A pore structure parameter identification method, system, device, medium and program

Abstract

The application relates to a pore structure parameter identification method, system, device, storage medium and computer program, and the method comprises the following steps: acquiring well logging data, dividing the well logging data into conventional well logging data, electrical imaging well logging data and nuclear magnetic resonance well logging data; calculating a first pore structure parameter of pores according to the conventional well logging data; performing graphic segmentation on the electrical imaging well logging data to obtain a pore logging image, and calculating a second pore structure parameter according to the pore logging image; determining an optimal spatial configuration relationship representing pore space according to the nuclear magnetic resonance well logging data, and calculating a third pore structure parameter according to the optimal spatial configuration relationship; and collecting the first pore structure parameter, the second pore structure parameter and the third pore structure parameter to obtain a pore structure parameter corresponding to the well logging data. The application improves the accuracy of pore structure parameter identification.

Description

Technical Field

[0001] This application relates to the field of pore structure parameter technology, and in particular to a method, system, device, medium and program for identifying pore structure parameters. Background Technology

[0002] Oil and gas saturation is a key parameter for quantitative evaluation of reservoir logging. Due to the significant differences in electrical properties between oil and gas and formation water, saturation evaluation based on electrical properties has always been the main method for quantitative evaluation of well logging saturation. According to the development history of oil and gas exploration and combined with the characteristics of different types of reservoirs, oil and gas reservoirs are divided into two categories: (1) simple homogeneous reservoirs, mainly including medium-high porosity and permeability pure sandstone reservoirs and matrix-developed carbonate reservoirs; (2) complex heterogeneous reservoirs, mainly including argillaceous sandstone reservoirs and reservoirs with complex pore structures. For conventional reservoirs such as pure sandstone and matrix-developed carbonate rocks, Archie's first and second formulas are commonly used to evaluate their water saturation. Due to the occurrence of the "non-Archie" phenomenon in complex pore structure reservoirs caused by pore structure, the development of saturation models based on pore structure has become inevitable. Since there are multiple reservoir spaces such as matrix pores, dissolution cavities and fractures in tight sandstone and carbonate rocks, the study of complex reservoir saturation models based on the conductivity of multiple porous media has become necessary.

[0003] Existing technologies propose a dual-porosity model based on pore structure. This model posits that rock conductivity is formed by the parallel connection of macropores and micropores. Macropores contain movable fluid space, while micropores contain bound fluid space. Oil and gas only enter the movable fluid space, while the bound fluid pore space contains immovable water. This model separates the smaller micropores from the porous medium based on factors such as pore morphology, size, and conductivity, treating macropore and micropore conductivity differently. However, this model suffers from several problems: too many model parameters, a lack of methods for obtaining these parameters, difficulty in solving for model parameters using actual well logging data, and the fact that it only considers pore volume parameters without considering pore structure parameters. Therefore, accurately calculating the pore structure parameters of the saturation model is a pressing issue that needs to be addressed. Summary of the Invention

[0004] This application provides a method, system, device, medium, and program for identifying pore structure parameters to solve the problem of poor accuracy in identifying pore structure parameters.

[0005] In a first aspect, this application provides a method for identifying pore structure parameters, including:

[0006] Acquire well logging data and classify the well logging data into conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance well logging data;

[0007] Calculate the first pore structure parameters of the pores based on the conventional well logging data;

[0008] The electrical imaging logging data is image segmented to obtain a pore logging image, and the second pore structure parameters are calculated based on the pore logging image.

[0009] The optimal spatial configuration relationship characterizing the pore space is determined based on the nuclear magnetic resonance logging data, and the third pore structure parameters are calculated based on the optimal spatial configuration relationship.

[0010] By combining the first pore structure parameters, the second pore structure parameters, and the third pore structure parameters, the pore structure parameters corresponding to the well logging data are obtained.

[0011] In some embodiments, calculating the first pore structure parameter of the pores based on the conventional logging data includes:

[0012] Calculate the formation density logging porosity, neutron logging porosity, and primary intergranular porosity based on the conventional logging data.

[0013] The following formulas are used to calculate the formation density logging porosity, neutron logging porosity, and primary intergranular porosity:

[0014]

[0015] Where, φ D ρ represents density logging porosity. ma ρ represents the density value of the rock skeleton in conventional well logging data. b ρ represents the density value measured by density logging in conventional well logging data. f φ represents the density value of pore fluid in conventional well logging data. N Φ represents the porosity in neutron logging. ma Φ f Φ N φ represents the hydrogen content index measured in conventional well logging data, specifically the rock skeleton, pore fluid, and neutron logging data. S Indicates the primary intergranular porosity, Δt ma Δt f Δt and Δt represent the time differences of rock skeleton, pore fluid and logging time difference measurements in conventional logging data, respectively;

[0016] The total porosity is calculated based on the neutron logging porosity and the density logging porosity.

[0017] The total porosity is calculated using the following formula:

[0018]

[0019] Where, φ t φ represents the total porosity. D φ represents density logging porosity.N Indicates neutron logging porosity;

[0020] The porosity of the matrix and the fracture porosity are calculated based on the total porosity and the primary intergranular porosity.

[0021] The porosity of the matrix porosity and fracture porosity is calculated using the following formula:

[0022] φ f =φ t -φ s ,φ b =φ s

[0023] Where, φ f φ represents the porosity of the crack pores. t φ represents the total porosity. S Indicates the primary intergranular porosity, φ b Porosity indicates the porosity of the matrix.

[0024] The first pore structure parameter is obtained by combining the total porosity, the matrix porosity, and the crack porosity.

[0025] In some embodiments, the step of performing graphic segmentation on the electrical imaging logging data to obtain a porosity logging image includes:

[0026] Calculate the grayscale histogram of each logging image in the electrical imaging logging data;

[0027] The threshold for maximizing the inter-class variance is calculated based on the gray-level histogram, and the well logging image is segmented using the maximum inter-class difference method based on the threshold to obtain a segmented image;

[0028] Median filtering is applied to the segmented image to obtain a porosity logging image.

[0029] In some embodiments, calculating the second pore structure parameters based on the pore logging image includes:

[0030] Edge detection is performed on the porosity logging images to obtain the range of fracture curves and the range of dissolution cavities;

[0031] Calculate the crack structure parameters based on the crack curve range;

[0032] The structural parameters of the crack are calculated using the following formula:

[0033] W = CAR m b R xo (1-b)

[0034]

[0035] Where W represents the crack width, C represents the preset first instrument structure parameter, A represents the abnormal current area, and R... m R represents the resistivity of the mud. xo The value represents the resistivity of the intrusion band, b represents the preset second instrument structure parameter, and V. e Z0 represents the plate potential value, and I represents the basic half-width. a I represents the electrode current. b The electrode current represents the undisturbed formation or framework, z represents the displacement perpendicular to the fracture trajectory within the fracture curve range, and φ represents the current of the electrode in the undisturbed formation or framework. f W represents the porosity of the crack. i L represents the width of the i-th crack. i τ represents the length of the i-th fracture within a preset statistical window length L, D represents the well diameter in the logging data, and τ wfp Indicates crack tortuosity, qng represents crack dip angle, Rad wfp Indicates the crack radius;

[0036] Calculate the structural parameters of the dissolution cavities based on the range of the dissolution cavities;

[0037] By combining the crack structure parameters and the dissolution cavity structure parameters, the second pore structure parameters are obtained.

[0038] In some embodiments, determining the optimal spatial configuration relationship characterizing the pore space based on the nuclear magnetic resonance logging data includes:

[0039] Based on the transverse relaxation time spectrum in the nuclear magnetic resonance logging data, the number of model groups for the pore structure model of the PV tube representing the pore space is determined.

[0040] Calculate the configuration relationship between the spherical pores and the tubular throat in the spherical tube pore structure model based on the number of model groups.

[0041] Calculate the echo train representing the pore space under each configuration relationship, and determine the optimal configuration relationship based on the echo train.

[0042] In some embodiments, calculating the third pore structure parameters based on the optimal spatial configuration relationship includes:

[0043] Calculate the arithmetic mean and geometric mean of the sphere pore time spectrum and the tubular throat time spectrum based on the optimal configuration relationship;

[0044] The radius and tortuosity of the spherical pores and tubular throats are calculated based on the arithmetic mean and the geometric mean.

[0045] The radius and tortuosity of the spherical pores and tubular throats are calculated using the formulas shown below:

[0046]

[0047] Among them, Rad wp The radius of the pores in the sphere is represented by ρ, and the surface relaxation rate is represented by T. 2SAM τ represents the arithmetic mean of the pore size of a sphere. wp T represents the tortuosity of the pores in a sphere. 2SGM Rad represents the geometric mean of the porosity of a sphere. wt The radius of the tubular throat is represented by ρ, the relaxation rate of the core surface is represented by T. 2CAM τ represents the arithmetic mean of the tubular larynx. wt T represents the tortuosity of the tubular larynx. 2CGM Represents the geometric mean of the tubular throat;

[0048] The radius and tortuosity of the spherical pores and the radius and tortuosity of the tubular throat are combined to obtain the third pore structure parameters.

[0049] Secondly, this application provides a pore structure parameter identification device, comprising:

[0050] The well logging data segmentation module is used to acquire well logging data and segment the well logging data into conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance well logging data.

[0051] The first pore structure parameter calculation module is used to calculate the first pore structure parameters of the pores based on the conventional logging data.

[0052] The second pore structure parameter calculation module is used to perform graphic segmentation on the electrical imaging logging data to obtain a pore logging image, and to calculate the second pore structure parameters based on the pore logging image.

[0053] The third pore structure parameter calculation module is used to determine the optimal spatial configuration relationship characterizing the pore space based on the nuclear magnetic resonance logging data, and to calculate the third pore structure parameters based on the optimal spatial configuration relationship.

[0054] The pore structure parameter collection module is used to collect the first pore structure parameter, the second pore structure parameter and the third pore structure parameter to obtain the pore structure parameter corresponding to the well logging data.

[0055] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0056] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the above aspects.

[0057] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described above.

[0058] This application provides a method, system, device, medium, and program for identifying pore structure parameters. By dividing well logging data into conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance well logging data, it can calculate the first pore structure parameter corresponding to conventional well logging data, the second pore structure parameter corresponding to electrical imaging well logging data, and the third pore structure parameter corresponding to nuclear magnetic resonance well logging data, respectively. Therefore, pore structure parameters can be considered in various well logging data, improving the accuracy of pore structure parameter calculation for reservoir saturation models corresponding to well logging data. Attached Figure Description

[0059] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0060] Figure 1 A flowchart illustrating a method for identifying pore structure parameters provided in an embodiment of this application;

[0061] Figure 2 A schematic diagram of a pore structure model of a X-ray tube provided in an embodiment of this application;

[0062] Figure 3 A functional module diagram of a pore structure parameter identification device provided in an embodiment of this application;

[0063] Figure 4 This is a schematic diagram of an electronic device for a pore structure parameter identification method provided in an embodiment of this application.

[0064] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0065] To enable those skilled in the art to better understand the technical solutions of this application, and to fully understand and implement the process of how this application uses technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. The embodiments of this application and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this application.

[0066] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 this application 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 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.

[0067] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0068] This application provides a method for identifying pore structure parameters. The executing entity of this pore structure parameter identification method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the system provided in this application: a server, a terminal, etc. In other words, the pore structure parameter identification method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0069] Example One

[0070] Figure 1 This is a flowchart illustrating a method for identifying pore structure parameters provided in an embodiment of this application, as shown below. Figure 1 As shown, the method for identifying pore structure parameters includes:

[0071] S1. Obtain well logging data and classify the well logging data into conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance well logging data.

[0072] In one embodiment, well logging data refers to data and information about the characteristics of underground reservoirs obtained through well logging techniques during oil and gas exploration and development. This data is crucial for understanding and assessing the reservoir's rock physical properties, fluid properties, and connectivity.

[0073] In detail, well logging data can include conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance (NMR) well logging data. Conventional well logging data mainly refers to the well logging methods used in oil and gas exploration and development, including logging in exploration wells, appraisal wells, and development wells. Electrical imaging well logging data is obtained by using sensor array scanning or rotational scanning downhole to collect a large amount of formation information along the longitudinal, circumferential, or radial direction of the wellbore. After being transmitted to the surface, image processing technology is used to obtain two-dimensional images of the wellbore or three-dimensional images of the area around the wellbore within a certain detection depth. Through electrical imaging images and formation dip angles, various formation interfaces, fractures, and structural morphologies can be intuitively and qualitatively identified. Nuclear magnetic resonance (NMR) well logging data is a geological logging technique that uses the principle of nuclear magnetic resonance to perform non-invasive measurements on underground rocks. It can provide three types of information: fluid content, fluid characteristics, pore size, and porosity, providing richer formation geological data.

[0074] In one embodiment, dividing the well logging data into conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance well logging data includes:

[0075] Identify the data source of the well logging data;

[0076] Based on the data sources, the logging data are classified into conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data.

[0077] In one embodiment, conventional logging data includes curve measurement methods such as natural gamma, spontaneous potential, and borehole diameter three-lithology curves, shallow, medium, and deep three-resistivity curves, and sonic, neutron, and density three-porosity curves. Electrical imaging logging data is acquired through electrical imaging technology, and nuclear magnetic resonance logging data is acquired through nuclear magnetic resonance technology. Therefore, conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data can be classified according to their data sources. Corresponding pore structure parameters can be calculated based on different logging data to improve the accuracy of pore structure parameter calculation.

[0078] S2. Calculate the first pore structure parameters of the pores based on the conventional logging data.

[0079] In one embodiment, three-porosity logging data from conventional well logging can provide formation porosity information. The total porosity, matrix porosity, and fracture porosity can be calculated from conventional well logging data as the first pore structure parameter.

[0080] In one embodiment, calculating the first pore structure parameter of the pores based on the conventional well logging data includes:

[0081] Calculate the formation density logging porosity, neutron logging porosity, and primary intergranular porosity based on the conventional logging data.

[0082] The total porosity is calculated based on the neutron logging porosity and the density logging porosity.

[0083] The porosity of the matrix and the fracture porosity are calculated based on the total porosity and the primary intergranular porosity.

[0084] The first pore structure parameter is obtained by combining the total porosity, the matrix porosity, and the crack porosity.

[0085] In one embodiment, the three-porosity logging data in conventional logging data can provide formation porosity information, wherein neutron logging porosity and density logging porosity reflect the total formation porosity, while sonic velocity logging mainly reflects the primary intergranular porosity.

[0086] In detail, the formation density logging porosity, neutron logging porosity, and primary intergranular porosity are calculated using the following formulas:

[0087]

[0088] Where, φ D ρ represents density logging porosity. ma ρ represents the density value of the rock skeleton in conventional well logging data. b ρ represents the density value measured by density logging in conventional well logging data. fφ represents the density value of pore fluid in conventional well logging data. N Φ represents the porosity in neutron logging. ma Φ f Φ N φ represents the hydrogen content index measured in conventional well logging data, specifically the rock skeleton, pore fluid, and neutron logging data. S Indicates the primary intergranular porosity, Δt ma Δt f Δt and Δt represent the time differences of rock skeleton, pore fluid, and logging time difference measurements in conventional logging data, respectively.

[0089] In one embodiment, the total porosity is calculated using the following formula:

[0090]

[0091] Where, φ t φ represents the total porosity. D φ represents density logging porosity. N This indicates the porosity of neutron logging.

[0092] Furthermore, the porosity of the matrix porosity and the fracture porosity are calculated using the following formula:

[0093] φ f =φ t -φ s ,φ b =φ s

[0094] Where, φ f φ represents the porosity of the crack pores. t φ represents the total porosity. S Indicates the primary intergranular porosity, φ b This indicates the porosity of the matrix pores.

[0095] In one embodiment, by aggregating the total porosity, the matrix porosity, and the fracture porosity, the porosity of different pore types can be calculated, thereby improving the accuracy and comprehensiveness of pore structure parameter calculation.

[0096] S3. Perform graphic segmentation on the electrical imaging logging data to obtain a pore logging image, and calculate the second pore structure parameters based on the pore logging image.

[0097] In one embodiment, since electrical imaging logging data is obtained by using sensor array scanning or rotational scanning measurements downhole to collect a large amount of formation information along the longitudinal, circumferential, or radial direction of the wellbore, and then transmitting it to the surface, image processing technology is used to obtain a two-dimensional image of the wellbore or a three-dimensional image within a certain detection depth around the wellbore. Generally speaking, in electrical imaging logging data, the high-resistivity rock skeleton appears as a bright color, while formation water exists in dissolution pores and fracture pores, which have low resistivity and appear as a dark color in the electrical imaging data. Fractures are imaged as dark sinusoidal curves, and dissolution pores appear as irregular clumps, spots, or near-circular shapes in the electrical imaging logging data. Therefore, image segmentation can be performed on the electrical imaging logging data to identify pore logging images that effectively represent the pore, fracture, and cavity parts of the rock in the electrical imaging logging data.

[0098] In one embodiment, the step of performing graphic segmentation on the electrical imaging logging data to obtain a porosity logging image includes:

[0099] Calculate the grayscale histogram of each logging image in the electrical imaging logging data;

[0100] The threshold for maximizing the inter-class variance is calculated based on the gray-level histogram, and the well logging image is segmented using the maximum inter-class difference method based on the threshold to obtain a segmented image;

[0101] Median filtering is applied to the segmented image to obtain a porosity logging image.

[0102] In one embodiment, the inter-class variance of the foreground and background regions at each threshold can be calculated using a grayscale histogram. By iterating through all possible thresholds, the threshold that maximizes the inter-class variance is found. This threshold segments the image into foreground and background, and the foreground and background are used as the segmented image.

[0103] Furthermore, since the Otsu's inter-class difference segmentation algorithm is quite sensitive to noise and uneven illumination, it may lead to poor segmentation results. Therefore, median filtering can be used to filter and eliminate noise to obtain more accurate porosity logging images.

[0104] In one embodiment, the second pore structure parameters include the porosity of cracks and dissolution cavities, as well as the radius and tortuosity of cracks.

[0105] Specifically, the step of calculating the second pore structure parameters based on the pore logging image includes:

[0106] Edge detection is performed on the porosity logging images to obtain the range of fracture curves and the range of dissolution cavities;

[0107] Calculate the crack structure parameters based on the crack curve range;

[0108] Calculate the structural parameters of the dissolution cavities based on the range of the dissolution cavities;

[0109] By combining the crack structure parameters and the dissolution cavity structure parameters, the second pore structure parameters are obtained.

[0110] In one embodiment, the black sine curve in the electro-imaging data is regarded as an open fracture. The range of the black sine curve is delineated by edge detection, and the fracture width (aperture) is calculated. At the same time, the fracture in the porosity logging image can be identified by the electro-imaging logging software ciflog, and the dip angle and dip direction of the fracture can be directly obtained. Then, the fracture porosity, radius and tortuosity can be calculated to obtain the fracture structure parameters.

[0111] In one embodiment, the crack structure parameters are calculated using the following formula:

[0112] W = CAR m b R xo (1-b)

[0113]

[0114] Where W represents the crack width, C represents the preset first instrument structure parameter, A represents the abnormal current area, and R... m R represents the resistivity of the mud. xo The value represents the resistivity of the intrusion band, b represents the preset second instrument structure parameter, and V. e Z0 represents the plate potential value, and I represents the basic half-width. a I represents the electrode current. b The electrode current represents the undisturbed formation or framework, z represents the displacement perpendicular to the fracture trajectory within the fracture curve range, and φ represents the current of the electrode in the undisturbed formation or framework. f W represents the porosity of the crack. i L represents the width of the i-th crack. i τ represents the length of the i-th fracture within a preset statistical window length L, D represents the well diameter in the logging data, and τ wfp Indicates crack tortuosity, qng represents crack dip angle, Rad wfp Indicates the crack radius.

[0115] In detail, the first instrument structural parameter can be a parameter related to the instrument when acquiring electrical imaging logging data. For example, the first instrument structural parameter can be 0.004801, and the second instrument structural parameter can be 0.863, which is related to the instrument's own structure. Simultaneously, the instrument can measure parameters such as mud resistivity, invasion zone resistivity, and electrode current of the reservoir corresponding to the electrical imaging logging data. For example, the electrode current I... a It is a function of the vertical position z of the instrument when it crosses the crack.

[0116] In one embodiment, for irregular clumps, spots, or near-circular dissolution holes, edge detection delineates the distribution range of the dissolution holes, and point statistics are used to measure the segmented dissolution hole image portion. The number of segmented dissolution holes and the number of pixels in each dissolution hole are counted to obtain the porosity of the dissolution holes. In this application, the porosity of the dissolution holes is used as the structural parameter of the dissolution holes.

[0117] Specifically, the structural parameters of the dissolution pores are calculated using the following formula:

[0118]

[0119] Where, φ v VP represents the structural parameters of the dissolution pores. e denoted by , represents the number of pixels representing the pores in the e-th dissolution hole within the dissolution hole range, N represents the total number of dissolution holes within the dissolution hole range, and TP represents the total number of points within the dissolution hole range.

[0120] In one embodiment, the second pore structure parameter can be used to calculate the pore structure parameters when the logging data is electrical imaging logging data, thereby improving the universality and accuracy of the pore structure parameter calculation.

[0121] S4. Determine the optimal spatial configuration relationship characterizing the pore space based on the nuclear magnetic resonance logging data, and calculate the third pore structure parameters based on the optimal spatial configuration relationship.

[0122] In one embodiment, the maximum sphere method is used to extract a pore network model representing the pore space. The basic unit of the pore network model is a sphere-tube pore structure model. The length of the sphere-tube pore structure model is twice the equivalent pore radius Rade. The sphere-tube pore structure model includes two parts: spherical pores and tubular throats. The radius of the spherical pores is Rads, and the radius of the tubular throat is Radc (e.g., Radc). Figure 2 As shown in the figure, Cd represents the ratio of the tubular throat radius to the pore radius of the sphere in the sphere pore structure model, reflecting the spatial configuration relationship of the sphere pore structure model; the pore surface area of ​​the sphere is SFs, the surface area of ​​the tubular throat is SFc, and the total pore space surface area of ​​the sphere pore structure model is SFt.

[0123] Furthermore, the pore radius Rad of the sphere in the sphere-tube pore structure model can be given. s With equivalent pore radius Rad e The relationship is as follows:

[0124]

[0125] Wherein, Cs represents the relationship between the average radius of the grouped pores and the radius of the spherical pores, and is generally taken as 3;

[0126] The relationship Cd between the radius of the tubular throat and the radius of the sphere pores in the given sphere tube pore structure model is as follows:

[0127]

[0128] Among them, Rad c Rad represents the radius of the tubular throat. s Indicates the radius of the pores in the sphere;

[0129] Furthermore, the surface areas of the tubular throat and the pores of the sphere in the given sphere tube pore structure model are shown below:

[0130] SF c =π×Rad c 2 ×(Rad e -Rad c ),(3)

[0131] SF s =4π×Rad s 2 (4)

[0132] Among them, SF c Rad represents the surface area of ​​the tubular larynx. c Rad represents the radius of the tubular throat. e SF represents the equivalent pore radius. s Rad represents the pore surface area of ​​a sphere. s Indicates the radius of the pores in the sphere;

[0133] The total surface area of ​​the pore space in the X-ray tube pore structure model is shown below:

[0134] SF t =SF s +SF c (5)

[0135] Among them, SF t SF represents the total surface area of ​​the pore space. c SF represents the surface area of ​​the tubular larynx. s This represents the surface area of ​​the pores in a sphere.

[0136] Furthermore, the pore space of rocks can be characterized by multiple sets of sphere-tube pore structure models with different equivalent pore radii.

[0137] In detail, determining the optimal spatial configuration relationship characterizing the pore space based on the nuclear magnetic resonance logging data includes:

[0138] Based on the transverse relaxation time spectrum in the nuclear magnetic resonance logging data, the number of model groups for the pore structure model of the PV tube representing the pore space is determined.

[0139] Calculate the configuration relationship between the spherical pores and the tubular throat in the spherical tube pore structure model based on the number of model groups.

[0140] Calculate the echo train representing the pore space under each configuration relationship, and determine the optimal configuration relationship based on the echo train.

[0141] In one embodiment, the transverse relaxation time spectrum (NMR T2 spectrum) consists of multiple relaxation times and their amplitudes, i.e.

[0142] T2=(T 2y Am y ),(y=1,2,…,…),(6)

[0143] In the formula, T 2y Am represents the y-th relaxation time or the y-th placement point value in the T2 spectrum. y Let y be the spectral amplitude corresponding to the y-th relaxation time.

[0144] Furthermore, based on the NMR T2 spectrum, the pore space can be characterized as n sets of X-ray tube pore structure models with different equivalent pore radii. The quantitative relationship between the equivalent pore radius and relaxation time of the X-ray tube pore structure model is as follows:

[0145] Rad ei =3ρT 2y (7)

[0146] Among them, Rad ei T represents the equivalent pore radius. 2y Let y represent the y-th relaxation time in the T2 spectrum, and T represent the surface relaxation rate.

[0147] Furthermore, the configuration relationships between the spherical pores and tubular throats in all groups of PV tube pore structure models are enumerated. For example, the configuration relationship for the i-th group of PV tube pore structure models is as follows:

[0148]

[0149] In the formula, Rad ci Rad represents the radius of the tubular throat in the i-th group of PV tube pore structure models. si Cd represents the radius of the spherical pores in the i-th group of spherical tube pore structure models. i It represents the ratio of the tubular throat radius to the pore radius of the sphere in the i-th group of sphere-tube pore structure models.

[0150] Will Cd iDiscretize into 6 values.

[0151]

[0152] Therefore, for n sets of X-ray tube pore structure models, there are 6 possible configuration relationships for each set of X-ray tube pore structure models. Thus, there are a total of 6 possible configuration relationships for X-ray tube pore structure models across all sets. n indivual.

[0153] Based on the complete configuration relationships between spherical pores and tubular throats in all groups of spherical tube pore structure models, the NMR T2 spectrum is enumerated and decomposed into T2 spectra T representing the spherical pores. 2s and the T2 NMR spectrum characterizing the tubular larynx 2c .

[0154] For all groups of sphere-tube pore structure models, the configuration of sphere pores and tubular throat structures is 6 n The k-th configuration relation (Cd) in the relation 1k Cd 2k ,…,Cd ik ,…,Cd nk )(i=1,2,…,n;k=1,2,…,6 n For example, it can be represented as:

[0155] 1. Calculate the size of the spherical pores and the size of the tubular throat in each group of spherical tube pore structure models under the k-th configuration relationship, and back-calculate the relaxation time T of the spherical pores. 2sik and the relaxation time T of the tubular larynx 2cik Based on T2 relaxation time T 2y The equivalent pore radius Rad for each group of PV tube pore structure models is calculated using formula (7). ei Based on the configuration relationship Cd ik Formulas (1) and (2) are used to calculate the sphere pore radius Rad of the i-th group of sphere tube pore structure models. sik and the radius of the tubular larynx Rad cik ;

[0156] Based on the pore radius Radsik of the sphere and the radius Rad of the tubular throat cik Based on formula (7), the nuclear magnetic relaxation time T corresponding to the spherical pores and tubular throats is calculated. 2sik and T 2cik As shown below:

[0157]

[0158] Among them, T 2sik T 2cikRad represents the T2 relaxation time corresponding to the sphere pores and tubular throats in the i-th group of the sphere-tube pore structure model under the current k-th configuration relationship. sik Rad cik ρ represents the radius of the sphere pores and tubular throat in the i-th group of sphere-tube pore structure models under the current configuration relationship, and ρ represents the surface relaxation rate.

[0159] 2. Calculate the surface area of ​​the sphere pores and the surface area of ​​the tubular throat in each group of sphere-tube pore structure models under the current k-th configuration relationship. Calculate the surface area SF of the tubular throat in the i-th group of sphere-tube pore structures according to formulas (3), (4) and (5). sik Surface area of ​​sphere pores SF sik and the total surface area of ​​the pore space of the X-ray tube SF tik ;

[0160] 3. Calculate the volume of the spherical pores and the volume of the tubular throat in each group of spherical tube pore structure models under the current k-th configuration relationship, based on the relaxation time T in the NMR T2 spectrum. 2y amplitude Am y The porosity Am of the sphere under the current k-th configuration relationship is calculated based on the surface area ratio method. sik With the volume Am of the tubular larynx cik :

[0161]

[0162] In the formula, Am i SF represents the spectral amplitude corresponding to the relaxation time in the T2 NMR spectrum. cik SF sik SF tik These represent the surface areas of the tubular throat, the pores of the sphere, and the total surface area of ​​the pore space in the i-th group of the sphere-tube pore structure model under the current k-th configuration relationship;

[0163] 4. Obtain the NMR T2 spectrum representing the porosity of the sphere under the current k-th configuration relationship. 2sk and characterization of the tubular laryngeal tract T2 NMR spectrum 2ck .

[0164] Rad of the sphere pores in all groups of tube models calculated in steps 1 and 3 under the current k-th configuration relationship. sik With amplitude Am sik and the radius of the tubular larynx Rad cik With amplitude Am cik The spherical pore spectrum and the tubular throat spectrum were constructed:

[0165] T 2sk =(T 2sik Am sik), (i=1,2,…,n; k=1,2,…,6 n ),(14),

[0166] T 2ck =(T 2cik Am cik ), (i=1,2,…,n; k=1,2,…,6 n ),(15),

[0167] In one embodiment, the NMR T2 spectrum characterizing the spherical pores is obtained based on the configuration relationship between the spherical pores and the tubular throat in all X-ray tube pore structure models. 2sk and characterization of the tubular laryngeal tract T2 NMR spectrum 2ck And inversely calculate the echo train that characterizes the pore space.

[0168] For all groups of ball tube pore structure models, the ball rod structure configuration is 6 n The k-th configuration relation (Cd) in the relation 1k Cd 2k ,…,Cd ik ,…,Cd nk )(i=1,2,…,n;k=1,2,…,6 n For example:

[0169] 1. Based on the current k-th configuration relationship, the NMR T2 spectrum characterizing the porosity of the sphere. 2sk and characterization of the tubular laryngeal tract T2 NMR spectrum 2ck Calculate the echo train M characterizing the pore space. sck :

[0170]

[0171] Where t represents the echo train time, M sck This is the echo train calculated from the spherical pore spectrum and tubular throat spectrum obtained based on the X-ray tube model under the current k-th configuration relationship.

[0172] 2. Based on the NMR T2 spectrum (Formula 6), calculate the original echo train M. ori :

[0173]

[0174] Where t is the echo train time; M ori The echo train is calculated from the inverse of the T2 NMR spectrum.

[0175] 3. Calculate the calculated echo train Msck and the original echo train M in the sphere pore structure model and the tubular throat spectrum under the current k-th configuration relationship. ori The difference M errork :

[0176] M errork =|M sck -M ori |,(k=1,2,…,6 n ),(18)

[0177] In the formula, k represents the k-th configuration relationship, and M sck This is the echo train calculated from the spherical pore spectrum and the tubular throat spectrum based on the X-ray tube model under the current k-th configuration relationship; Mx ri The echo train is calculated from the inverse of the T2 NMR spectrum.

[0178] Furthermore, the minimum echo train error and the corresponding configuration relationship between the spherical pores and tubular throats in each group of X-ray tube pore structure models are selected as the optimal configuration relationship between the spherical pores and tubular throats in the X-ray tube pore structure model. The optimal configuration relationship is expressed as:

[0179] (Cd1 * Cd2 * ,…,Cd i * ,…,Cd n * ),(19)

[0180] Among them, Cd1 * This represents the ratio of the radius of the tubular throat to the radius of the spherical pore in the i-th group of X-ray tube models, reflecting the configuration relationship of the pore structure models in this group of X-ray tubes. Based on the optimal configuration relationship between the spherical pores and the tubular throat in all X-ray tube pore structure models, the above steps are used to decompose the NMR T2 spectrum into the optimal spherical pore spectrum T. 2s * And the optimal tubular laryngeal spectrum T 2c * As shown in the following formula:

[0181] T 2s * =(T 2sy * Am sy * ),(y=1,2,…,…)

[0182] T 2c *=(T 2cy * Am cy * ),(y=1,2,…,…)

[0183] Among them, T 2sy * Indicates the pore size T of the sphere 2s Am is the y-th relaxation time or the y-th placement point value in the spectrum. sy* Indicates the pore size T of the sphere 2s The spectral amplitude T corresponding to the y-th relaxation time or the y-th placement point value in the spectrum. 2cy * T represents the tubular larynx 2c Am is the y-th relaxation time or the y-th placement point value in the spectrum. sy * T represents the tubular larynx 2c The spectral amplitude corresponding to the y-th relaxation time or the y-th placement point value in the spectrum.

[0184] In one embodiment, the third pore structure parameters include the pore radius and tortuosity of a sphere, and the pore radius and tortuosity of a tubular throat.

[0185] Specifically, the calculation of the third pore structure parameters based on the optimal spatial configuration relationship includes:

[0186] Calculate the arithmetic mean and geometric mean of the sphere pore time spectrum and the tubular throat time spectrum based on the optimal configuration relationship;

[0187] The radius and tortuosity of the spherical pores and tubular throats are calculated based on the arithmetic mean and the geometric mean.

[0188] The radius and tortuosity of the spherical pores and the radius and tortuosity of the tubular throat are combined to obtain the third pore structure parameters.

[0189] The radius and tortuosity of the pores in a sphere and the throat in a tubular structure can be calculated using the following formula:

[0190]

[0191] Among them, Rad wp The radius of the pores in the sphere is represented by ρ, and the surface relaxation rate is represented by T. 2SAM τ represents the arithmetic mean of the pore size of a sphere. wp T represents the tortuosity of the pores in a sphere. 2SGM Rad represents the geometric mean of the porosity of a sphere. wt The radius of the tubular throat is represented by ρ, the relaxation rate of the core surface is represented by T. 2CAM τ represents the arithmetic mean of the tubular larynx. wt T represents the tortuosity of the tubular larynx. 2CGM This represents the geometric mean of the tubular throat.

[0192] In one embodiment, by calculating the third pore structure parameter, the structural parameters of the pore structure can be calculated when the logging data is nuclear magnetic resonance logging data, thereby improving the universality and accuracy of the pore structure parameter calculation.

[0193] S5. Collect the first pore structure parameters, the second pore structure parameters, and the third pore structure parameters to obtain the pore structure parameters corresponding to the well logging data.

[0194] In one embodiment, the pore structure parameters corresponding to the logging data are obtained through the first pore structure parameter, the second pore structure parameter, and the third pore structure parameter. The pore structure parameters can be calculated separately when the logging data is conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data. The pore structure parameters can be considered in the saturation model constructed from various logging data, thereby improving the accuracy of the calculation of pore structure parameters in the reservoir saturation model corresponding to the logging data.

[0195] Example 2

[0196] like Figure 3 The diagram shown is a functional block diagram of a pore structure parameter identification device 100 provided in this embodiment.

[0197] The pore structure parameter identification device 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the pore structure parameter identification device 100 may include a well logging data partitioning module 101, a first pore structure parameter calculation module 102, a second pore structure parameter calculation module 103, a third pore structure parameter calculation module 104, and a pore structure parameter aggregation module 105. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0198] In this embodiment, the functions of each module / unit are as follows:

[0199] The logging data segmentation module 101 is used to acquire logging data and segment the logging data into conventional logging data, electrical imaging logging data, and nuclear magnetic resonance logging data.

[0200] The first pore structure parameter calculation module 102 is used to calculate the first pore structure parameters of the pores based on the conventional logging data.

[0201] The second pore structure parameter calculation module 103 is used to perform graphic segmentation on the electrical imaging logging data to obtain a pore logging image, and to calculate the second pore structure parameters based on the pore logging image.

[0202] The third pore structure parameter calculation module 104 is used to determine the optimal spatial configuration relationship characterizing the pore space based on the nuclear magnetic resonance logging data, and to calculate the third pore structure parameters based on the optimal spatial configuration relationship.

[0203] The pore structure parameter collection module 105 is used to collect the first pore structure parameter, the second pore structure parameter and the third pore structure parameter to obtain the pore structure parameter corresponding to the well logging data.

[0204] Example 3

[0205] Figure 4 This is a schematic diagram of an electronic device for a pore structure parameter identification method provided in an embodiment of this application.

[0206] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0207] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0208] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0209] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0210] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0211] Computer-readable storage media may also store at least one computer-executable program, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0212] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0213] The processor can communicate with external devices via the communication interface of the I / O bus through wired or wireless networks.

[0214] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0215] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0216] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0217] Although the embodiments disclosed in this application are as described above, the above content is merely for the purpose of facilitating understanding of this application and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in this application; however, the scope of patent protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A method for identifying pore structure parameters, characterized in that, The method includes: Acquire well logging data and classify the well logging data into conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance well logging data; Calculate the first pore structure parameters of the pores based on the conventional well logging data; The electrical imaging logging data is image segmented to obtain a pore logging image, and the second pore structure parameters are calculated based on the pore logging image. The optimal spatial configuration relationship characterizing the pore space is determined based on the nuclear magnetic resonance logging data, and the third pore structure parameters are calculated based on the optimal spatial configuration relationship. By combining the first pore structure parameters, the second pore structure parameters, and the third pore structure parameters, the pore structure parameters corresponding to the well logging data are obtained.

2. The pore structure parameter identification method according to claim 1, characterized in that, The calculation of the first pore structure parameters based on the conventional well logging data includes: Calculate the formation density logging porosity, neutron logging porosity, and primary intergranular porosity based on the conventional logging data. The following formulas are used to calculate the formation density logging porosity, neutron logging porosity, and primary intergranular porosity: Where, φ D ρ represents density logging porosity. ma ρ represents the density value of the rock skeleton in conventional well logging data. b ρ represents the density value measured by density logging in conventional well logging data. f φ represents the density value of pore fluid in conventional well logging data. N Φ represents the porosity in neutron logging. ma Φ f Φ N φ represents the hydrogen content index measured in conventional well logging data, specifically the rock skeleton, pore fluid, and neutron logging data. S Indicates the primary intergranular porosity, Δt ma Δt f Δt and Δt represent the time differences of rock skeleton, pore fluid and logging time difference measurements in conventional logging data, respectively; The total porosity is calculated based on the neutron logging porosity and the density logging porosity. The total porosity is calculated using the following formula: Where, φ t φ represents the total porosity. D φ represents density logging porosity. N Indicates neutron logging porosity; The porosity of the matrix and the fracture porosity are calculated based on the total porosity and the primary intergranular porosity. The porosity of the matrix porosity and fracture porosity is calculated using the following formula: f f =φ t -f s ,f b =φ s Where, φ f φ represents the porosity of the crack pores. t φ represents the total porosity. S Indicates the primary intergranular porosity, φ b Porosity indicates the porosity of the matrix. The first pore structure parameter is obtained by combining the total porosity, the matrix porosity, and the crack porosity.

3. The pore structure parameter identification method according to claim 1, characterized in that, The step of performing graphic segmentation on the electrical imaging logging data to obtain porosity logging images includes: Calculate the grayscale histogram of each logging image in the electrical imaging logging data; The threshold for maximizing the inter-class variance is calculated based on the gray-level histogram, and the well logging image is segmented using the maximum inter-class difference method based on the threshold to obtain a segmented image; Median filtering is applied to the segmented image to obtain a porosity logging image.

4. The pore structure parameter identification method according to claim 1, characterized in that, The calculation of the second pore structure parameters based on the pore logging image includes: Edge detection is performed on the porosity logging images to obtain the range of fracture curves and the range of dissolution cavities; Calculate the crack structure parameters based on the crack curve range; The structural parameters of the crack are calculated using the following formula: W=CAR m b R xo (1-b) Where W represents the crack width, C represents the preset first instrument structure parameter, A represents the abnormal current area, and R... m R represents the resistivity of the mud. xo The value represents the resistivity of the intrusion band, b represents the preset second instrument structure parameter, and V. e Z0 represents the plate potential value, and I represents the basic half-width. a I represents the electrode current. b The electrode current represents the undisturbed formation or framework, z represents the displacement perpendicular to the fracture trajectory within the fracture curve range, and φ represents the current of the electrode in the undisturbed formation or framework. f W represents the porosity of the crack. i L represents the width of the i-th crack. i τ represents the length of the i-th fracture within a preset statistical window length L, D represents the well diameter in the logging data, and τ wfp Indicates crack tortuosity, qng represents crack dip angle, Rad wfp Indicates the crack radius; Calculate the structural parameters of the dissolution cavities based on the range of the dissolution cavities; By combining the crack structure parameters and the dissolution cavity structure parameters, the second pore structure parameters are obtained.

5. The pore structure parameter identification method according to claim 1, characterized in that, The determination of the optimal spatial configuration relationship characterizing the pore space based on the nuclear magnetic resonance logging data includes: Based on the transverse relaxation time spectrum in the nuclear magnetic resonance logging data, the number of model groups for the pore structure model of the PV tube representing the pore space is determined. Calculate the configuration relationship between the spherical pores and the tubular throat in the spherical tube pore structure model based on the number of model groups. Calculate the echo train representing the pore space under each configuration relationship, and determine the optimal configuration relationship based on the echo train.

6. The pore structure parameter identification method according to claim 1, characterized in that, The calculation of the third pore structure parameters based on the optimal spatial configuration relationship includes: Calculate the arithmetic mean and geometric mean of the sphere pore time spectrum and the tubular throat time spectrum based on the optimal configuration relationship; The radius and tortuosity of the spherical pores and tubular throats are calculated based on the arithmetic mean and the geometric mean. The radius and tortuosity of the spherical pores and tubular throats are calculated using the formulas shown below: Among them, Rad wp The radius of the pores in the sphere is represented by ρ, and the surface relaxation rate is represented by T. 2SAM τ represents the arithmetic mean of the pore size of a sphere. wp T represents the tortuosity of the pores in a sphere. 2SGM Rad represents the geometric mean of the porosity of a sphere. wt The radius of the tubular throat is represented by ρ, the relaxation rate of the core surface is represented by T. 2CAM τ represents the arithmetic mean of the tubular larynx. wt T represents the tortuosity of the tubular larynx. 2CGM Represents the geometric mean of the tubular throat; The radius and tortuosity of the spherical pores and the radius and tortuosity of the tubular throat are combined to obtain the third pore structure parameters.

7. A device for identifying pore structure parameters, characterized in that, The device includes: The well logging data segmentation module is used to acquire well logging data and segment the well logging data into conventional well logging data, electrical imaging well logging data, and nuclear magnetic resonance well logging data. The first pore structure parameter calculation module is used to calculate the first pore structure parameters of the pores based on the conventional logging data. The second pore structure parameter calculation module is used to perform graphic segmentation on the electrical imaging logging data to obtain a pore logging image, and to calculate the second pore structure parameters based on the pore logging image. The third pore structure parameter calculation module is used to determine the optimal spatial configuration relationship characterizing the pore space based on the nuclear magnetic resonance logging data, and to calculate the third pore structure parameters based on the optimal spatial configuration relationship. The pore structure parameter collection module is used to collect the first pore structure parameter, the second pore structure parameter and the third pore structure parameter to obtain the pore structure parameter corresponding to the well logging data.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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