme lacks a scientific optimization basis are solved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a method for quantifying the contribution of shale oil reservoir fractures and matrix pores to seepage. Background Technology

[0002] In the complex and sophisticated field of oil and gas field development, the development and efficient utilization of shale oil reservoirs have always held a pivotal position. Shale oil, as an unconventional oil and gas resource, exhibits significantly different reservoir characteristics compared to conventional oil and gas reservoirs, particularly in its internal foliation fractures and matrix pore system, which constitute unique channels and spaces for oil and gas migration and accumulation. However, it is precisely these subtle and complex structural features that pose a significant challenge to the study of the seepage mechanisms of shale oil reservoirs.

[0003] Specifically, shale fractures, as a natural fracture system in shale oil reservoirs, directly influence the permeability of oil and gas due to their distribution, morphology, and connectivity. Meanwhile, matrix pores, as the primary site for oil and gas storage, also play a crucial role in oil and gas migration and production due to their pore structure, size, and distribution. In traditional oil and gas field development, due to technological limitations, researchers often struggle to accurately distinguish and quantify the specific contributions of shale fractures and matrix pores to oil and gas permeability. This quantification ambiguity not only leads to significant deviations in the assessment of reservoir permeability but also further restricts the optimization and adjustment of oil and gas development plans, impacting overall development efficiency. More seriously, as oil and gas field development deepens, easily exploitable oil and gas resources gradually decrease, and development difficulties increase daily. Under these circumstances, how to more scientifically and accurately assess the permeability of shale oil reservoirs becomes key to improving oil and gas recovery rates and achieving efficient resource utilization.

[0004] Therefore, developing a method that can accurately quantify the contribution of shale fractures and matrix pores to seepage in shale oil reservoirs is not only a significant breakthrough for existing technologies, but also a crucial step in advancing oil and gas field development technologies to a higher level. Summary of the Invention

[0005] The purpose of this invention is to provide a quantitative method for measuring the contribution of shale oil reservoir fractures and matrix pores to seepage, which solves the technical problems of large reservoir assessment deviations and lack of scientific optimization basis for development schemes caused by the fuzzy quantification of traditional methods.

[0006] To achieve the above objectives, this invention provides a method for quantifying the contribution of shale oil reservoir fractures and matrix pore flow, comprising the following steps: S100. Prepare different shale cores and experimental setups, select representative cores, prepare standard plunger samples, and scan the same core sample sequentially using micron-CT and nano-CT systems to obtain high-definition three-dimensional images of the core as a whole and its nanopores. S200 integrates the three-dimensional high-definition image data obtained from S100 to establish a database covering complex matrix structures from micron-level pores to nano-level matrices; S300 uses grayscale thresholding and machine learning algorithms to accurately segment CT images to distinguish pores, organic matter, cracks and different mineral components. It uses multi-scale image registration technology to construct a unified digital core model and enhances image quality through anisotropic filtering and histogram equalization. S400. The preprocessed two-dimensional CT image sequence is aligned and superimposed layer by layer according to spatial coordinates to construct a three-dimensional voxel matrix with regular grid as the core data volume of digital core. The Marching Cubes algorithm is used to extract the isosurfaces of rock pores and mineral components to generate a high-fidelity three-dimensional digital core model. S500: Compare the real rock database with the reconstructed digital core model to verify the authenticity of the structure. If the verification is consistent, conduct seepage simulation and contribution analysis. If they are inconsistent, return to step S400 for iterative optimization until the model and the real core structure are completely consistent. S600. Perform routine porosity and permeability tests on core samples to obtain experimental data. Use the lattice Boltzmann method to simulate single-phase fluid flow on the validated digital core model. Use image difference calculation to determine whether the gas intrusion area preferentially occupies the foliation fractures or matrix pores. S700: The total absolute permeability is calculated using the original digital core model. The permeability of the system containing only pores and the permeability of the system containing only fractures are obtained by virtually filling the fractures and virtually smoothing the pores, respectively. Finally, the proportion of seepage contribution from fractures and pores is quantitatively calculated based on the simulation results of the three.

[0007] Preferably, in S300, Gaussian filtering and grayscale processing are introduced before the precise segmentation of the CT image, and the expression is as follows: ; ; in, The kernel is a Gaussian convolution. For the original image, It is a grayscale image. These are pixel coordinates; The unified digital core model expression is constructed as follows: ; in, For standard images; For spatial changes; It is a similarity measure function; These are the regularization and its weight coefficients, respectively. These are the optimal transformation parameters.

[0008] Preferably, a high-fidelity three-dimensional digital core model is generated in S400, and its expression is: ; in, For pixel endpoints; Linear interpolation; These are the spatial coordinates of the intersection points of the generated isosurface and the voxel edges.

[0009] Preferably, the image difference operation expression in S600 is: ; in, I This is an image after gas injection; This is the baseline image before gas injection.

[0010] Preferably, the expression for the contribution of fractures to the total permeability in S700 is: ; The expression for the contribution of pores to total permeability is: ; in, K 1 Total absolute penetration rate; K 2 The permeability of the porous system; K 3 The permeability of the fracture system.

[0011] Preferably, the three-dimensional digital core model constructed in S400 needs to be verified using conventional porosity and permeability testing or scanning electron microscopy data.

[0012] Therefore, the present invention employs the above-mentioned method for quantifying the contribution of shale oil reservoir fractures and matrix pores to seepage, and the technical effects are as follows: 1. Solving the problem of quantification fuzziness: By constructing a high-precision digital core model and combining it with seepage simulation technology, the scientific quantification of the seepage contribution of both was achieved, solving the core defect of "fuzzy evaluation" in traditional methods.

[0013] 2. Breaking through the bottleneck of complex structure analysis: By adopting the comparative simulation method of "virtual filling of cracks" and "virtual smoothing of pores", the contribution of the two can be directly quantified through the analysis of absolute permeability differences, which breaks through the limitations of traditional methods in the analysis of complex structures.

[0014] 3. Integrating the advantages of multiple disciplines: It combines digital core technology, numerical simulation methods and experimental verification to form an integrated "modeling-simulation-verification" technology system, overcoming the shortcomings of traditional methods and techniques that are limited in scope.

[0015] 4. Enhance the ability to optimize development plans: The quantitative results provided can directly guide key decisions such as fracturing design optimization (e.g., fracture density adjustment) and production well layout optimization (e.g., avoiding low-permeability zones), significantly improving recovery rate and reducing development costs. Attached Figure Description

[0016] Figure 1 This is a schematic diagram illustrating the complete steps of a method for quantifying the contribution of shale oil reservoir fractures and matrix pores to seepage. Figure 2 This is a schematic diagram of the complete process of the method for quantifying the contribution of seepage in shale oil reservoir fractures and matrix pores according to the present invention. Figure 3 This is a schematic diagram illustrating the construction process of a digital core model library for a method to quantify the contribution of shale oil reservoir pore fractures and matrix pores to seepage. Figure 4 This invention provides a method for quantifying the contribution of shale oil reservoir pore fractures and matrix pores to seepage, using digital core absolute permeability simulation. Figure 5 This invention provides a method for quantifying the contribution of shale oil reservoir fractures and matrix pores to seepage, using digital cores to simulate absolute permeability after only "virtually filling" fractures; Figure 6 This invention provides a method for quantifying the contribution of seepage to shale oil reservoir pore fractures and matrix pores. The absolute permeability simulation diagram is obtained by "virtually smoothing" the pores in a digital core. Detailed Implementation

[0017] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0019] Example 1 like Figures 1-2 As shown, this invention provides a quantitative method for measuring the contribution of fractures and matrix pores to seepage in shale oil reservoirs. This method enables non-destructive, accurate, and cross-scale three-dimensional characterization of shale reservoirs, from nanoscale matrix pores to micron-scale fractures, and allows for the construction of high-fidelity digital core models. Innovatively, it quantitatively separates the contribution ratios of the fracture system and the matrix pore system to the total absolute permeability in the digital core model, solving the problem of the inability to separate these two systems in physical experiments.

[0020] The method includes the following steps: Multiscale CT scan of S100 core samples: S110 prepares different shale core samples and seepage simulation experimental devices; S120 selected representative shale cores; S130 was used to prepare standard plunger samples to ensure that the size and shape of the samples met the experimental requirements. S140 sequentially scanned the same core sample using both a micron-based CT system and a nano-based CT system. The micron-based CT scan acquired a three-dimensional image of the entire core; the nano-based CT scan captured high-resolution three-dimensional images of the nanopores.

[0021] S200 constructs a real core database: The S210 system integrates and stores CT scan data and information at the micron and nanoscale. S220 establishes a real core database ranging from micron-level pores to nanoscale matrix complex structures.

[0022] S300 Image Preprocessing: The S310 employs an image segmentation method based on grayscale thresholding and machine learning algorithms to accurately classify each voxel in CT scan images, distinguishing pores, organic matter, fractures, and the matrix of different mineral components. This provides a foundation for subsequent pore network modeling and physical property parameter calculation. The Gaussian filtering and grayscale processing formulas are as follows: ; ; in, The kernel is a Gaussian convolution. For the original image, It is a grayscale image. These are pixel coordinates.

[0023] The S320 precisely registers multi-scale images obtained from micron-CT and nano-CT scans in a spatial coordinate system, ensuring the alignment of their geometric features in three-dimensional space, thereby constructing a unified multi-scale digital core model, as shown in the following formula: ; in, For standard images; For spatial changes; It is a similarity measure function; These are the regularization and its weight coefficients, respectively. These are the optimal transformation parameters.

[0024] The S330 employs algorithms such as anisotropic filtering and histogram equalization to enhance the original CT images, aiming to suppress image noise and improve the contrast and clarity of microcracks and pore boundaries in the target area, laying the foundation for subsequent accurate segmentation and recognition.

[0025] S400 constructs accurate digital core models: S410 takes the preprocessed two-dimensional image sequence and, based on its spatial coordinates and stratigraphic information, precisely aligns and superimposes it layer by layer in the height direction of three-dimensional space, thereby reconstructing a three-dimensional voxel matrix with a regular grid. This matrix is ​​the core data volume of the digital core.

[0026] Based on the aforementioned 3D voxel data, S420 employs the Marching Cubes algorithm. By traversing each voxel and detecting its intersection with a preset threshold, it fits and extracts isosurfaces between rock pores and mineral components in the form of a set of triangular facets, thereby generating a high-fidelity 3D geometric surface model of the rock. The formula is as follows: ; in, For pixel endpoints; Linear interpolation; These are the spatial coordinates of the intersection points of the generated isosurface and the voxel edges.

[0027] S500 accurately characterizes the distribution patterns of multi-scale cracks and pores: S510 compares the real rock database with the reconstructed digital core model to ensure the authenticity and physical reliability of its digital core structure.

[0028] If the comparison results of S520 are consistent, then proceed with subsequent digital core seepage simulation and contribution analysis; if they are inconsistent, repeat S400 to construct an accurate digital core model until the results are consistent.

[0029] S600 Digital Core Seepage Simulation and Seepage Contribution Mechanism: S610 was used to perform routine porosity and permeability tests on core samples to obtain experimental data.

[0030] S620 uses the lattice Boltzmann method to simulate the microscopic seepage of single-phase fluids on a validated digital core model.

[0031] The S630 will display the image after inflation. I Reference image before gas injection Perform image difference operations to obtain the difference image. ΔI That is, the gas intrusion area: ; Based on the geometry, spatial location, and CT value variation of the gas intrusion area, S640 accurately determines whether the gas preferentially occupies continuous, large-scale lamellar fractures or dispersed, small-scale matrix pores.

[0032] S700 quantitatively evaluates the contribution of cracks and porosity: S710 is based on the original digital core model, which includes pores and fractures, and calculates the total absolute permeability. K 1 ; In the digital model, the S720 virtually fills all cracks by modifying the image voxel properties of the crack regions to match the matrix skeleton, retaining only the matrix pores, and then calculating the permeability of the pore system. K 2 ; In the digital model, S730 virtually smooths out all pores by modifying the image voxel properties of the pore region to match the matrix skeleton, retaining only the fracture system, and then calculates the permeability of the fracture system. K 3 ; Based on the above simulation results, S740 quantitatively calculates the seepage contribution ratio of cracks and pores using the following formula: The formula for calculating the contribution of cracks to total permeability is: ; The formula for calculating the contribution of pores to total permeability is: .

[0033] Taking Gulong shale as an example, the implementation process of the method of the present invention will be specifically explained as follows: A representative core was drilled from the target stratum of the Gulong Shale, and a standard plunger sample (approximately 2.5 cm in diameter and 5.0 cm in height) was prepared using a core drill grinder.

[0034] First, the plunger sample was scanned using a micro-CT system at a resolution of 3 μm to obtain an overall three-dimensional structural image containing micron-scale laminations and larger pores. Then, a micro-plunger sample with a diameter of approximately 1 mm was drilled from the plunger sample and scanned using a nano-CT system at a resolution of 50 nm to precisely capture the nanoscale matrix pore structure.

[0035] like Figure 3 As shown, the obtained micron-scale and nanon-scale CT images were imported into the image processing software Dragonfly. First, image preprocessing was performed, including Gaussian filtering and grayscale conversion to suppress noise. Based on the voxel grayscale values ​​and local texture features, the images were precisely segmented into different phases such as the skeleton, matrix pores, organic matter, and lamellar fractures.

[0036] A high-precision pore model generated from nanoscale CT images is precisely embedded into a fracture network framework constructed from microscale CT images, achieving multi-scale image fusion. After image segmentation and fusion, the Marching Cubes algorithm is applied for 3D reconstruction, generating a high-fidelity 3D digital core model that simultaneously contains nanoscale pores and microscale foliation fractures.

[0037] As shown in Table 1, LBM seepage simulations were performed on the validated digital core model for the following three scenarios. The pressure field distribution is shown in the simulation based on the complete digital core model (including pores and fractures). Figure 4 As shown, the calculated total absolute permeability K is 0.0115 mD. A "virtual filling" operation was performed in the digital core model, modifying the voxel properties of all foliation fracture regions to be identical to the rock skeleton, retaining only the matrix porosity. The model state is as follows. Figure 5 As shown in the figure. Seepage simulation was performed on this model, and the permeability K of the matrix pore system was calculated to be 0.0012 mD. A "virtual smoothing" operation was performed in the digital core model to modify the voxel properties of all matrix pore regions to be the same as the rock skeleton, retaining only the foliation fracture system. The model state is as follows. Figure 6 As shown in the figure. Seepage simulation was performed on this model, and the calculated permeability K of the fractured system was 0.0108 mD.

[0038] Table 1. Comparison of simulated absolute permeability values ​​from digital cores and measured absolute permeability values ​​from actual cores;

[0039] Based on the above simulation results, the contribution degree is calculated using the formula proposed in this invention: The contribution of cracks to the total permeability is C = (0.0115 - 0.0012) / 0.0115 × 100% ≈ 89.6%.

[0040] The contribution of pores to the total permeability is C = (0.0115 - 0.0108) / 0.0115 × 100% ≈ 6.1%.

[0041] The calculation results clearly show that, for this Gulong shale sample, the foliation fracture system provides approximately 89.6% of the seepage capacity, making it the absolutely dominant seepage channel for fluids; while the contribution of matrix porosity accounts for only about 6.1%. This quantitative conclusion is highly consistent with the geological understanding that the Gulong shale is highly heterogeneous and has densely developed foliation fractures.

[0042] Therefore, this invention employs the aforementioned method for quantifying the contribution of shale oil reservoir fractures and matrix pore flow. By constructing a high-fidelity digital core model through multi-scale CT scanning, and using the controlled variable method of "virtual filling" and "virtual smoothing," combined with lattice Boltzmann flow simulation, it achieves for the first time a non-destructive and precise quantification of the contribution of fractures and matrix pore flow, providing a key scientific basis for shale oil reservoir evaluation and development plan formulation.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for quantifying the contribution of shale oil reservoir pore fractures and matrix pores to seepage, characterized in that, Includes the following steps: S100. Prepare different shale cores and experimental setups, select representative cores, prepare standard plunger samples, and scan the same core sample sequentially using micron-CT and nano-CT systems to obtain high-definition three-dimensional images of the core as a whole and its nanopores. S200 integrates the three-dimensional high-definition image data obtained from S100 to establish a database covering complex matrix structures from micron-level pores to nano-level matrices; S300 uses grayscale thresholding and machine learning algorithms to accurately segment CT images to distinguish pores, organic matter, cracks and different mineral components. It uses multi-scale image registration technology to construct a unified digital core model and enhances image quality through anisotropic filtering and histogram equalization. S400. The preprocessed two-dimensional CT image sequence is aligned and superimposed layer by layer according to spatial coordinates to construct a three-dimensional voxel matrix with regular grid as the core data volume of digital core. The Marching Cubes algorithm is used to extract the isosurfaces of rock pores and mineral components to generate a high-fidelity three-dimensional digital core model. S500: Compare the real rock database with the reconstructed digital core model to verify the authenticity of the structure. If the verification is consistent, conduct seepage simulation and contribution analysis. If they are inconsistent, return to step S400 for iterative optimization until the model and the real core structure are completely consistent. S600. Perform routine porosity and permeability tests on core samples to obtain experimental data. Use the lattice Boltzmann method to simulate single-phase fluid flow on the validated digital core model. Use image difference calculation to determine whether the gas intrusion area preferentially occupies the foliation fractures or matrix pores. S700: The total absolute permeability is calculated using the original digital core model. The permeability of the system containing only pores and the permeability of the system containing only fractures are obtained by virtually filling the fractures and virtually smoothing the pores, respectively. Finally, the proportion of seepage contribution from fractures and pores is quantitatively calculated based on the simulation results of the three.

2. The method for quantifying the contribution of shale oil reservoir fractures and matrix pore flow according to claim 1, characterized in that, In S300, Gaussian filtering and grayscale processing are introduced before precise segmentation of CT images. The expression is as follows: ； ； in, The kernel is a Gaussian convolution. For the original image, It is a grayscale image. These are pixel coordinates; The unified digital core model expression is constructed as follows: ； in, For standard images; For spatial changes; It is a similarity measure function; These are the regularization and its weight coefficients, respectively. These are the optimal transformation parameters.

3. The method for quantifying the contribution of shale oil reservoir pore fractures and matrix pore flow according to claim 1, characterized in that, The expression for generating a high-fidelity 3D digital core model in S400 is: ； in, For pixel endpoints; Linear interpolation; These are the spatial coordinates of the intersection points of the generated isosurface and the voxel edges.

4. The method for quantifying the contribution of shale oil reservoir pore fractures and matrix pores to seepage as described in claim 1, characterized in that, The expression for image difference operation in S600 is: ； in, I This is an image after gas injection; This is the baseline image before gas injection.

5. A method for quantifying the contribution of shale oil reservoir fractures and matrix pore flow according to claim 1, characterized in that, The expression for the contribution of fractures to total permeability in S700 is as follows: ； The expression for the contribution of pores to total permeability is: ； in, K 1 Total absolute penetration rate; K 2 The permeability of the porous system; K 3 The permeability of the fracture system.

6. The method for quantifying the contribution of shale oil reservoir pore fractures and matrix pore flow according to claim 1, characterized in that, The three-dimensional digital core model constructed in S400 needs to be verified using conventional porosity and permeability testing or scanning electron microscopy data.

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