Method and system for accurately characterizing reservoir seepage space of shale oil reservoir based on multi-scale imaging fusion

Abstract

The invention discloses a shale oil reservoir seepage space accurate characterization method and system based on multi-scale imaging fusion, and relates to the technical field of oil and gas geology and reservoir characterization. According to the method, the three-dimensional structure of the reservoir and seepage space of the shale oil reservoir is reconstructed by fusing the multi-scale image data, and the physical property data is introduced on the basis of the image data, so that the physical constraint is added for reconstructing the three-dimensional structure, and the reservoir and seepage space three-dimensional structure model under the physical constraint is generated; and the reservoir seepage space of the shale oil reservoir can be accurately represented through visualization and quantitative description. According to the method, limitation of traditional single-scale representation is broken through innovatively, fusion of multi-scale imaging data and three-dimensional reconstruction under physical constraints are achieved, and continuity, authenticity and accuracy of shale oil reservoir seepage space representation are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas geology and reservoir characterization technology. Specifically, it relates to a method and system for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion. Background Technology

[0002] Currently, the characterization of shale oil reservoir permeability space mainly relies on various experimental and imaging techniques, including scanning electron microscopy (SEM), focused ion beam SEM, X-ray micro-CT, and industrial CT. These methods can acquire structural images of pores, pore throats, fractures, and mineral components at different scales, providing crucial support for the study of shale oil reservoir permeability space structure. Simultaneously, by combining physical property testing methods such as nitrogen adsorption, mercury porosimetry, and nuclear magnetic resonance (NMR), parameters such as pore size distribution, porosity, and permeability can be obtained, enabling quantitative characterization of the reservoir permeability system. Furthermore, some numerical reconstruction and digital core modeling techniques, through three-dimensional reconstruction based on image data, are further used for pore throat network analysis and seepage simulation, providing fundamental data support for the detailed description of shale oil reservoir permeability space.

[0003] However, existing methods generally suffer from problems such as fragmented multi-scale data, insufficient physical constraints, and poor structural continuity. The lack of a unified coordinate system between images of different scales makes it difficult to achieve the fusion and reconstruction of multi-scale structures; the failure to effectively couple physical property data with image information results in insufficient realism of reservoir-permeability space models, making it difficult to achieve accurate three-dimensional characterization of shale oil reservoir-permeability systems. Summary of the Invention

[0004] This invention provides a method and system for accurately characterizing the reservoir permeability space of shale oil reservoirs based on multi-scale imaging fusion, which at least solves the problem of fragmented multi-scale data in the prior art, thereby significantly improving the standard accuracy of the reservoir permeability space of shale oil reservoirs.

[0005] To achieve the above objectives, the present invention provides a method and system for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion.

[0006] In a first aspect, the present invention provides a method for accurately characterizing the reservoir permeability space of shale oil reservoirs based on multi-scale imaging fusion, the method comprising: Core samples were collected from shale oil reservoirs, and multi-scale images and physical property data of the core samples were obtained. The multi-scale image data is standardized and preprocessed and structure is identified to obtain a multi-scale digital three-dimensional structure. The geometric and topological feature parameters of the structural units of the shale oil reservoir's storage and permeability space are calculated from the multi-scale digital three-dimensional structure through feature extraction. Based on the geometric and topological feature parameters, scale mapping, spatial registration and spatial fusion are performed on the multi-scale digital three-dimensional structure to form a continuous three-dimensional structural model of the shale oil reservoir's storage and permeability space. Based on the three-dimensional structural model, the physical property data are used as constraints to optimize the physical consistency and reconstruct the structure of the three-dimensional structural model, so as to form a three-dimensional structural model under the physical constraints of the shale oil reservoir's storage and permeability space. The three-dimensional structural model under the physical constraints is quantitatively characterized to realize a multi-dimensional quantitative description of the shale oil reservoir's storage and permeability space; and a three-dimensional visualization model of the shale oil reservoir's storage and permeability space is output based on the three-dimensional structural model under the physical constraints. Among them, the multi-scale includes four scales: nanoscale, microscale, millimeter scale, and core scale.

[0007] Specifically, acquiring the multi-scale image data of the core sample includes: The shale oil reservoir core samples were graded and selected to obtain nano-scale, micron-scale, millimeter-scale and core-scale samples respectively; Images of the sample at corresponding scales are acquired using various imaging techniques, and the images are then processed to obtain the multi-scale image data.

[0008] Specifically, the geometric and topological feature parameters include aperture distribution, aperture-throat ratio, connectivity, and fractal dimension.

[0009] Specifically, the scaling and spatial registration of the multi-scale digital 3D structure based on the geometric and topological feature parameters includes: In the multi-scale digital three-dimensional structure, the micron-scale digital three-dimensional structure is selected, and the nano-scale, millimeter-scale, and core-scale digital three-dimensional structures are scaled to unify the scale of the nano-scale, millimeter-scale, and core-scale digital three-dimensional structures to the scale of the micron-scale digital three-dimensional structure. Spatial transformation is performed on the nanoscale, millimeter-scale, and core-scale digital three-dimensional structures to align them with the micrometer-scale digital three-dimensional structure in three-dimensional space; geometric and topological feature parameters are used to constrain the alignment of the nanoscale, millimeter-scale, and core-scale digital three-dimensional structures with the micrometer-scale digital three-dimensional structure; finally, the multi-scale digital three-dimensional structure in a unified coordinate system is output for subsequent spatial fusion.

[0010] Specifically, the spatial fusion includes: The multi-scale digital three-dimensional structures located in the same structural unit of the shale oil reservoir's storage and permeability space are integrated under a unified coordinate system; combined with the geometric and topological feature parameters, the detail priority and weighted fusion method of the multi-scale digital three-dimensional structures in the structural unit of the shale oil reservoir's storage and permeability space are determined to generate a continuous and consistent three-dimensional spatial structure of the shale oil reservoir's storage and permeability space. The local parts of the three-dimensional spatial structure are optimized to enhance the integrity and connectivity of the overall three-dimensional spatial structure; then, a continuous and unified three-dimensional structural model of the shale oil reservoir's storage and permeation space is output.

[0011] Specifically, the step of using the physical property data as constraints to perform physical consistency optimization and structural reconstruction of the three-dimensional structural model includes: Based on the three-dimensional structural model, the physical property data are mapped to the corresponding structural units of the shale oil reservoir's storage and permeability space, and physical consistency constraints are set. The physical consistency algorithm is used to locally adjust and iteratively optimize the structural units of the reservoir space of each shale oil reservoir. Under the premise of maintaining geometric and topological continuity, the physical properties of the three-dimensional structural model itself are kept consistent with the physical property data, thereby forming a three-dimensional structural model under the physical constraints.

[0012] Specifically, the quantitative characterization includes: Geometric feature characterization, which includes the pore volume distribution and pore throat morphology parameters of the shale oil reservoir's storage and permeability space; Topological structure characterization, which includes the structural parameters of the pore-throat network of the shale oil reservoir's storage and permeability space and the connectivity index of the pore-throat network; The seepage characteristics are characterized by the complexity of the seepage path, the effective permeability, and the distribution of the seepage path in the shale oil reservoir's seepage space. Multi-scale comprehensive index, which includes scale coupling coefficient and structure-permeability coupling parameter.

[0013] Secondly, this invention provides a precise characterization system for the reservoir permeability space of shale oil reservoirs based on multi-scale imaging fusion, the system comprising: A multi-scale data acquisition module, which is used to acquire the multi-scale image and the physical property data; A multi-scale structure construction and feature extraction module is connected to the multi-scale data acquisition module. The multi-scale structure construction and feature extraction module is used to form the multi-scale digital three-dimensional structure and extract the geometric and topological feature parameters. Multi-scale spatial fusion module: The multi-scale spatial fusion module is connected to the multi-scale structure construction and feature extraction module, and the multi-scale spatial fusion module is used to generate the three-dimensional structure model; A physical constraint optimization module is connected to the multi-scale spatial fusion module and the multi-scale data acquisition module. The physical constraint optimization module is used to generate a three-dimensional structural model under the physical constraints. A multidimensional quantitative characterization and visualization module is connected to the physical constraint optimization module. The multidimensional quantitative characterization and visualization module is used to perform multidimensional quantitative characterization of the three-dimensional structural model under the physical constraints and output the three-dimensional visualization model.

[0014] This application provides a method for accurately characterizing the reservoir permeability space of shale oil reservoirs based on multi-scale imaging fusion. This method reconstructs the three-dimensional structure of the reservoir permeability space by fusing multi-scale image data. Furthermore, it introduces physical property data based on the image data, adding physical constraints to the reconstructed three-dimensional structure, thus generating a physically constrained three-dimensional structural model of the reservoir permeability space. Through visualization and quantitative description, the reservoir permeability space of shale oil reservoirs can be accurately characterized. This method overcomes the limitations of traditional single-scale characterization, achieving the fusion of multi-scale imaging data and three-dimensional reconstruction under physical constraints, significantly improving the continuity, realism, and accuracy of the reservoir permeability space characterization. Attached Figure Description

[0015] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart illustrating a method for accurately characterizing the reservoir permeability space of shale oil reservoirs based on multi-scale imaging fusion. Figure 2 This is a schematic diagram of the connection of a shale oil reservoir permeability space accurate characterization system based on multi-scale imaging fusion.

[0016] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0019] In this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0020] This application provides a method and system for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion. This characterization method reconstructs the three-dimensional structure of shale oil reservoir permeability space by fusing multi-scale image data, and introduces physical property data on the basis of image data to add physical constraints to the reconstructed three-dimensional structure, thereby generating a three-dimensional structural model of the permeability space under physical constraints. The method achieves accurate characterization of shale oil reservoir permeability space through visualization and quantitative description.

[0021] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0022] Figure 1 A flowchart illustrating the method for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion provided in this application is shown below. Figure 1 As shown, this embodiment provides a method for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion. The method includes: S101: Collect core samples from shale oil reservoirs and obtain multi-scale images and physical property data of the core samples.

[0023] Specifically, acquiring the multi-scale image data of the core sample includes: The shale oil reservoir core samples were graded and selected to obtain nano-scale, micron-scale, millimeter-scale and core-scale samples respectively; Images of the sample at corresponding scales are acquired using various imaging techniques, and the images are then processed to obtain the multi-scale image data.

[0024] The specific steps of implementation S101 include: 1. Acquisition of multi-scale images of core samples 1.1 Selection of Core Samples by Grading Core samples from shale oil reservoirs were processed to obtain multi-scale samples.

[0025] 1.1.1 Core-grade sample acquisition From the original core samples retrieved from the well, representative sections are selected according to reservoir depth and lithological characteristics; the selected sections are numbered, their directions are marked, and they are cut into appropriate lengths (such as 5-10cm) to obtain core-grade samples.

[0026] 1.1.2 Acquisition of millimeter-scale samples On core samples, small cylinders or cubes with a diameter of about 3-10 mm are removed using a micro drill or cutter, and the sampling location and number are recorded to obtain millimeter-sized samples.

[0027] 1.1.3 Acquisition of micron-sized samples From the millimeter-sized sample, small pieces or thin slices with a side length of about 0.5-2 mm are cut out and their surfaces are ground or polished to obtain a smooth observation surface; the sampling location and number are recorded to obtain the micrometer-sized sample.

[0028] 1.1.4 Acquisition of Nanoscale Samples In a micrometer-scale sample, a region of interest is selected, and an ultrathin sample with a thickness of tens to hundreds of nanometers is prepared by focusing ion beam (FIB) or ultrathin slicing technology; the sampling location and number are recorded to obtain a nanometer-scale sample.

[0029] 1.2 Acquisition of Multi-Scale Images Core samples are obtained using industrial CT scanning or X-ray imaging to capture their centimeter-scale three-dimensional structure. Millimeter-scale specimens were scanned using high-resolution micro-CT (micrometer CT) to obtain three-dimensional images of the distribution of pores, skeleton, and microcracks; Micrometer-sized samples were imaged using scanning electron microscopy (SEM) or focused ion beam scanning electron microscopy (FIB-SEM) to identify fine pore structures such as organic pores, inorganic pores, and clay pores. Nanoscale samples were imaged using transmission electron microscopy (TEM) or focused ion beam scanning electron microscopy (FIB-SEM) to observe the internal nanopores, mineral lattices, and pore throat connections of organic matter.

[0030] Organize the images at each scale to remember the multi-scale image data.

[0031] 2. Acquisition of physical property data from core samples 2.1 Sample Preparation The selected core samples are numbered, cleaned, and dried; they are then cut into standard sizes (such as cylindrical or cubic specimens) according to testing requirements.

[0032] 2.2 Determination of physical property data 2.2.1 Nitrogen adsorption method The standard sample was crushed or prepared into small pieces, and nitrogen adsorption-desorption experiments were carried out under low temperature (77K) conditions. The pore size distribution curve was inverted based on the adsorption isotherm, the specific surface area was calculated using the BET model, and the pore volume and porosity were obtained through the adsorption volume. Parameters obtained: pore size distribution (<100nm), specific surface area, pore volume, and nanopore contribution rate.

[0033] 2.2.2 Mercury Porosimetry Select a small standard sample and gradually apply high pressure to inject mercury into the pores of the sample; record the relationship curve between mercury intrusion volume and pressure, and calculate the pore throat radius distribution based on the capillary principle. Parameters to be obtained: pore throat radius distribution (tens of nanometers to tens of micrometers), porosity, pore throat connectivity and sealing.

[0034] 2.2.3 Nuclear Magnetic Resonance Method A standard sample is injected with a saturated fluid (such as deionized water or simulated oil) under vacuum conditions to fill the pore space; the standard sample injected with saturated fluid is placed in an external magnetic field to measure the hydrogen nucleus relaxation time (T2) distribution, and the pore size and permeability are inverted by combining empirical models. Parameters obtained: porosity (total hydrogen signal intensity), pore size distribution (inverted from T2 spectrum), and permeability (calculated based on T2 spectrum model).

[0035] This step, by acquiring multi-scale imagery and physical property testing data from shale oil reservoir core samples, allows for a comprehensive understanding of the reservoir's structural characteristics and physical properties across different spatial scales. Multi-scale imagery reveals the spatial morphology and structural evolution of the reservoir, from nanopores and micropores to fractures and even bedding; physical property data reflects key parameters such as porosity, permeability, pore size distribution, and pore throat connectivity of the core samples. The synergistic acquisition of these two data points provides a complete data foundation for subsequent 3D structural modeling and physical constraints, achieving a unified multi-dimensional characterization of reservoir structure and physical property information, and laying a reliable foundation for the fine characterization and quantitative analysis of shale oil reservoirs.

[0036] Step S102: Standardize and preprocess the multi-scale image data and perform structural recognition to obtain a multi-scale digital three-dimensional structure, and calculate the geometric and topological feature parameters of the structural units of the shale oil reservoir's permeability space from the multi-scale digital three-dimensional structure through feature extraction.

[0037] Specifically, the geometric and topological feature parameters include aperture distribution, aperture-throat ratio, connectivity, and fractal dimension.

[0038] The specific steps of implementation S102 include: 1. Digital 3D Structure 1.1 Image Data Import and Annotation Preprocessing Image data of different scales (nanometer, micrometer, millimeter, and core) are uniformly imported into the processing platform and subjected to format unification, scale correction, and spatial resolution standardization. Noise removal, grayscale equalization, filtering enhancement, and boundary optimization are performed on the images to improve the accuracy of subsequent structure recognition.

[0039] 1.2 Structural Region Segmentation and Identification The acquired multi-scale image slices (which can be continuous or non-continuous sequences) are processed; threshold segmentation, region growing, or machine learning algorithms are used to automatically identify and classify structural units such as pores, throats, and mineral particles; binary or multiphase structural images are generated in each two-dimensional image to distinguish different spatial phases such as "pore-skeleton-throat"; and clear structural boundaries and phase information are provided for subsequent spatial registration and three-dimensional reconstruction.

[0040] 1.3 Three-dimensional reconstruction and voxelization Spatial reconstruction processing is performed on image slices identified by structure recognition: When the image slices are a continuous sequence, spatial registration and stacking algorithms are used to directly generate digital three-dimensional structures, thereby achieving continuous reconstruction of pore-throat structures. When the image slices are non-continuously sampled, the missing regions are filled and reconstructed based on similar structural features or statistical interpolation methods, and the reconstruction results are converted into voxel form to generate digital three-dimensional structures. The digital three-dimensional structures at various scales are organized to obtain multi-scale digital three-dimensional structures.

[0041] 2. Extraction and calculation of geometric and topological feature parameters 2.1 Preparation and Alignment Multi-scale digital 3D structures (nano, micro, millimeter and core-level) are imported into a unified spatial coordinate system; spatial alignment of models of different scales is performed to establish scale correspondence.

[0042] 2.2 Pore-throat network segmentation For each scale of the digitized 3D structure, pores and throats are independently segmented to generate a binary pore-throat network model, providing a basis for the extraction of geometric and topological feature parameters at multiple scales.

[0043] 2.3 Geometric Feature Extraction 2.3.1 Calculation of Aperture Distribution Connectivity analysis was performed on all pore voxels in the pore-throat binary model to identify independent pore voxel clusters; the volume of each pore voxel cluster was calculated. It serves as a geometric unit for independent pores.

[0044] Volume of each pore Converted to equivalent spherical diameter :

[0045] Calculate the equivalent diameter of all pores The aperture distribution curve is obtained by analyzing the distribution.

[0046] 2.3.2 Calculation of the pore-throat ratio In the pore-throat network, identify the minimum or average diameter of each pore and its connected throat; calculate the ratio of pore diameter to throat diameter:

[0047] in, For the pore-throat ratio, The pore diameter is This is the diameter of the larynx.

[0048] The ratio distribution of all pore-throat pairs is summarized and statistically analyzed to characterize the transitional structural features of the reservoir space.

[0049] 2.4 Topological Feature Extraction 2.4.1 Connectivity Analysis The pore-throat network is skeletonized, simplifying its spatial structure to a node-edge form. The number of nodes, edges, and the maximum connected cluster volume are calculated. Connectivity is obtained by calculating the average node degree and the number of connected channels in the pore network. To quantitatively characterize fluid accessibility and seepage path characteristics.

[0050] 2.4.2 Fractal Dimension Calculation Box-counting was employed to perform multi-scale (box-scale) spatial coverage analysis on the pore-throat system by setting different spatial box scales on the digital 3D structure. The distribution of the number of boxes containing pore structures at different box scales was calculated, and the fractal dimension was obtained by fitting the results. .

[0051] This step involves standardized preprocessing and structural identification of multi-scale image data to obtain a spatially consistent digital 3D structure. Geometric and topological feature parameters, such as pore size distribution, pore-throat ratio, connectivity, and fractal dimension, are extracted, enabling a quantitative transformation of reservoir space from image information to structural parameters. This process not only reveals the spatial distribution characteristics and connectivity of the pore-throat system but also provides a high-precision structural foundation for subsequent multi-scale fusion, physical constraint optimization, and 3D visualization modeling, significantly improving the accuracy and simulation performance of reservoir characterization.

[0052] S103: Based on the geometric and topological feature parameters, scale mapping, spatial registration and spatial fusion are performed on the multi-scale digital three-dimensional structure to form a three-dimensional structural model of the continuous shale oil reservoir storage and permeability space. Specifically, the scaling and spatial registration of the digitized 3D structure based on the geometric and topological feature parameters includes: In the multi-scale digital three-dimensional structure, the micron-scale digital three-dimensional structure is selected, and the nano-scale, millimeter-scale, and core-scale digital three-dimensional structures are scaled to unify the scale of the nano-scale, millimeter-scale, and core-scale digital three-dimensional structures to the scale of the micron-scale digital three-dimensional structure. Spatial transformation is performed on the nanoscale, millimeter-scale, and core-scale digital three-dimensional structures to align them with the micrometer-scale digital three-dimensional structure in three-dimensional space; geometric and topological feature parameters are used to constrain the alignment of the nanoscale, millimeter-scale, and core-scale digital three-dimensional structures with the micrometer-scale digital three-dimensional structure; finally, the multi-scale digital three-dimensional structure in a unified coordinate system is output for subsequent spatial fusion.

[0053] Specifically, the spatial fusion includes: The multi-scale digital three-dimensional structures located in the same structural unit of the shale oil reservoir's storage and permeability space are integrated under a unified coordinate system; combined with the geometric and topological feature parameters, the detail priority and weighted fusion method of the multi-scale digital three-dimensional structures in the structural unit of the shale oil reservoir's storage and permeability space are determined to generate a continuous and consistent three-dimensional spatial structure of the shale oil reservoir's storage and permeability space. The local parts of the three-dimensional spatial structure are optimized to enhance the integrity and connectivity of the overall three-dimensional spatial structure; then, a continuous and unified three-dimensional structural model of the shale oil reservoir's storage and permeation space is output.

[0054] The specific steps in step S103 during implementation include: 1. Multi-scale digital 3D structure scale mapping Scale mapping refers to the transformation of the scale and coordinates of digital 3D structures with different resolutions under a unified spatial scale. Specifically, firstly, a scale with a large structural coverage and strong representativeness is selected as the reference scale (usually medium resolution to take into account both details and overall structure), with micrometers being the preferred reference scale in practice. Scaling factors between scales are calculated based on the physical dimensions of voxels, and the remaining structures are subjected to size unification and coordinate transformation. Subsequently, geometric and topological characteristic parameters such as porosity distribution and connectivity are compared under the unified scale to correct local structural differences and achieve consistency in geometric scale and spatial position of multi-scale data.

[0055] 2. Multi-scale digital 3D structure spatial registration Spatial registration refers to the process of precisely aligning multi-scale digitized 3D structures in space after scale mapping. Specifically, by identifying corresponding feature regions (such as pore distribution boundaries, mineral grains, or marker points) in structures at each scale, a rigid transformation is used to achieve initial alignment of the overall position and orientation. Then, a non-rigid transformation is used to perform minor deformation corrections on local areas, so that structures at each scale can accurately coincide in spatial coordinates, thereby ensuring the spatial continuity and correspondence of structures such as pores and throats across different scales.

[0056] 3. Multi-scale digital 3D structure spatial fusion 3.1 Structural Integration Preparation under a Unified Space After scale mapping and spatial registration are completed, all digitized 3D structures are now aligned with a unified coordinate system and scale. This stage primarily involves dividing and indexing the spatial extent, and establishing a fusion region set.

[0057] in Indicates the first A structure in coordinates voxel value at the location; Digital 3D structures at various scales; The integration process provides a spatial correspondence and index foundation for subsequent weighted fusion and local optimization.

[0058] 3.2 Identification of Overlapping Regions and Determination of Weights Overlapping regions refer to areas where data of different scales are covered under the same coordinate system; Geometric and topological parameters corresponding to overlapping regions are extracted from geometric and topological feature parameters, and the confidence scores of data at each scale are calculated. First, calculate the image quality index. and inter-scale structural consistency index : Image quality indicators :

[0059] in, This represents the edge sharpness index, used to reflect the accuracy of pore-throat boundary identification. Higher image quality indicates more reliable pore-throat identification results.

[0060] Inter-scale structural consistency index : Calculations are performed based on the differences in geometric and topological features at various scales:

[0061] Here, ref represents the data at the reference scale, and in this embodiment, a high resolution (nanometer-scale) is selected as the reference scale. If the geometric and topological features are small in difference from the reference scale, it indicates good structural continuity and high confidence.

[0062] Overall confidence level calculate: Image quality and structural consistency together determine the overall reliability of data at this scale, calculated as follows:

[0063] in, These are the weighting coefficients (usually taken as...). The settings are based on the importance of image clarity and structural stability.

[0064] Then, based on the resolution of the data at each scale With overall confidence level Determine the fusion weights :

[0065] in, This represents the resolution index of the i-th scale at the current voxel or fusion position, used to calculate the numerator of the weights for that scale, i.e., "the share contributed by that scale"; This represents the resolution index of all scales at the current voxel or fusion location, used for summing the denominator to ensure all weights are normalized.

[0066] 3.3 Weighted fusion to generate continuous structures Weighted fusion of voxels in overlapping regions:

[0067] Structure-preserving interpolation or smoothing algorithms are used at the pore-throat boundary region to ensure a continuous transition of the fused pore-throat network.

[0068] 3.4 Processing of Non-overlapping Regions For non-overlapping areas not covered by high-resolution data (usually low-resolution or core-level data areas), the original structural data is preserved. At the same time, local smoothing or interpolation is performed based on the geometric and topological characteristics (such as porosity and connectivity) of adjacent fused areas, so that the non-overlapping areas are structurally connected to the surrounding fused areas.

[0069] 3.5 Local Optimization and Connectivity Repair Optimize regions with pore breaks, isolated throats, or uneven boundary transitions that appear in the entire fusion model: Use skeleton extension or hole-throat network reconnection algorithms to ensure connectivity; Morphological smoothing of local pore-throat boundaries; Based on connectivity functions Iterative corrections are made until the connectivity of the porous network reaches stability; whereby the connectivity function... This indicates the connectivity of a set of voxels or a pore-throat network; the closer the value is to 1, the more connected the network. The number of connected units refers to the actual number of pore / throat network units connected in the voxel set. This indicates the total number of units, usually referring to the total number of pore or throat units in a voxel set.

[0070] This step addresses both the connection between non-overlapping regions and the fusion region, and also repairs any local discontinuities that may arise during the weighted fusion process.

[0071] 3.6 Output a continuous and unified three-dimensional structural model The final generated three-dimensional structural model of the shale oil reservoir's reservoir-permeability space takes into account both high-resolution details and macroscopic structural continuity. The pore-throat network is complete and has good connectivity, providing a unified structural basis for subsequent physical property constraint optimization and three-dimensional visualization.

[0072] This step, based on geometric and topological characteristic parameters, performs scale mapping, spatial registration, and spatial fusion, which can unify multi-scale digital 3D structures with different resolutions into the same coordinate system. This not only achieves the continuity of the pore-throat network and the consistency of the overall structure, but also effectively combines high-resolution details with low-resolution macroscopic structures, improving the accuracy and physical realism of the 3D model of the reservoir's storage and permeability space. This provides a reliable foundation for subsequent physical property constraint optimization and 3D visualization.

[0073] Step S104: Based on the three-dimensional structural model, the physical property data is used as a constraint to perform physical consistency optimization and structural reconstruction on the three-dimensional structural model, so as to form a three-dimensional structural model under the physical constraints of the shale oil reservoir's storage and permeability space.

[0074] Specifically, the step of using the physical property data as constraints to perform physical consistency optimization and structural reconstruction of the three-dimensional structural model includes: Based on the three-dimensional structural model, the physical property data are mapped to the corresponding structural units of the shale oil reservoir's storage and permeability space, and physical consistency constraints are set. The physical consistency algorithm is used to locally adjust and iteratively optimize the structural units of the reservoir space of each shale oil reservoir. Under the premise of maintaining geometric and topological continuity, the physical properties of the three-dimensional structural model itself are kept consistent with the physical property data, thereby forming a three-dimensional structural model under the physical constraints.

[0075] The specific steps in step S104 during implementation include: 1. Physical property data mapping Map the physical property data obtained from the experiment to the corresponding structural unit or voxel positions in the three-dimensional structural model: Nitrogen adsorption method → ​​pore size distribution (<100nm), specific surface area, pore volume, and nanopore contribution rate Mercury porosimetry → Pore throat radius distribution (tens of nanometers to tens of micrometers), porosity, pore throat connectivity and sealing Nuclear magnetic resonance (NMR) method → ​​total porosity (hydrogen signal intensity), pore size distribution, permeability Each voxel or structural unit establishes a mapping relationship with its corresponding physical property parameters, forming a constraint basis.

[0076] 2. Set physical consistency constraints Define constraints to make the physical properties of the model match the experimental data, such as:

[0077] Where exp represents the physical property data obtained from indoor tests, and the data to the left of the approximate equality sign represents the physical property data of the model; Porosity The orifice-throat radius, Let represent the pore-throat connectivity, and k represent the permeability.

[0078] 3. Local adjustments and iterative optimization For each structural element (such as a local region, voxel block, or pore-throat element), local adjustments are made to optimize the objective function:

[0079] in, , , , The weights of each physical property parameter reflect the importance of optimization.

[0080] By continuously adjusting the pore-throat voxels using iterative algorithms (such as the least squares method), geometric and topological continuity is ensured, while local physical properties are made consistent with experimental data.

[0081] 4. Generate a 3D structural model under physical constraints The output is an iteratively optimized 3D structural model that retains the connectivity of the pore-throat network and the details of the microstructure, while also meeting the constraints of physical property data such as nitrogen adsorption, mercury intrusion porosimetry, and nuclear magnetic resonance, and can be used for 3D visualization.

[0082] This step utilizes physical property data obtained from experiments such as nitrogen adsorption, mercury intrusion porosimetry, and nuclear magnetic resonance (NMR) to impose physical consistency constraints and optimize the digital 3D structural model. This ensures that the model maintains geometric and topological continuity while reflecting the pore size distribution, porosity, and permeability characteristics of the real pore-throat system. Through iterative adjustments and constraint corrections, deviations between the model and experimental parameters can be effectively eliminated, achieving a unified coupling between structural characterization and physical properties. This yields a 3D structural model of shale oil reservoirs with realistic reservoir-permeability characteristics, providing a reliable foundation for subsequent visualization and accurate characterization of reservoir-permeability space.

[0083] S105: Quantitatively characterize the three-dimensional structural model under the physical constraints to achieve a multi-dimensional quantitative description of the shale oil reservoir's permeability space; and output a three-dimensional visualization model of the shale oil reservoir's permeability space based on the three-dimensional structural model under the physical constraints.

[0084] Specifically, the quantitative characterization includes: Geometric feature characterization, which includes the pore volume distribution and pore throat morphology parameters of the shale oil reservoir's storage and permeability space; Topological structure characterization, which includes the structural parameters of the pore-throat network of the shale oil reservoir's storage and permeability space and the connectivity index of the pore-throat network; The seepage characteristics are characterized by the complexity of the seepage path, the effective permeability, and the distribution of the seepage path in the shale oil reservoir's seepage space. Multi-scale comprehensive index, which includes scale coupling coefficient and structure-permeability coupling parameter.

[0085] The specific steps included in implementing step S105 are as follows: 1. Multidimensional quantitative characterization of reservoir permeability space in shale oil reservoirs 1.1 Geometric Feature Representation Pore ​​volume distribution: The proportion of pore voxels to total voxels in a statistically digitized 3D model, calculated using the following formula:

[0086] in, For pore volume, Total volume; and These represent the porosity and the total prime number, respectively.

[0087] Pore ​​throat morphology parameters: Based on the pore throat recognition results, the pore diameter is extracted. Throat diameter , throat ratio The distribution range and mean were statistically analyzed.

[0088] 1.2 Topological Structure Representation Phreak throat network structure parameters: The pore throat connection network is extracted by skeletonization, and the average node degree is calculated. :

[0089] in, Let be the number of sides. This represents the number of nodes.

[0090] Connectivity Indicators Represented by the percentage of the volume of the largest connected subnet:

[0091] in, The maximum connected volume in a porous network. This represents the total volume of the porous network.

[0092] 1.3 Characterization of seepage characteristics seepage path complexity Calculate the average flow path length using the shortest path algorithm. And calculate the average flow path length. With geometric line length The ratio:

[0093] A value greater than 1 indicates a more complex path.

[0094] Effective permeability: Porosity was selected using an empirical correlation method. Specific surface area of ​​pores To establish an empirical formula to calculate the effective penetration rate :

[0095] Seepage path distribution: Records the spatial distribution density of the main channels, reflecting the heterogeneity of seepage.

[0096] 1.4 Multi-scale comprehensive index Scale coupling coefficient : Measuring the correlation of pore throat parameters at different scales:

[0097] i,j represent different scales.

[0098] Structure-flow coupling parameter: comprehensive porosity Connectivity With penetration rate k:

[0099] in, This represents the maximum penetration rate.

[0100] The parameters used in the above quantitative characterization calculations were all extracted and calculated from the three-dimensional structural model under physical constraints.

[0101] 2. Three-dimensional visualization model of shale oil reservoir permeability space Based on a physically constrained and optimized 3D structural model, voxel rendering and multi-attribute mapping techniques are used to visualize and distinguish pore, throat, and matrix regions using different colors or transparency. Combined with parameters such as porosity, connectivity, and seepage path, corresponding attribute fields are generated, enabling a visually intuitive 3D display of the reservoir's structure and physical properties. Through interactive rotation, slicing, and local magnification functions, a 3D visualization model of the shale oil reservoir's reservoir space with multi-scale detail visualization and physical attribute annotation is output, providing intuitive support for reservoir microstructure analysis and seepage process simulation.

[0102] This step systematically and quantitatively characterizes the geometric features, topological structure, and seepage characteristics of shale oil reservoirs' reservoir-permeability space. This enables a multi-dimensional and quantifiable description of the reservoir-permeability space, providing data support for the coupled analysis of structural differences and physical property variations. By establishing multi-scale comprehensive indicators, the correlation and coupling effects between pore-throat structures at different scales can be revealed, reflecting the complexity and heterogeneity of the reservoir's internal structure. Combined with the output of a three-dimensional visualization model, a direct display of the pore-throat network and seepage channels can be achieved, significantly improving the accuracy of reservoir structure and seepage characteristic identification and the intuitiveness of interpretation. This provides a reliable digital foundation for the refined evaluation and seepage simulation of shale oil reservoirs.

[0103] This method reconstructs the three-dimensional structure of shale oil reservoir permeability space by fusing multi-scale image data. It introduces physical property data based on the image data, adding physical constraints to the reconstructed 3D structure, thus generating a physically constrained 3D structural model of the reservoir permeability space. Visualization and quantitative description enable precise characterization of the shale oil reservoir permeability space. This invention overcomes the limitations of traditional single-scale characterization through methodological innovation, achieving the fusion of multi-scale imaging data and 3D reconstruction under physical constraints. This method significantly improves the accuracy and continuity of shale oil reservoir permeability space structure characterization, enabling unified expression and quantitative analysis of multi-scale pore-throat structures. The resulting model is more realistic and reliable in terms of geometric structure, topological connectivity, and physical properties, accurately reflecting the pore connectivity and seepage characteristics of the reservoir. The method results exhibit high precision, high consistency, and strong physical rationality, providing strong support for the refined characterization and development evaluation of shale oil reservoirs.

[0104] Figure 2 This is a schematic diagram of the connection of the shale oil reservoir permeability space accurate characterization system based on multi-scale imaging fusion provided in this application, as shown in the figure. Figure 2 As shown, this embodiment provides a precise characterization system for shale oil reservoir permeability space based on multi-scale imaging fusion. This system applies... Figure 1 The embodiment describes a method for accurately characterizing the reservoir permeability space of shale oil reservoirs based on multi-scale imaging fusion. The system includes: A multi-scale data acquisition module, which is used to acquire the multi-scale image and the physical property data; A multi-scale structure construction and feature extraction module is connected to the multi-scale data acquisition module. The multi-scale structure construction and feature extraction module is used to form the multi-scale digital three-dimensional structure and extract the geometric and topological feature parameters. Multi-scale spatial fusion module: The multi-scale spatial fusion module is connected to the multi-scale structure construction and feature extraction module, and the multi-scale spatial fusion module is used to generate the three-dimensional structure model; A physical constraint optimization module is connected to the multi-scale spatial fusion module and the multi-scale data acquisition module. The physical constraint optimization module is used to generate a three-dimensional structural model under the physical constraints. A multidimensional quantitative characterization and visualization module is connected to the physical constraint optimization module. The multidimensional quantitative characterization and visualization module is used to perform multidimensional quantitative characterization of the three-dimensional structural model under the physical constraints and output the three-dimensional visualization model.

[0105] In practice, the multi-scale data acquisition module inputs the acquired multi-scale images and physical property data into the multi-scale structure construction and feature extraction module; the digital three-dimensional structure and geometric and topological feature parameters output by the multi-scale structure construction and feature extraction module are transferred to the multi-scale spatial fusion module for scale mapping, spatial registration and fusion; the fused three-dimensional structural model is sent to the physical constraint optimization module for structural consistency adjustment under physical property constraints; finally, the multi-dimensional quantitative characterization and visualization module realizes the quantitative analysis and visualization output of the three-dimensional model, forming a complete and accurate characterization result of the shale oil reservoir's permeability space.

[0106] This system achieves continuous three-dimensional reconstruction of shale oil reservoir permeability space from the nanometer to the core scale through the fusion of multi-scale imaging data and optimization of physical constraints. It accurately reflects the geometric morphology and connectivity of pore structures within a unified coordinate system. The system effectively integrates multi-source images and physical property information, resulting in a model with both high precision and high reliability in structural details and macroscopic consistency, significantly improving the refinement and physical consistency of reservoir permeability space characterization. Simultaneously, through multi-dimensional quantitative analysis and three-dimensional visualization output, it can intuitively display the spatial distribution characteristics of reservoir structure and seepage channels, providing a more realistic and instructive digital foundation for shale oil reservoir evaluation and development.

[0107] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0108] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for accurately characterizing the reservoir-permeability space of shale oil reservoirs based on multi-scale imaging fusion, characterized in that, include: Core samples were collected from shale oil reservoirs, and multi-scale images and physical property data of the core samples were obtained. The multi-scale image data is standardized and preprocessed and structure is identified to obtain a multi-scale digital three-dimensional structure. The geometric and topological feature parameters of the structural units of the shale oil reservoir's storage and permeability space are calculated from the multi-scale digital three-dimensional structure through feature extraction. Based on the geometric and topological feature parameters, scale mapping, spatial registration and spatial fusion are performed on the multi-scale digital three-dimensional structure to form a continuous three-dimensional structural model of the shale oil reservoir's storage and permeability space. Based on the three-dimensional structural model, the physical property data are used as constraints to optimize the physical consistency and reconstruct the structure of the three-dimensional structural model, so as to form a three-dimensional structural model under the physical constraints of the shale oil reservoir's storage and permeability space. The three-dimensional structural model under the physical constraints is quantitatively characterized to realize a multi-dimensional quantitative description of the shale oil reservoir's storage and permeability space; and a three-dimensional visualization model of the shale oil reservoir's storage and permeability space is output based on the three-dimensional structural model under the physical constraints. Among them, the multi-scale includes four scales: nanoscale, microscale, millimeter scale, and core scale.

2. The method for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion according to claim 1, characterized in that, The acquisition of multi-scale image data of the core sample includes: The shale oil reservoir core samples were graded and selected to obtain nanoscale, micrometer-scale, millimeter-scale and core-scale samples respectively. Images of the sample at corresponding scales are acquired using various imaging techniques, and the images are then processed to obtain the multi-scale image data.

3. The method for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion according to claim 1, characterized in that, The geometric and topological characteristic parameters include aperture distribution, aperture-throat ratio, connectivity, and fractal dimension.

4. The method for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion according to claim 1, characterized in that, The scaling and spatial registration of the multi-scale digital 3D structure based on the geometric and topological feature parameters includes: In the multi-scale digital three-dimensional structure, the micron-scale digital three-dimensional structure is selected, and the nano-scale, millimeter-scale, and core-scale digital three-dimensional structures are scaled to unify the scale of the nano-scale, millimeter-scale, and core-scale digital three-dimensional structures to the scale of the micron-scale digital three-dimensional structure. Spatial transformation is performed on the nanoscale, millimeter-scale, and core-scale digital three-dimensional structures to align them with the micrometer-scale digital three-dimensional structure in three-dimensional space; geometric and topological feature parameters are used to constrain the alignment of the nanoscale, millimeter-scale, and core-scale digital three-dimensional structures with the micrometer-scale digital three-dimensional structure; finally, the multi-scale digital three-dimensional structure in a unified coordinate system is output for subsequent spatial fusion.

5. The method for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion according to claim 1, characterized in that, The spatial fusion includes: The multi-scale digital three-dimensional structures located in the same structural unit of the shale oil reservoir's storage and permeability space are integrated under a unified coordinate system; combined with the geometric and topological feature parameters, the detail priority and weighted fusion method of the multi-scale digital three-dimensional structures in the structural unit of the shale oil reservoir's storage and permeability space are determined to generate a continuous and consistent three-dimensional spatial structure of the shale oil reservoir's storage and permeability space. The local parts of the three-dimensional spatial structure are optimized to enhance the integrity and connectivity of the overall three-dimensional spatial structure; then, a continuous and unified three-dimensional structural model of the shale oil reservoir's storage and permeation space is output.

6. The method for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion according to claim 1, characterized in that, The step of using the physical property data as constraints to perform physical consistency optimization and structural reconstruction of the three-dimensional structural model includes: Based on the three-dimensional structural model, the physical property data are mapped to the corresponding structural units of the shale oil reservoir's storage and permeability space, and physical consistency constraints are set. The physical consistency algorithm is used to locally adjust and iteratively optimize the structural units of the reservoir space of each shale oil reservoir. Under the premise of maintaining geometric and topological continuity, the physical properties of the three-dimensional structural model itself are kept consistent with the physical property data, thereby forming a three-dimensional structural model under the physical constraints.

7. The method for accurate characterization of shale oil reservoir permeability space based on multi-scale imaging fusion according to claim 1, characterized in that, The quantitative characterization includes: Geometric feature characterization, which includes the pore volume distribution and pore throat morphology parameters of the shale oil reservoir's storage and permeability space; Topological structure characterization, which includes the structural parameters of the pore-throat network of the shale oil reservoir's storage and permeability space and the connectivity index of the pore-throat network; The seepage characteristics are characterized by the complexity of the seepage path, the effective permeability, and the distribution of the seepage path in the shale oil reservoir's seepage space. Multi-scale comprehensive index, which includes scale coupling coefficient and structure-permeability coupling parameter.

8. A precise characterization system for the reservoir permeability space of shale oil reservoirs based on multi-scale imaging fusion, characterized in that, The system employs the method according to any one of claims 1-7, the system comprising: A multi-scale data acquisition module, which is used to acquire the multi-scale image and the physical property data; A multi-scale structure construction and feature extraction module is connected to the multi-scale data acquisition module. The multi-scale structure construction and feature extraction module is used to form the multi-scale digital three-dimensional structure and extract the geometric and topological feature parameters. Multi-scale spatial fusion module: The multi-scale spatial fusion module is connected to the multi-scale structure construction and feature extraction module, and the multi-scale spatial fusion module is used to generate the three-dimensional structure model; A physical constraint optimization module is connected to the multi-scale spatial fusion module and the multi-scale data acquisition module. The physical constraint optimization module is used to generate a three-dimensional structural model under the physical constraints. A multidimensional quantitative characterization and visualization module is connected to the physical constraint optimization module. The multidimensional quantitative characterization and visualization module is used to perform multidimensional quantitative characterization of the three-dimensional structural model under the physical constraints and output the three-dimensional visualization model.

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