Method, device and equipment for determining pore structure of oil reservoir, medium and product

Abstract

The invention discloses an oil reservoir pore structure determination method and device, equipment, a medium and a product. The method comprises the following steps: performing two-dimensional scanning imaging on an oil reservoir core by utilizing high-resolution X-ray CT, and constructing an initial pore network model according to an obtained CT cross-sectional image of the oil reservoir core; processing the oil reservoir core by adopting at least two micro-pore characterization experiments to obtain at least two kinds of nano-pore statistical information; newly-added pore nodes are generated according to the at least two kinds of nano-pore experimental data information and the initial pore network model, and the newly-added pore nodes and the initial pore network model are fused to obtain a target pore network model; and determining a pore structure chart of the oil reservoir core according to the target pore network model. According to the method, the initial pore network model is optimized through the multi-source nano-pore data obtained through experiments, supplementary modeling is carried out on nano-scale pores which cannot be recognized by CT, the precision of the target pore network model is improved, and multi-scale unification of pore structures is achieved.

Description

Technical Field

[0001] This invention relates to the field of reservoir development technology, and in particular to a method, apparatus, equipment, medium and product for determining the pore structure of an oil reservoir. Background Technology

[0002] Oil reservoir rocks (especially unconventional reservoirs such as low-permeability sandstone and shale) possess multi-scale pore structures, with pore sizes ranging from micrometers to nanometers. Obtaining the three-dimensional pore structure of rock cores using X-ray CT scanning is a crucial method for digital core modeling. CT imaging distinguishes pores from the matrix by thresholding grayscale values, providing a direct visual representation of the three-dimensional morphology of larger pores within the rock.

[0003] However, limited by the imaging resolution of CT, micron-scale CT can typically only distinguish larger pores and struggles to capture nanoscale micropores. For low-permeability to tight oil reservoir rocks containing numerous micro and nanopores, digital core models reconstructed solely by micron-scale CT cannot include nanopores. This results in the porosity and permeability of the constructed models being significantly lower than the actual values, making it difficult to effectively map statistical information onto manufacturable chip geometry. Consequently, it fails to meet the requirements of "realistic topology + consistent physical properties + manufacturability" for chip fabrication engineering implementation. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and product for determining the pore structure of an oil reservoir, in order to solve the problem of low accuracy in pore structure reconstruction based on CT imaging.

[0005] According to one aspect of the present invention, a method for determining the pore structure of an oil reservoir is provided, comprising: Two-dimensional scanning imaging of reservoir cores was performed using high-resolution X-ray CT, and an initial pore network model was constructed based on the obtained CT tomographic images of the reservoir cores. The reservoir core was processed using at least two micropore characterization experiments to obtain at least two types of nanopore statistical information; wherein, the at least two types of nanopore statistical information include at least nanopore distribution information and total porosity; New pore nodes are generated based on the at least two types of nanopore experimental data and the initial pore network model, and the new pore nodes are fused with the initial pore network model to obtain the target pore network model. The pore structure diagram of the reservoir core is determined based on the target pore network model.

[0006] According to another aspect of the present invention, an apparatus for determining the pore structure of an oil reservoir is provided, comprising: The initial pore network model module is used to perform two-dimensional scanning imaging of reservoir cores using high-resolution X-ray CT, and to construct an initial pore network model based on the obtained CT tomographic images of the reservoir cores. The micropore information determination module is used to process the reservoir core using at least two micropore characterization experiments to obtain at least two types of nanopore statistical information; wherein, the at least two types of nanopore statistical information include at least nanopore distribution information and total porosity; A pore fusion module is used to generate new pore nodes based on the at least two types of nanopore experimental data and the initial pore network model, and to fuse the new pore nodes with the initial pore network model to obtain a target pore network model. The pore structure diagram determination module is used to determine the pore structure diagram of the reservoir core based on the target pore network model.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for determining reservoir pore structure according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for determining the reservoir pore structure according to any embodiment of the present invention.

[0009] According to another aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method for determining reservoir pore structure as described in any embodiment of this application.

[0010] The technical solution of this invention optimizes the initial pore network model obtained by CT scan using multi-source nanopore data obtained from at least two micropore characterization experiments, and supplements the modeling of nanoscale pores that CT failed to identify, so that the porosity and permeability results of the target pore network model are closer to the actual values, thereby improving the accuracy of the target pore network model and achieving multi-scale unification of pore structure.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0013] Figure 1 This is a flowchart of a method for determining the pore structure of an oil reservoir according to an embodiment of the present invention; Figure 2 This is a flowchart of another method for determining the pore structure of an oil reservoir according to an embodiment of the present invention; Figure 3 This is a flowchart of another method for determining the pore structure of an oil reservoir according to an embodiment of the present invention; Figure 4 This is a comparison diagram of the initial pore network model and the target pore network model provided in the embodiments of the present invention; Figure 5 This is a schematic diagram of a device for determining the pore structure of an oil reservoir according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device for implementing the method for determining the reservoir pore structure according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "candidate," "target," etc., used 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 the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Figure 1 This invention provides a flowchart of a method for determining reservoir pore structure. This embodiment is applicable to optimizing pore structures constructed based on CT scan results. The method can be executed by a reservoir pore structure determination device, which can be implemented in hardware and / or software and can be configured in a server. Figure 1 As shown, the method includes: S110. High-resolution X-ray CT is used to perform two-dimensional scanning imaging of the reservoir core, and an initial pore network model is constructed based on the obtained CT tomographic images of the reservoir core.

[0017] Among them, high-resolution X-ray CT is a non-destructive three-dimensional imaging technology that obtains high-precision fault images of the internal structure of oil reservoir cores by penetrating the cores with X-rays and combining them with computer reconstruction. Oil reservoir cores refer to cylindrical rock samples drilled from underground oil and gas reservoirs and are used to study reservoir properties. CT tomographic images are a series of two-dimensional cross-sectional images obtained after CT scanning, reflecting the distribution of different density regions (such as mineral skeletons and pores) inside the core. The initial pore network model simplifies the pore space in CT tomographic images into a topological network composed of nodes (pore bodies) and throats (connecting channels) to simulate the seepage behavior of fluids in rocks.

[0018] Specifically, firstly, a high-resolution X-ray CT device is used to perform layer-by-layer two-dimensional scanning of the reservoir core sample to obtain a series of continuous CT tomographic images; then, the images are preprocessed (such as noise reduction and enhancement) and binarized (to distinguish between the solid skeleton and pore space); then, the geometric and topological information of pores and throats is extracted by image analysis algorithms (such as the maximum sphere algorithm or skeletonization method), and finally, an initial pore network model that can characterize the micro-pore structure of the core is constructed.

[0019] For example, a reservoir core sample is selected, and high-resolution X-ray CT is used to perform two-dimensional scanning imaging of the core sample to obtain a sequence of CT tomographic images of the internal structure of the core. The CT tomographic image sequence is preprocessed sequentially, including filtering and noise reduction, grayscale thresholding, and binarization of the image into pores and matrix. Noise reduction and removal of isolated specks are then performed to obtain a clear three-dimensional pore distribution. Representative volume units (REVs) are determined for the target core to ensure that the selected CT volumes have representative pore structure characteristics. Based on the processed two-dimensional binary core images, an initial pore network model is constructed using a pore network extraction algorithm (such as the watershed algorithm or skeleton extraction). This initial pore network model contains identifiable pores and throat connectivity within the CT resolution range, recording parameters such as pore radius, pore-throat radius, and connectivity. The initial pore network model reflects the larger pores and throat structures of the core, but due to limitations in CT imaging resolution, it lacks nanoscale pore information.

[0020] S120. At least two micropore characterization experiments were used to process the reservoir core to obtain at least two types of nanopore statistical information.

[0021] Micropore characterization experiments refer to physical or chemical analytical methods used to detect tiny pores (especially at the nanoscale) inside rocks. Nanopore statistics include quantitative parameters such as nanopore size distribution, specific surface area, pore volume, and pore throat connectivity. Nanopores typically target pores with diameters ranging from 1 to 1000 nanometers. Common micropore characterization experiments include gas adsorption methods (such as...). or Adsorption, which can measure micropores and mesopores, high pressure mercury intrusion porosimetry (MIP, applicable to mesopores to macropores), small angle X-ray scattering (SAXS) or focused ion beam scanning electron microscopy (FIB-SEM), etc.

[0022] Specifically, for the same reservoir core sample from step 110, at least two complementary micropore characterization experiments should be conducted (e.g., simultaneously conducting...). (Adsorption experiments and high-pressure mercury intrusion porosimetry experiments); each experiment acquires pore response signals based on different physical principles, and then obtains corresponding nanopore statistical information through theoretical model inversion; finally, multi-source data are integrated to obtain a more representative pore structure covering a wider pore size range. A comprehensive characterization of the nanoscale pore structure of reservoir cores is achieved through multiple microscopic experimental methods to obtain more comprehensive and reliable pore information. At least two complementary microscopic pore characterization experiments refer to two or more experimental methods with different principles, overlapping detection scales or sensitivity ranges, but complementing each other, to jointly characterize the micro- and nanoscale pore structures in rocks (such as reservoir cores). Since single experimental methods often have limitations in detection range, assumptions, or physical mechanisms, the fusion of multiple methods can obtain more complete and accurate pore information.

[0023] The statistical information on nanopores includes at least two types of information: nanopore distribution information and total porosity. Nanopore distribution information refers to the relationship between the number, volume, or surface area of ​​pores at the nanoscale and their size. For example, nanopore distribution information is a pore size distribution curve, reflecting the relative abundance and concentration range of micropores (<2nm), mesopores (2–50nm), and some macropores (50–1000nm). Total porosity refers to the percentage of the total volume of all pores (including nanopores, micropores, and fractures) in a rock sample. For example, total porosity refers to the effective porosity, including nanopores, measured by high-precision experiments (such as gas adsorption or small-angle scattering). Combining this with pore distribution information can further reveal the contribution ratio of nanopores to the total porosity, thus providing a more comprehensive assessment of the storage and flow characteristics of unconventional reservoirs such as tight oil and gas or shale.

[0024] S130. Generate new pore nodes based on at least two types of nanopore experimental data and the initial pore network model, and fuse the new pore nodes with the initial pore network model to obtain the target pore network model.

[0025] Among them, at least two types of nanopore experimental data information refer to the statistical results of nanopores obtained through complementary micropore characterization experiments, including quantitative information such as pore size distribution, specific surface area, and pore volume; the initial pore network model is a simplified topological structure composed of nodes (representing pore volumes) and throats (representing connecting channels) reconstructed from high-resolution X-ray CT images, but it is limited by CT resolution (usually only able to capture micron-sized and larger pores), and often lacks a large number of nano-sized pore details; the newly added pore nodes are virtual nodes representing nanopores generated in the model based on experimental data to make up for this deficiency; the target pore network model is a comprehensive network model that integrates CT-visible pores and experimentally inferred nanopores.

[0026] Specifically, the distribution characteristics of nanopores (such as pore volume density in different pore size ranges) are first extracted from micropore characterization experiments. Combined with rock mineral composition or local CT grayscale information, nanopores are allocated to specific regions of the initial pore network model (such as inside the matrix or around macropores) according to physical rationality. Then, new pore nodes with corresponding number, size and connectivity are generated according to statistical laws and connected to the original network in the initial pore network model through virtual throats. Finally, through topology verification and physical constraints (such as pore volume conservation and permeability matching), a target pore network model that can reflect both macroscopic pore structure and nanoscale reservoir space is formed, providing a foundation for accurately simulating fluid occurrence and multi-scale seepage behavior in unconventional reservoirs.

[0027] For example, an initial pore network model is first constructed using high-resolution X-ray CT scanning, containing only pores and cracks larger than 1 micrometer; subsequently, combined with... The 2–50 nm mesopore volume distribution obtained from adsorption experiments and The adsorption inversion data of micropores smaller than 2 nm were used to calculate the density and average size of nanopores per unit volume of rock. Then, these nanopore information were spatially distributed according to organic matter enrichment regions (identified by CT grayscale or energy dispersive spectroscopy analysis). A large number of new pore nodes representing nanopores were generated inside the matrix units of the initial pore network model and connected to the adjacent micron-sized pores through short throats. Finally, the new pore nodes were topologically fused with the original network, and the total porosity was corrected to be consistent with the experimentally measured total porosity, thus obtaining the target pore network model.

[0028] S140. Determine the pore structure diagram of the reservoir core based on the target pore network model.

[0029] By using visualization or structural mapping methods, the spatial distribution of pore nodes and throats in the target pore network model is transformed into an intuitive and analyzable pore structure diagram. The pore structure diagram includes the spatial distribution, connectivity, and geometric features of pores (such as pore size, throat length, and node location), and also reflects the combination patterns of pores at different scales (such as macropore-nanopore coupling structures), thus comprehensively characterizing the real multi-scale pore system inside the reservoir core.

[0030] For example, based on the spatial distribution of pore nodes and throats in the target pore network model, a corresponding mask CAD drawing is generated on a two-dimensional plane. Specifically, the solid rock portion corresponds to the solid micro-skeleton in the structure drawing, and the pore space corresponds to the connected flow channels in the structure drawing. Furthermore, when generating the structure drawing, the scale limitations and manufacturing processes of chip processing must be considered. Structures that are too deep or too fine are appropriately adjusted or enlarged. The resulting chip porous structure pattern serves as the pore structure drawing. This pore structure drawing can retain the geometric topological features of the real rock core pore network to the greatest extent, while simultaneously converting the optimized three-dimensional target pore network model into a structural design drawing suitable for microfluidic chip processing.

[0031] Furthermore, using microfabrication techniques such as photolithography, etching, or 3D printing, the pore structure map is fabricated onto transparent chip materials (such as silicon glass, PDMS, etc.) to form a microfluidic chip with a reservoir pore structure. After the chip is fabricated, the pore morphology of the chip can be checked under a microscope to see if it matches the design, and a flow test can be performed on the chip. For example, a displacement experiment can be conducted using the same fluid as the core sample to visually observe the flow behavior in the porous network and compare it with the experimental phenomena of real core samples to verify the effectiveness of the chip. If the chip is effective, reservoir-related experimental simulations can be performed based on the chip to improve the accuracy and visualization of reservoir experiments. In this embodiment, the distribution of high-velocity regions in the chip obtained by fabricating the target pore network model is more complex, and streamlines can flow in multiple directions, resulting in the coexistence of dominant flow paths and stagnant zones. This is closer to the microscopic oil displacement process in real reservoirs. That is, the chip structure in this embodiment can accurately simulate the spatial heterogeneity and pore connectivity characteristics inside the reservoir, greatly improving the credibility and practical value of microfluidic model experiments.

[0032] The technical solution of this embodiment optimizes the initial pore network model obtained by CT scan using multi-source nanopore data obtained from at least two micropore characterization experiments, and supplements the modeling of nanoscale pores that CT failed to identify, so that the porosity and permeability results of the target pore network model are closer to the actual values, thereby improving the accuracy of the target pore network model and achieving multi-scale unification of pore structure.

[0033] Figure 2 This is a flowchart illustrating another method for determining reservoir pore structure according to an embodiment of the present invention. This embodiment further refines the process of generating new pore nodes in the above embodiments. Figure 2 As shown, the method includes: S210. High-resolution X-ray CT is used to perform two-dimensional scanning imaging of reservoir cores, and an initial pore network model is constructed based on the obtained CT tomographic images of the reservoir cores.

[0034] S220. At least two micropore characterization experiments were used to process the reservoir core to obtain at least two types of nanopore statistical information.

[0035] Among them, at least two types of nanopore statistical information include nanopore distribution information and total porosity.

[0036] S230. Determine the initial model porosity based on the initial pore network model, and determine the pore information to be supplemented based on the total porosity and the initial model porosity.

[0037] Since the initial pore network model constructed based on high-resolution X-ray CT images can only reflect pores larger than micrometers, the initial model porosity determined based on the initial pore network model is usually lower than the actual value. This value is then compared with the total porosity obtained experimentally (containing pores of all scales), and the difference represents the pore information missing from CT capture. This difference serves as a key constraint to guide the subsequent reasonable addition of nanoscale pore nodes to the initial pore network model, ensuring that the final pore network model's porosity is consistent with the real rock core. By comparing the experimentally measured total porosity with the porosity calculated from the initial model, the missing nanopore information is quantified and supplemented, thereby improving the physical realism and simulation reliability of the model. The pore information to be supplemented includes information such as the total pore volume to be supplemented. For example, the difference between the experimentally measured total pore volume and the initial pore volume determined from the initial pore network model is used to determine the total pore volume to be supplemented, thus ensuring that the model's total porosity matches the measured value.

[0038] For example, in the initial pore network model, the connected pore space is abstracted into a series of nodes (representing pore volumes) and throats (representing connecting channels); each node is assigned an equivalent volume, and the sum of the volumes of all nodes is the total volume of identifiable pores in the initial pore network model; then, by dividing by the total volume of the reservoir core sample (calculated from the CT image size), the porosity of the initial model can be obtained.

[0039] S240. Based on the nanopore distribution information and the pore information to be supplemented, corresponding nanoscale pore nodes are generated at the solid matrix positions of the initial pore network model as new pore nodes.

[0040] Since X-ray CT imaging cannot distinguish nanoscale pores, the initial pore network model only contains pores larger than micrometers. The solid matrix region in the initial pore network model appears dense and non-porous in CT images, but is actually rich in nanopores. Therefore, by combining nanopore distribution information and information on pores to be supplemented, virtual nodes representing nanopores are generated within the solid matrix of the initial pore network model. These virtual nodes have sizes, numbers, and spatial locations that conform to experimental statistical laws, serving as new pore nodes. This transforms the dense matrix in the original pore network model into a porous medium containing reservoir nanopores, laying the foundation for constructing a full-scale pore network model.

[0041] Among them, the nanopore distribution information includes the pore size distribution information of nanopores, as well as the volume ratio information of different pore sizes, such as the pore size being concentrated in 2–50 nm and the volume ratio of pores of different sizes; the pore information to be supplemented includes the pore volume to be supplemented by 6%, which is mainly distributed in the organic matter enrichment area, etc.

[0042] For example, the initial X-ray CT reconstruction only identified cracks and pores larger than 2 micrometers, calculating the initial porosity of the pore network model to be 8%. However, gas adsorption experiments measured a total porosity of 16%, and other experiments revealed that 70% of the missing pore volume was concentrated in the 5–30 nm range, primarily located in organic-rich regions. Based on this information, the solid matrix blocks with lower grayscale values ​​in the CT images, corresponding to organic matter, were located in the initial pore network model. The required 8% pore volume (i.e., 0.08 cm³ of additional pore volume per cubic centimeter of core) was then added. 3 A large number of spherical pore nodes with diameters of 5–30 nm are randomly or according to a density gradient generated within the matrix region; each newly generated node is given a corresponding volume and connected to the adjacent micron-sized pores through a short throat to simulate the real structure of nanopores embedded in the matrix and connected through micropores. These newly generated nano nodes are called newly generated pore nodes.

[0043] S250. The newly added pore nodes are fused with the initial pore network model to obtain the target pore network model.

[0044] In one feasible embodiment, after fusing the newly added pore nodes with the initial pore network model, the method further includes: The physical properties of the model rock were calculated using digital core simulation technology after the fusion of pore network models; The results of comparing the physical properties of the model rocks with those of the experimental rocks in the reservoir core were determined. If they match, the fused pore network model is determined as the target pore network model. If there is a discrepancy, the parameters or connection methods of the newly added pore nodes are adjusted and the comparison is repeated iteratively until the target pore network model is determined.

[0045] For example, after integrating the newly added pore nodes into the initial pore network model, digital core technology (such as the lattice Boltzmann method, network simulation, or finite element analysis) is used to calculate the model rock physical properties of the fused model, such as at least one of permeability, porosity, relative permeability, or elastic modulus. Subsequently, the model rock physical properties are compared with the experimental rock physical properties measured from the same reservoir core sample. If the two are consistent within a reasonable error range, it indicates that the fused model can accurately reflect the pore structure and physical behavior of the real core, and it can be determined as the final target pore network model. If they are inconsistent, the key parameters of the newly added pore nodes need to be adjusted, such as pore size distribution, number, spatial location, or their connection method, such as throat length, connectivity, and whether they are set as dead ends. After the adjustment, the fusion and simulation are repeated, and through multiple iterations of optimization, the model rock physical properties match the experimental rock physical properties.

[0046] This embodiment significantly improves the accuracy of the target pore network model in reproducing the microstructure of real reservoir cores by integrating multi-scale pore information, introducing experimental constraints, and adopting an iterative verification mechanism. It not only makes up for the insufficient resolution of single imaging technology, but also ensures that the target pore network model is consistent with the experimental data in terms of geometric structure and physical response, thereby ensuring the geometric authenticity and physical reliability of the target pore network model.

[0047] S260. Determine the pore structure diagram of the reservoir core based on the target pore network model.

[0048] The technical solution in this embodiment effectively solves the problem of CT modeling's difficulty in capturing nanopores through multi-source data fusion. This allows the expanded target pore network model to simultaneously include micron-sized pores at CT resolution and nanopores not captured by CT, resulting in a more accurate match between the physical properties of the digital core model and reality. This significantly improves model accuracy and achieves multi-scale unification of pore structure. The microfluidic chip device designed based on this model more realistically reflects the reservoir pore structure than existing solutions, thus providing a precise and visualized experimental platform for studying the microscopic seepage mechanism of low-permeability shale reservoirs. Figure 3 This is a flowchart illustrating another method for determining reservoir pore structure according to an embodiment of the present invention. This embodiment further refines the process of generating new pore nodes in the above embodiments. Figure 3 As shown, the method includes: S310. High-resolution X-ray CT is used to perform two-dimensional scanning imaging of the reservoir core, and an initial pore network model is constructed based on the obtained CT tomographic images of the reservoir core.

[0050] S320. High-resolution scanning electron microscopy was used to analyze the reservoir core to obtain the distribution characteristics of nanopores.

[0051] High-resolution scanning electron microscopy (SEM) was used to observe the microscopic structure of reservoir core samples to obtain the spatial morphology and statistical regularities of nanoscale pores, which serve as the distribution characteristics of nanopores. These nanopore distribution characteristics include statistical and spatial descriptions of the number, size, shape, spatial location, and density of pores within the nanometer range. This typically includes information such as pore size frequency distribution, pore density (number of pores per unit area), pore morphology (e.g., circular, slit-like, irregular), and occurrence preferences in different mineral phases (e.g., organic matter, clay, quartz). For example, in field emission scanning electron microscopy (FE-SEM) images of reservoir cores, a large number of elliptical pores (50–300 nm) are densely distributed within organic matter, while slit-like pores with a width of approximately 10–50 nm are observed between clay mineral layers. By using image analysis software to identify and statistically analyze pores in multiple high-magnification SEM images, the average diameter of nanopores in organic matter can be quantified as 150 nm, and the pore surface density as 120 pores / μm. 2 Specific nanopore distribution characteristics, etc.

[0052] For example, small pieces or polished thin sections of rock samples are taken and observed using high-resolution scanning electron microscopy to examine the development of nanoscale pores, particularly the morphology and spatial distribution of organic matter pores and microcracks in shale, to obtain qualitative and quantitative information. Furthermore, focused ion beam-SEM (FIB-SEM) is used for three-dimensional reconstruction to provide local details of the nanoporous structure.

[0053] S330 and low-temperature nitrogen adsorption experiments were used to process reservoir cores to obtain information on the specific surface area, pore volume, and pore size distribution of nanopores.

[0054] Low-pressure nitrogen adsorption-desorption isotherms were determined from reservoir cores using low-temperature nitrogen adsorption experiments. The specific surface area, pore volume, and pore size distribution of micropores / mesopores were calculated using the BET and BJH methods. This experiment primarily characterizes the number and distribution characteristics of micropores in the diameter range of 2–100 nm.

[0055] For example, reservoir cores are exposed to a nitrogen environment at liquid nitrogen temperature (77 K), causing physical adsorption on the surface of their internal pores. The amount of nitrogen adsorbed under different relative pressures is measured and analyzed using theoretical models such as BET, t-plot, DFT, or BJH. This allows for the quantitative acquisition of three key parameters of the nanoscale pores (typically 0.35–500 nm) in the core, including specific surface area (the internal surface area of ​​pores per unit mass of rock), m³ / s. 2 / g; pore volume, which is the total volume of nanopores in a unit mass of rock, and pore size distribution, which is the proportion of volume or number of pores in different pore size ranges.

[0056] The S340 high-pressure pump experiment was used to process reservoir cores to obtain information on the pore size distribution, connectivity index and total porosity of nanopores.

[0057] High-pressure mercury intrusion experiments were conducted on reservoir cores to obtain pore size (throat radius) distribution curves, covering a range from several nanometers to tens of micrometers. Macroscopic parameters such as total porosity, pore-throat radius distribution, and connectivity of the rock can be obtained from the mercury intrusion data.

[0058] For example, reservoir core samples are placed in a vacuum environment, and then pressures of up to several hundred megapascals are gradually applied to force non-wetting liquid mercury to overcome surface tension and enter the rock pores. According to the Washburn equation, different pressures correspond to different pore throat diameters. The higher the pressure, the smaller the pores that mercury can enter, thus retrieving pore size distribution information from the micrometer to the nanometer scale (typically measurable to about 3–6 nm). At the same time, the cumulative volume of injected mercury can be converted into the total volume of the invaded pores, and then the total porosity reflecting the proportion of connected pore space can be calculated. In addition, by analyzing the hysteresis loop characteristics of the mercury ingress / regression curve, the adsorption efficiency, or the pore throat ratio, connectivity indicators characterizing the connection ability between pores (such as the proportion of connected pores, throat bottleneck effect, etc.) can also be obtained.

[0059] Through the above experiments, information such as the total pore volume ratio and size distribution of tiny pores that cannot be distinguished by CT was obtained. For example, SEM observation provides intuitive evidence of the morphology of nanopores, while N2 adsorption and MIP quantification give the porosity of small pores and the proportion of pores of different sizes. In this embodiment, the order of execution of steps 320, 330, and 340 is not limited, but to ensure the accuracy of the experimental results, all three experiments are performed on the same reservoir core.

[0060] S350. Determine the initial model porosity based on the initial pore network model, and determine the pore information to be supplemented based on the total porosity and the initial model porosity.

[0061] S360. Based on the pore size distribution information obtained from the low-temperature nitrogen adsorption experiment and the high-pressure pump experiment, a corresponding number of nanoscale pore nodes are generated.

[0062] Based on the pore size distribution obtained from the low-temperature nitrogen adsorption experiment and the high-pressure pump experiment, a corresponding number of nanoscale pore nodes were generated in the solid matrix of the initial pore network model.

[0063] For example, the micropore to mesopore (approximately 0.35–50 nm) distribution provided by nitrogen adsorption experiments is spliced ​​and merged with the mesopore to macropore (approximately 3 nm–100 μm) distribution covered by high-pressure mercury intrusion porosimetry experiments to form a more comprehensive full-pore size distribution curve covering a wider scale. Then, based on the pore volume or number density corresponding to each pore size range in this integrated distribution, and combined with the total pore volume to be supplemented, virtual nanoscale pore nodes of corresponding number, size, and proportion are generated in the solid matrix region of the initial pore network model according to statistical laws. For example, high-density small nodes are generated in the 2–10 nm range based on nitrogen adsorption data, and relatively fewer but larger nodes are generated in the 10–100 nm range based on mercury intrusion porosimetry data. This ensures that the newly added nodes are consistent with the experimental results in terms of scale distribution and total volume, providing a microstructural basis for constructing a high-fidelity multi-scale pore network model.

[0064] S370. Based on the nanopore distribution characteristics obtained from the high-resolution scanning electron microscopy analysis experiment, determine the position information of the nano-sized pore nodes in the solid matrix of the initial pore network model, and use it as the newly added pore nodes.

[0065] Based on the distribution characteristics provided by SEM, it is embedded into a suitable location in the matrix (e.g., an organic matter-rich area or near microcracks).

[0066] For example, since SEM images can visually display the actual location, aggregation region, and spatial morphology of nanopores within reservoir cores in different mineral phases (such as organic matter, clay, quartz, etc.), the spatial correspondence between nanopores and solid matrix region information is determined based on SEM images. According to this correspondence, within the corresponding matrix unit of the initial pore network model, such as the organic matter block with grayscale feature matching in CT images, the nanoscale pore nodes determined through nitrogen adsorption and mercury intrusion porosimetry experiments are rationally arranged according to the spatial correspondence revealed by SEM. For instance, more nodes are concentrated inside the organic matter rather than around quartz particles, or slit-like pores are oriented along the layered clay direction. This location information determined based on real microscopic images ensures that the newly generated pore nodes not only conform to experimental statistics in terms of quantity and size but also closely approximate the actual pore configuration of the rock in terms of spatial distribution, significantly improving the geological authenticity and physical representativeness of the pore network model.

[0067] S380. The newly added pore nodes are fused with the initial pore network model to obtain the target pore network model.

[0068] In one feasible embodiment, the newly added pore nodes are fused with the initial pore network model, including: Based on connectivity indices and nanopore distribution characteristics obtained from at least two micropore characterization experiments, the connection relationship between newly added pore nodes and the original pore network in the initial pore network model is established by adding cross-scale pore throats.

[0069] Based on pore-throat connectivity indices and nanopore spatial distribution characteristics obtained from at least two micropore characterization experiments, the connection possibilities and path characteristics between nanopores and macropores are determined. Building upon this, a cross-scale pore throat is introduced into the initial pore network model; this is a virtual throat connecting newly added nanoscale pore nodes with existing micrometer-scale pore nodes. Its size, number, and topology are set based on connectivity data from experiments (such as mercury removal efficiency and mercury ingress saturation) and pore adjacency relationships observed by SEM. For example, if mercury intrusion porosimetry experiments show poor connectivity and nanopores are mostly isolated within organic matter in SEM images, only a small number or unidirectional connections are established; conversely, if significant pore channels exist, the cross-scale throat density is increased. In this way, the originally isolated nanopores are integrated into the overall permeation network, constructing a pore structure model with realistic multi-scale connectivity characteristics.

[0070] This embodiment establishes the connectivity between newly added nanopore nodes and existing micropore nodes based on experimental results, thereby improving the accuracy of fusing multi-source data with the original pore network.

[0071] In one feasible embodiment, the connection between the newly added pore nodes and the original pore network in the initial pore network model is established by adding cross-scale pore throats, including: The connection relationship between the newly added pore nodes and the original pore network is established by using random simulation or statistical reconstruction algorithms.

[0072] Because of the extremely small size of nanopores, the throats between them and micron-sized pores cannot be clearly distinguished directly by microscopic pore characterization experiments such as SEM. Therefore, it is necessary to use stochastic simulation (such as Monte Carlo method) or statistical reconstruction algorithm (such as two-point correlation function, Markov random field or multi-point geostatistical method) to probabilistically generate cross-scale throats under the premise of satisfying experimental constraints (such as total connected pore volume, connectivity index, pore spatial distribution preference).

[0073] For example, after establishing direct connections between newly added pore nodes and the existing pore network based on connectivity indices and nanopore distribution characteristics obtained from at least two micropore characterization experiments, simulated connections between the newly added pore nodes and the existing pore network are established according to statistical laws based on the spatial location and size of the newly added pore nodes and the topology of the existing pore network using stochastic simulation or statistical reconstruction algorithms. These direct and simulated connections together constitute the overall connection between the newly added pore nodes and the existing pore network, ensuring that the generated connections conform to the actual seepage characteristics of the rock. For example, nanopores near macropores are preferentially designated as connected, while those deeply buried within the matrix may be designated as dead ends. The resulting connection network maintains both physical rationality and statistical representativeness, providing a reliable foundation for subsequent multi-scale seepage simulations.

[0074] S390. Determine the pore structure diagram of the reservoir core based on the target pore network model.

[0075] like Figure 4 The diagram shows a comparison between the initial pore network model and the target pore network model. For example, a low-permeability core sample was initially modeled using CT, with a model depth of 80 nm (as shown in the left figure). The model's porosity was only 4.87%, and its permeability was 0.0292 mD, while the actual measured porosity was 6.42%, a significant difference. After supplementing the nanopores using the method of this embodiment (as shown in the right figure), the model's porosity increased to 6.6%, and the permeability was 0.05 mD, almost identical to the measured values. Simultaneously, the pore connectivity in the model was significantly improved, and the tortuosity decreased, more accurately reflecting the internal flow path connectivity characteristics of the rock. These results verify the effectiveness of the method in this embodiment, demonstrating that the pore network model constructed after fusing multi-source scale pore data can accurately predict parameters such as rock porosity and permeability.

[0076] The technical solution in this embodiment, by fusing multi-source data such as CT scans, SEM, low-temperature nitrogen adsorption, and mercury intrusion porosimetry to obtain a target pore network model, can significantly improve the accuracy of digital core models in representing real pore structures. Furthermore, a microfluidic chip fabricated based on the optimized target pore network model realistically reproduces the porous media structure of the reservoir. Fluid flow experiments conducted on this chip show flow behavior and distribution characteristics similar to actual core samples, which helps promote the development of digital core and microfluidic experimental technologies for unconventional oil reservoirs and is of great significance for improving the fine characterization of oil reservoirs and optimizing development plans. Figure 5 This is a schematic diagram of a device for determining the pore structure of an oil reservoir, provided as an embodiment of the present invention. Figure 5 As shown, the device includes: The initial pore network model module 510 is used to perform two-dimensional scanning imaging of reservoir cores using high-resolution X-ray CT, and to construct an initial pore network model based on the obtained CT tomographic images of the reservoir cores. The micropore information determination module 520 is used to process reservoir cores using at least two micropore characterization experiments to obtain at least two types of nanopore statistical information; wherein, the at least two types of nanopore statistical information include at least nanopore distribution information and total porosity. The pore fusion module 530 is used to generate new pore nodes based on the at least two types of nanopore experimental data and the initial pore network model, and to fuse the new pore nodes with the initial pore network model to obtain a target pore network model. The pore structure diagram determination module 540 is used to determine the pore structure diagram of the reservoir core based on the target pore network model.

[0078] The technical solution of this embodiment optimizes the initial pore network model obtained by CT scan using multi-source nanopore data obtained from at least two micropore characterization experiments, and supplements the modeling of nanoscale pores that CT failed to identify, so that the porosity and permeability results of the target pore network model are closer to the actual values, thereby improving the accuracy of the target pore network model and achieving multi-scale unification of pore structure.

[0079] Optional, the pore fusion module includes: The unit for determining the pores to be supplemented is used to determine the initial model porosity based on the initial pore network model, and to determine the pore information to be supplemented based on the total porosity and the initial model porosity. The newly added node determination unit is used to generate corresponding nanoscale pore nodes at the solid matrix position of the initial pore network model based on the nanopore distribution information and the pore information to be supplemented, as newly added pore nodes.

[0080] Optionally, the at least two micropore characterization experiments include high-resolution scanning electron microscopy analysis, low-temperature nitrogen adsorption experiment, and high-pressure pump experiment. The micropore information determination module includes: The first experimental unit is used to process the reservoir core using the high-resolution scanning electron microscopy analysis experiment to obtain the nanopore distribution characteristics. The second experimental unit is used to process the reservoir core in the low-temperature nitrogen adsorption experiment to obtain information on the specific surface area, pore volume and pore size distribution of nanopores. The third experimental unit is used to process the reservoir core in the high-pressure pump experiment to obtain the pore size distribution information, connectivity index and total porosity of the nanopores.

[0081] Optionally, a new node determination unit may be added, including: Based on the pore size distribution information obtained from the low-temperature nitrogen adsorption experiment and the high-pressure pump experiment, a corresponding number of nanoscale pore nodes are generated. Based on the nanopore distribution characteristics obtained from the high-resolution scanning electron microscopy analysis experiment, the location information of the nanoscale pore nodes in the solid matrix of the initial pore network model is determined.

[0082] The pore fusion module includes a model fusion unit, used for: Based on the connectivity indices and nanopore distribution characteristics obtained from the at least two micropore characterization experiments, the connection relationship between the newly added pore nodes and the original pore network in the initial pore network model is established by adding cross-scale pore throats.

[0083] Optional, model fusion unit, specifically used for: The connection relationship between the newly added pore nodes and the original pore network is established using random simulation or statistical reconstruction algorithms.

[0084] Optionally, the device also includes a model verification module, used to calculate the model rock properties of the fused pore network model using digital core simulation technology after the newly added pore nodes are fused with the initial pore network model. The results of comparing the physical properties of the model rocks with the experimental rock properties of the reservoir core were determined. If they match, the fused pore network model is determined to be the target pore network model; If there is a discrepancy, the parameters or connection methods of the newly added pore nodes are adjusted and the comparison is repeated iteratively until the target pore network model is determined.

[0085] The reservoir pore structure determination device provided in this embodiment of the invention can execute the reservoir pore structure determination method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0086] The acquisition, storage, use, and processing of data in this application comply with relevant national laws and regulations and do not violate public order and good morals. According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0088] Figure 6A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0089] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0090] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0091] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods described above, such as methods for determining reservoir porosity structure.

[0092] In some embodiments, the method for determining reservoir porosity may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining reservoir porosity described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for determining reservoir porosity by any other suitable means (e.g., by means of firmware).

[0093] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific reference products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0095] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0096] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0097] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as data servers), or computing systems that include switching components (e.g., application servers), or computing systems that include front-end components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such back-end, switching, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0098] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0099] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0100] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the method for determining reservoir pore structure as provided in any embodiment of this application.

[0101] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0102] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0103] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the pore structure of an oil reservoir, characterized in that, The method includes: Two-dimensional scanning imaging of reservoir cores was performed using high-resolution X-ray CT, and an initial pore network model was constructed based on the obtained CT tomographic images of the reservoir cores. The reservoir core was processed using at least two micropore characterization experiments to obtain at least two types of nanopore statistical information; wherein, the at least two types of nanopore statistical information include at least nanopore distribution information and total porosity; New pore nodes are generated based on the at least two types of nanopore experimental data and the initial pore network model, and the new pore nodes are fused with the initial pore network model to obtain the target pore network model. The pore structure diagram of the reservoir core is determined based on the target pore network model.

2. The method according to claim 1, characterized in that, New pore nodes are generated based on the at least two types of nanopore experimental data and the initial pore network model, including: The initial model porosity is determined based on the initial pore network model, and the pore information to be supplemented is determined based on the total porosity and the initial model porosity. Based on the nanopore distribution information and the pore information to be supplemented, corresponding nanoscale pore nodes are generated at the solid matrix positions of the initial pore network model as newly added pore nodes.

3. The method according to claim 2, characterized in that, in, The at least two micropore characterization experiments include high-resolution scanning electron microscopy analysis, low-temperature nitrogen adsorption experiment, and high-pressure pump experiment. The reservoir core was processed using at least two micropore characterization experiments to obtain at least two types of nanopore statistical information, including: The reservoir core was processed using the high-resolution scanning electron microscopy analysis experiment to obtain the nanopore distribution characteristics; The low-temperature nitrogen adsorption experiment was used to process the reservoir core to obtain information on the specific surface area, pore volume, and pore size distribution of nanopores. The high-pressure pump experiment was used to process the reservoir core to obtain information on the pore size distribution, connectivity index and total porosity of the nanopores.

4. The method according to claim 3, characterized in that, Based on the nanopore distribution information and the pore information to be supplemented, corresponding nanoscale pore nodes are generated at the solid matrix locations of the initial pore network model, including: Based on the pore size distribution information obtained from the low-temperature nitrogen adsorption experiment and the high-pressure pump experiment, a corresponding number of nanoscale pore nodes are generated. Based on the nanopore distribution characteristics obtained from the high-resolution scanning electron microscopy analysis experiment, the location information of the nanoscale pore nodes in the solid matrix of the initial pore network model is determined.

5. The method according to claim 3, characterized in that, The process of fusing the newly added pore nodes with the initial pore network model includes: Based on the connectivity indices and nanopore distribution characteristics obtained from the at least two micropore characterization experiments, the connection relationship between the newly added pore nodes and the original pore network in the initial pore network model is established by adding cross-scale pore throats.

6. The method according to claim 5, characterized in that, The connection between the newly added pore nodes and the original pore network in the initial pore network model is established by adding cross-scale pore throats, including: The connection relationship between the newly added pore nodes and the original pore network is established using random simulation or statistical reconstruction algorithms.

7. The method according to claim 1, characterized in that, After fusing the newly added pore nodes with the initial pore network model, the method further includes: The physical properties of the model rock were calculated using digital core simulation technology after the fusion of pore network models; The results of comparing the physical properties of the model rocks with the experimental rock properties of the reservoir core were determined. If they match, the fused pore network model is determined to be the target pore network model; If there is a discrepancy, the parameters or connection methods of the newly added pore nodes are adjusted and the comparison is repeated iteratively until the target pore network model is determined.

8. A device for determining the pore structure of an oil reservoir, characterized in that, The device includes: The initial pore network model module is used to perform two-dimensional scanning imaging of reservoir cores using high-resolution X-ray CT, and to construct an initial pore network model based on the obtained CT tomographic images of the reservoir cores. The micropore information determination module is used to process the reservoir core using at least two micropore characterization experiments to obtain at least two types of nanopore statistical information; wherein, the at least two types of nanopore statistical information include at least nanopore distribution information and total porosity; A pore fusion module is used to generate new pore nodes based on the at least two types of nanopore experimental data and the initial pore network model, and to fuse the new pore nodes with the initial pore network model to obtain a target pore network model. The pore structure diagram determination module is used to determine the pore structure diagram of the reservoir core based on the target pore network model.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the reservoir pore structure according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the reservoir pore structure according to any one of claims 1-7.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for determining the reservoir pore structure according to any one of claims 1-7.

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