Glutenite reservoir pore throat structure quantitative identification method and device, medium and equipment

Abstract

The invention discloses a glutenite reservoir pore throat structure quantitative identification method and device, a medium and computing equipment, and the method comprises the steps: analyzing a pre-processed sample based on nuclear magnetic resonance and mercury intrusion test, and obtaining a full-scale pore throat radius data set corresponding to a full-band nuclear magnetic resonance spectrum; analyzing the full-scale pore throat radius data set to obtain a turning point of the fractal dimension curve; based on the turning point of the fractal dimension curve, determining the pore throat space type of the pre-treated sample; and based on the pore throat space type and the content corresponding to the pore throat space type, quantitatively identifying different modal pore throat structures of the pre-treated sample. The method and the device aim at improving the accuracy of quantitative recognition of different modal pore throat structures by performing full-scale characterization on the pre-processed sample.

Description

[0001] Cross-references

[0002] This disclosure claims the application number 202410121661.3 filed on January 29, 2024 and entitled "A kind of gravel Priority of the Chinese patent application for "Quantitative identification method, device, medium and equipment for pore throat structure of rock reservoir" The entire contents of this application are incorporated herein by reference in their entirety. Technical Field

[0003] The present application relates to the technical field of oil and gas exploration and development, and in particular to a method, device, medium and equipment for quantitatively identifying pore throat structure of a sandstone reservoir. Background Art

[0004] Pore-throat structure refers to the geometric shape, size, distribution and interconnectedness of pores and throats (i.e., channels connecting pores) in rocks or soil.

[0005] Among them, the pore throat structure of the conglomerate reservoir is complex and has a unique modal pore throat structure. In the prior art, the quantitative identification accuracy of different modal pore throat structures is low.

[0006] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0007] The main purpose of this application is to provide a method, device, medium and computing equipment for quantitative identification of pore throat structure of sandstone reservoirs, aiming to improve the accuracy of quantitative identification of pore throat structures of different modes by full-scale characterization of pre-processed samples.

[0008] To achieve the above-mentioned purpose, the present application provides a method for quantitatively identifying the pore throat structure of a sandstone conglomerate reservoir, the method comprising: analyzing the pre-treated samples based on nuclear magnetic resonance and mercury injection testing respectively to obtain a full-scale pore throat radius data set corresponding to the full-band nuclear magnetic resonance spectrum; analyzing the full-scale pore throat radius data set to obtain the turning point of a fractal dimension curve; determining the pore throat space type of the pre-treated sample based on the turning point of the fractal dimension curve; and quantitatively identifying different modal pore throat structures of the pre-treated sample based on the pore throat space type and the content corresponding to the pore throat space type.

[0009] Optionally, the pre-treated samples are analyzed based on nuclear magnetic resonance and mercury injection testing respectively to obtain a full-scale pore throat radius data set corresponding to the full-band nuclear magnetic resonance spectrum, including: performing a low-field nuclear magnetic resonance scan on the pre-treated samples to obtain a transverse relaxation time data set of the pre-treated samples; performing a mercury injection test on the samples after the low-field nuclear magnetic resonance scan to obtain a mercury saturation data set; interpolating the mercury saturation data set according to the transverse relaxation time data set to obtain a corresponding relationship set between the full-band capillary pressure distribution and the transverse relaxation time distribution; performing data fitting on the corresponding relationship set to obtain the full-scale pore throat radius corresponding to the full-band nuclear magnetic resonance spectrum.

[0010] Optionally, the mercury saturation dataset is interpolated according to the transverse relaxation time dataset to obtain a correspondence set between the full-band capillary pressure distribution and the transverse relaxation time distribution, including: determining the cumulative frequency percentage of each transverse relaxation time according to the percentage of the amplitude corresponding to each transverse relaxation time in the transverse relaxation time dataset to the sum of all amplitudes; determining the cumulative frequency percentage of the mercury saturation dataset according to each capillary pressure in the mercury saturation dataset; interpolating the cumulative frequency percentage of the capillary pressure in the mercury saturation dataset according to the cumulative frequency percentage of each transverse relaxation time to obtain a correspondence set between the capillary pressure distribution and the transverse relaxation time distribution at the same cumulative frequency percentage.

[0011] Optionally, the full-scale pore throat radius data set is analyzed to obtain the turning point of the fractal dimension curve, including: taking the logarithm of each pore throat radius and the cumulative volume frequency of each pore throat radius in the full-scale pore throat radius data set to obtain the logarithm of each pore throat radius and the logarithm of the cumulative volume frequency of each pore throat radius; obtaining a scattered point data set based on the logarithm of each pore throat radius and the logarithm of the cumulative volume frequency of each pore throat radius; performing cluster analysis on the scattered point data set to obtain a set of clustered data points with similar characteristics; performing piecewise linear fitting on the set of clustered data points to obtain a fractal dimension curve; and analyzing the fractal dimension curve to obtain the turning point of the fractal dimension curve.

[0012] Optionally, the pore throat space types include large pore throat type, medium pore throat type and small pore throat type; determining the pore throat space type of the pre-treated sample based on the turning point of the fractal dimension curve includes: analyzing the turning point of the fractal dimension curve, and determining the turning point of the fractal dimension curve as the dividing point of the fractal dimension curve; based on the dividing point of the fractal dimension curve, determining the pore throat space type of the pre-treated sample to be a large pore throat type, a medium pore throat type and / or a small pore throat type.

[0013] Optionally, based on the pore throat space type and the content corresponding to the pore throat space type, the different modal pore throat structures of the pre-processed sample are quantitatively identified, including: if the pore throat space types of the pre-processed sample are large pore throat type, medium pore throat type and small pore throat type, and the content of the large pore throat type is greater than a first preset threshold, then the pre-processed sample is quantitatively identified as a complex modal pore throat structure; if the pore throat space types of the pre-processed sample are the medium pore throat type and the small pore throat type, and the content of the medium pore throat type is greater than a second preset threshold, and the content of the small pore throat type is less than a third preset threshold, then the pre-processed sample is quantitatively identified as a bimodal pore throat structure; if the pore throat space types of the pre-processed sample are the medium pore throat type and the small pore throat type, and the content of the medium pore throat type is less than the second preset threshold, and the content of the small pore throat type is greater than the third preset threshold, then the pre-processed sample is quantitatively identified as a single modal pore throat structure.

[0014] Optionally, after quantitatively identifying different modal pore throat structures of the pre-treated sample based on the pore throat space type and content, the method further includes: verifying the quantitatively identified different modal pore throat structures of the pre-treated sample using cast thin sections.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a device for quantitatively identifying the pore throat structure of a sandstone reservoir, the device comprising: a first processing module, used to obtain the full-scale pore throat radius of a pre-processed sample based on nuclear magnetic resonance and mercury injection testing; a second processing module, used to determine the turning point of the fractal dimension curve of the pre-processed sample based on the full-scale pore throat radius; a third processing module, used to determine the pore throat space type of the pre-processed sample based on the turning point of the fractal dimension curve; a quantitative identification module, used to quantitatively identify different modal pore throat structures of the pre-processed sample based on the pore throat space type and the content corresponding to the pore throat space type.

[0016] In addition, the present application also provides a computing device, which includes: at least one processor, a memory and an input-output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the quantitative identification method of the pore throat structure of the sandstone reservoir described in any one of the first aspects.

[0017] In addition, the present application also provides a computer-readable storage medium, which includes instructions, and when the instructions are executed on a computer, the computer executes the method for quantitatively identifying pore throat structure of a conglomerate reservoir as described in any one of the first aspects.

[0018] The embodiments of the present application propose a method, device, medium and equipment for quantitatively identifying the pore throat structure of a sandstone reservoir. On the one hand, by utilizing nuclear magnetic resonance and mercury injection testing, a full-scale pore throat radius data set of a pre-processed sample is obtained. The full-scale pore throat radius data set can perform full-scale characterization of the pore throat radius of the pre-processed sample, which not only effectively avoids the defect that the conventional mercury injection testing cannot characterize the smaller pore throat radius, but also can accurately and comprehensively characterize the pore throat structure of the pre-processed sample, so that the measured pore throat radius data is more precise and accurate; on the other hand, by analyzing the full-scale pore throat radius data set, a turning point of a fractal dimension curve of the pre-processed sample is obtained. The turning point of the fractal dimension curve can characterize the fractal characteristics of the pore throat structure, and then the spatial type of the pore throat structure can be obtained more accurately. Then, according to the pore throat space type and the content corresponding to the pore throat space type, different modal pore throat structures of the pre-processed sample are identified, thereby improving the accuracy of quantitatively identifying different modal pore throat structures of the pre-processed sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a flow chart of a method for quantitatively identifying pore throat structure of a conglomerate reservoir provided in one embodiment of the present application;

[0020] Figure 2a The comparison between the pore throat radius converted from the transverse relaxation time spectrum of the conglomerate reservoir sample and the pore throat radius measured by mercury injection Figure 1 ;

[0021] Figure 2b for Figure 2a The capillary pressure curve obtained after conversion of the pore throat radius in;

[0022] Figure 2c for Figure 2a The pore throat space types of the conglomerate reservoir samples in the study;

[0023] Figure 3a Schematic diagram 2 for the comparison between the pore throat radius converted from the transverse relaxation time spectrum of the conglomerate reservoir sample and the pore throat radius measured by mercury injection;

[0024] Figure 3b for Figure 3a The capillary pressure curve obtained after conversion of the pore throat radius in;

[0025] Figure 3c for Figure 3a The pore throat space types of the medium conglomerate reservoir samples;

[0026] Figure 4 A schematic flow chart of a method for quantitatively identifying pore throat structure of a conglomerate reservoir provided in another embodiment of the present application;

[0027] Figure 5 A schematic diagram of functional modules of a device for quantitatively identifying pore throat structures of conglomerate reservoirs provided in one embodiment of the present application;

[0028] Figure 6 A schematic diagram of the structure of a medium provided in one embodiment of the present application;

[0029] Figure 7 A schematic diagram of the structure of a computing device provided in one embodiment of the present application.

[0030] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0031] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0032] Those skilled in the art know that the embodiments of the present application can be implemented as a system, device, equipment, method or computer program product. Therefore, the present application can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0033] The main solutions of the embodiments of the present application are: based on nuclear magnetic resonance and mercury injection testing, the pre-treated samples are analyzed respectively to obtain a full-scale pore throat radius data set corresponding to the full-band nuclear magnetic resonance spectrum; the full-scale pore throat radius data set is analyzed to obtain the turning point of the fractal dimension curve; based on the turning point of the fractal dimension curve, the pore throat space type of the pre-treated sample is determined; based on the pore throat space type and content, the different modal pore throat structures of the pre-treated sample are quantitatively identified.

[0034] The pore-throat structure refers to the geometric shape, size, distribution and interconnectedness of pores and throats (i.e., channels connecting pores) in rocks or soils. The pore-throat structure of conglomerate reservoirs is complex and has a unique modal pore-throat structure.

[0035] In the prior art, the accuracy of quantitative identification of different modal pore throat structures is low.

[0036] The present application provides a solution. On the one hand, by utilizing nuclear magnetic resonance and mercury injection testing, a full-scale pore throat radius data set of the pre-processed sample is obtained. The full-scale pore throat radius data set can perform full-scale characterization of the pore throat radius of the pre-processed sample, which not only effectively avoids the defect that the conventional mercury injection testing cannot characterize the smaller pore throat radius, but also can accurately and comprehensively characterize the pore throat structure of the pre-processed sample, so that the measured pore throat radius data is more precise and accurate; on the other hand, by analyzing the full-scale pore throat radius data set, the turning point of the fractal dimension curve of the pre-processed sample is obtained. The turning point of the fractal dimension curve can characterize the fractal characteristics of the pore throat structure, and then the spatial type of the pore throat structure can be obtained more accurately. Afterwards, according to the pore throat space type and the content corresponding to the pore throat space type, the different modal pore throat structures of the pre-processed sample are identified, thereby improving the accuracy of quantitatively identifying the different modal pore throat structures of the pre-processed sample.

[0037] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0038] The principles and spirit of the present application are explained in detail below with reference to several representative implementations of the present application.

[0039] Reference Figure 1 An embodiment of the present application provides a method for quantitatively identifying the pore throat structure of a conglomerate reservoir. The method can be used to quantitatively identify the pore throat structure of conglomerate reservoirs of different modes, such as Figure 1 As shown, the method for quantitatively identifying the pore throat structure of the sandstone conglomerate reservoir may include the following steps:

[0040] Step S110, analyzing the pre-treated samples based on nuclear magnetic resonance and mercury injection testing respectively, to obtain a full-scale pore throat radius data set corresponding to the full-band nuclear magnetic resonance spectrum.

[0041] In an exemplary embodiment, the sample pre-treatment process includes: first, selecting a core sample of a high permeability section of a sandstone reservoir, drilling a circular plunger sample with a certain diameter, and then cutting the circular plunger sample into a plunger sample with a certain length, that is, a first sample, for example, the first sample is a plunger sample with a diameter of 2.5 cm and a length of 2.5 cm. Afterwards, the first sample is subjected to oil washing treatment, and weighed to obtain a first weight m1, for example, m1 is 35.16 g. Afterwards, the first sample is subjected to vacuum treatment, and the vacuuming time can last for 24 hours. Afterwards, the first sample after vacuum treatment is placed in a saturated water experimental device for saturated water treatment to obtain a second sample, and the second sample is weighed to obtain a second weight m2, for example, m2 is 37.12 g, and the saturated water treatment process can last for 48 hours. Finally, the second sample is cleaned with anhydrous ethanol, and dried after cleaning to obtain a third sample, and the drying time can last for 6 hours. The weight difference between the second weight and the first weight can be used to determine the volume of the pore structure in the sample.

[0042] By analyzing the second sample in the above content using nuclear magnetic resonance, the transverse relaxation time data of the pore throat structure in the second sample can be obtained.

[0043] By analyzing the third sample in the above content using mercury injection testing, saturation data of different pore throat radii in the pore throat structure of the third sample and the capillary pressure corresponding to the saturation data can be obtained.

[0044] The transverse relaxation time data of NMR are correlated with the saturation data of mercury injection testing and capillary pressure to obtain a full-scale pore throat radius data set that can characterize the pore throat structure of the sample.

[0045] In some specific embodiments, the above step S110 may specifically include:

[0046] Step S1101, performing a low-field nuclear magnetic resonance scan on the pre-processed sample to obtain a transverse relaxation time data set of the pre-processed sample.

[0047] In an exemplary embodiment, the transverse relaxation time data set includes transverse relaxation time T2 and amplitudes corresponding to the transverse relaxation time T2. The amplitudes of the transverse relaxation time T2 can be used to determine the cumulative frequency percentage corresponding to each transverse relaxation time in a subsequent step.

[0048] It can be understood from the above embodiments that the second sample is obtained by subjecting the first sample to vacuum treatment and saturated water treatment. When the second sample is subjected to low-field nuclear magnetic resonance scanning, the larger the amplitude of the transverse relaxation time T2, the more saturated water is contained in the corresponding pore structure, and the larger the pore throat radius of the corresponding pore throat structure; conversely, the smaller the amplitude of the transverse relaxation time T2, the less saturated water is contained in the corresponding pore structure, and the smaller the pore throat radius of the corresponding pore throat structure.

[0049] Step S1102, performing a mercury intrusion test on the sample after the low-field nuclear magnetic resonance scanning to obtain a mercury saturation data set.

[0050] In the example embodiment, the mercury saturation data set includes mercury saturation data and capillary pressure data corresponding to the mercury pressure data, and characterizes the corresponding relationship between mercury saturation and mercury pressure. The mercury saturation data set is used to determine the pore throat radii of different sizes in the sample. Specifically, each mercury saturation data corresponds to a capillary pressure data, and there is a unique corresponding relationship between the capillary pressure and the pore throat radius. Therefore, the pore throat radii of different pore structures in the sample can be determined through each capillary pressure data in the mercury saturation data set, that is, the capillary pressure in the mercury injection process is converted into the pore throat radius.

[0051] Step S1103 , interpolating the mercury saturation data set according to the transverse relaxation time data set to obtain a corresponding relationship set between the full-band capillary pressure distribution and the transverse relaxation time distribution.

[0052] In the example embodiment, the interpolation process is to interpolate the mercury saturation data set using a linear interpolation method, so that the amount of capillary pressure data in the mercury saturation data set is the same as the amount of transverse relaxation time data in the transverse relaxation time data set. In other words, because the mercury liquid cannot penetrate into the area where the pore throat radius is too small in the third sample, the amount of data in the mercury saturation data set obtained after the mercury injection test is less than the amount of data in the transverse relaxation time data set. Therefore, it is necessary to interpolate the mercury saturation data set according to the transverse relaxation time data set, so that the amount of capillary pressure data in the mercury saturation data set is the same as the amount of transverse relaxation time data in the transverse relaxation time data set.

[0053] In a specific embodiment, the cumulative frequency percentage of each transverse relaxation time is determined based on the percentage of the amplitude corresponding to each transverse relaxation time in the transverse relaxation time data set to the sum of all amplitudes; the cumulative frequency percentage of the mercury saturation data set is determined based on each capillary pressure in the mercury saturation data set; the cumulative frequency percentage of the capillary pressure in the mercury saturation data set is interpolated based on the cumulative frequency percentage of each transverse relaxation time to obtain a corresponding relationship set of capillary pressure distribution and transverse relaxation time distribution under the same cumulative frequency percentage.

[0054] As can be understood in conjunction with the above embodiments, the transverse relaxation time data set includes the transverse relaxation time T2 and the amplitude corresponding to the transverse relaxation time T2. Then, the ratio of the amplitude corresponding to each transverse relaxation time to the sum of all amplitudes can be determined as the distribution frequency corresponding to each transverse relaxation time, and then the distribution frequency is accumulated to obtain the cumulative frequency percentage corresponding to each transverse relaxation time. For example, the distribution frequency corresponding to each transverse relaxation time is (X1, X2, X3, ..., Xn), and the distribution frequency corresponding to the transverse relaxation time Tm is Xm, then the cumulative frequency percentage corresponding to the transverse relaxation time Tm is (X1+X2+...+Xm).

[0055] The mercury saturation data set contains mercury saturation data and capillary pressure data. The pore throat radius of the pore throat structure can be calculated based on the capillary pressure data using the following formula (1):

[0056]

[0057] Where r is the pore throat radius, in μm; Pc is the capillary pressure, in MPa; θ is the wetting angle, in °; σ is the interfacial tension, in N / m.

[0058] Then, the distribution frequency of different pore throat radii is determined according to the percentage of each mercury saturation in the mercury saturation data set; finally, the cumulative frequency percentage of the mercury saturation data set is determined according to the distribution frequency of the pore throat radius. For example, the cumulative frequency percentage corresponding to the pore throat radius of 1 μm is the sum of the distribution frequency corresponding to the minimum pore throat radius and the distribution frequency corresponding to the pore throat radius of 1 μm.

[0059] Finally, after obtaining the cumulative frequency percentage of each transverse relaxation time and the cumulative frequency percentage of the mercury saturation data set, the cumulative frequency percentage of the capillary pressure in the mercury saturation data set is interpolated according to the cumulative frequency percentage of each transverse relaxation time to obtain the corresponding relationship set of capillary pressure distribution and transverse relaxation time distribution under the same cumulative frequency percentage.

[0060] Step S1104, performing data fitting on the corresponding relationship set to obtain the full-scale pore throat radius corresponding to the full-band nuclear magnetic resonance spectrum.

[0061] In an exemplary embodiment, data fitting is performed on the corresponding relationship set, that is, linear fitting is performed on the nuclear magnetic resonance lnT2 and mercury injection ln r of the same cumulative frequency.

[0062] Specifically, the linear fitting result of the high permeability conglomerate is a four-segment formula. According to the linear relationship of each segment, C1, C2, C3, C4 and n1, n2, n3, n4 are calculated respectively. The correlation coefficients are all greater than 0.9, and the fitting results are good. Then, using formula (2), the pore throat radius distribution of each segment can be calculated respectively, and then the pore throat radius distribution indicated by the nuclear magnetic resonance T2 spectrum of the whole segment can be obtained. For example, refer to Figure 2a and Figure 3a After interpolation processing, the pore throat radius in the interval of [0.01μm-100μm] is converted into the pore throat radius in the interval of [0.0001μm-100μm], achieving the purpose of full-scale characterization of the pore throat radius.

[0063]

[0064] Where r is the pore throat radius, unit: μm; T2 is the transverse relaxation time of nuclear magnetic resonance, unit: ms; C, n are constants, dimensionless.

[0065] Step S120, analyzing the full-scale pore throat radius data set to obtain the turning point of the fractal dimension curve.

[0066] In an example embodiment, the fractal dimension curve is generally referred to as a Koch curve.

[0067] In a specific embodiment, the method for determining the fractal dimension curve includes:

[0068] Step S1201, taking the logarithm of each pore throat radius and the cumulative volume frequency of each pore throat radius in the full-scale pore throat radius data set, to obtain the logarithm of each pore throat radius and the logarithm of the cumulative volume frequency of each pore throat radius.

[0069] Step S1202: obtaining a scattered point data set based on the logarithm of each pore throat radius and the logarithm of the cumulative volume frequency of each pore throat radius.

[0070] In the example embodiment, the logarithm of each pore throat radius and the cumulative volume frequency of each pore throat radius in the full-scale pore throat radius dataset is taken, that is, each pore throat radius and the cumulative volume frequency of each pore throat radius in the full-scale pore throat radius dataset are converted into a logarithmic form with log10 as the base, so as to achieve numerical scaling of each pore throat radius and the cumulative volume frequency of each pore throat radius in the full-scale pore throat radius dataset, so as to facilitate subsequent data processing.

[0071] Step S1203: perform cluster analysis on the scattered data set to obtain a set of clustered data points with similar features.

[0072] In an example embodiment, cluster analysis is a data analysis method that groups a collection of physical or abstract objects into multiple classes consisting of similar objects. After the cumulative volume frequency of each pore throat radius of the pore throat structure and each pore throat radius are logarithmically scaled, cluster analysis is performed on the scaled data to establish a scattered point data set.

[0073] Step S1204: performing piecewise linear fitting on the clustered data point set to obtain a fractal dimension curve.

[0074] Combined with the above content, it can be understood that the data points in the scattered data set are linearly fitted to obtain the fractal dimension curve. In the sandstone reservoir, the pore throat structure is fractal, and the fractal refers to the fractal structure of pores and throats, and the fractal has fractal characteristics. The linear slope of the fractal dimension curve can characterize the fractal characteristics of the pore throat structure, that is, the linear slope is the fractal dimension. For example, refer to Figure 2b and Figure 3b , linear piecewise fitting is performed on the data points in the scattered data set to obtain four piecewise linear functions. The slopes of the four piecewise linear functions can characterize the fractal characteristics of the pore throat structure.

[0075] After the fractal dimension curve is determined, the method further includes analyzing the fractal dimension curve to obtain the turning point of the fractal dimension curve.

[0076] Step S130, determining the pore throat space type of the pre-processed sample based on the turning point of the fractal dimension curve.

[0077] In an example embodiment, the pore throat space types include large pore throat type, medium pore throat type, and small pore throat type. Figure 2c and Figure 3c The fractal dimension curve presents a multi-segment type, and the dividing point of each segment is almost uniform among different samples. The pore throat space is divided into large pore throat type, medium pore throat type, small pore throat type, and micropore throat type with the turning point of the fractal dimension curve as the dividing point. However, the pore throat radius of the micropore throat type pore throat space is too small, and there is almost no storage and seepage capacity, so it is merged into the small pore throat type pore throat space.

[0078] Specifically, the above step S130 includes: analyzing the turning point of the fractal dimension curve to determine the turning point of the fractal dimension curve as the dividing point of the fractal dimension curve; based on the dividing point of the fractal dimension curve, determining that the pore throat space type of the pre-treated sample is a large pore throat type, a medium pore throat type and / or a small pore throat type.

[0079] Step S140, based on the pore throat space type and the content corresponding to the pore throat space type, quantitatively identify different modal pore throat structures of the pre-processed sample.

[0080] In the example embodiment, the content refers to the content of each type of pore throat space type.

[0081] In a specific embodiment, if the pore throat space types of the pre-processed sample are large pore throat type, medium pore throat type and small pore throat type, and the content of large pore throat type is greater than the first preset threshold, the pre-processed sample is quantitatively identified as a complex modal pore throat structure.

[0082] In an example embodiment, the first preset threshold can be zero; that is, if the pore throat space types of the pre-processed sample are large pore throat type, medium pore throat type and small pore throat type, and the content of large pore throat type is greater than zero, then the pre-processed sample is quantitatively identified as a complex modal pore throat structure.

[0083] It can be understood that, in combination with the above embodiments and referring again to Figure 2a , 2b 2c. The second sample in the above content is analyzed by nuclear magnetic resonance to obtain a transverse relaxation time data set. Figure 2a The distribution diagram of T2 conversion pore throat radius in the above content; the third sample in the above content is analyzed by mercury injection test to obtain the mercury saturation data set, and the mercury saturation data set is drawn according to the mercury saturation data set Figure 2a The measured pore throat radius distribution diagram of medium-pressure mercury; then, the cumulative frequency percentage of capillary pressure is interpolated according to the cumulative frequency percentage of each transverse relaxation time to obtain the corresponding relationship set between capillary pressure distribution and transverse relaxation time distribution, and the corresponding relationship set is drawn according to the corresponding relationship set. Figure 3a The mercury injection interpolation pore throat radius distribution diagram in . Then, by taking the logarithm of each pore throat radius and the cumulative volume frequency of each pore throat radius and performing cluster analysis, a cluster data point set is obtained, and the cluster data point set is drawn Figure 2b The scatter plot shown in , the piecewise linear fitting of the scatter plot is obtained Figure 2b The fractal dimension curve shown in , which includes 4 segmented straight lines, and the 4 segmented straight lines have three turning points. Taking these three turning points as the dividing points, the pore throat space types of the second sample are determined to be large pore throat type, medium pore throat type and small pore throat type. Finally, according to the pore throat space type and content, the pore throat structure of the second sample is determined to be a complex modal pore throat structure.

[0084] In another specific embodiment, if the pore throat space types of the pre-treated sample are mesopore throat type and small pore throat type, and the content of the mesopore throat type is greater than the second preset threshold and the content of the small pore throat type is less than the third preset threshold, then the pre-treated sample is quantitatively identified as a bimodal pore throat structure.

[0085] In an example embodiment, the second preset threshold value may be 10%; the third preset threshold value may be 90%. That is, if the pore throat space types of the pre-processed sample are mesopore throat type and small pore throat type, and the mesopore throat type content is >10%, and the small pore throat type content is <90%, then the pre-processed sample is quantitatively identified as a dual-mode pore throat structure.

[0086] In another specific embodiment, if the pore throat space types of the pre-treated sample are the mesopore throat type and the small pore throat type, and the content of the mesopore throat type is less than the second preset threshold and the content of the small pore throat type is greater than the third preset threshold, then the pre-treated sample is quantitatively identified as a single-modal pore throat structure.

[0087] In an exemplary embodiment, if the pore throat space types of the pre-treated sample are mesopore throat type and small pore throat type, and the mesopore throat type content is <10%, and the small pore throat type content is >90%, then the pre-treated sample is quantitatively identified as a single-mode pore throat structure.

[0088] It can be understood that, in combination with the above embodiments and referring again to Figure 3a , 3b 3c. The second sample in the above content is analyzed by nuclear magnetic resonance to obtain a transverse relaxation time data set. Figure 3a The distribution diagram of T2 conversion pore throat radius in the above content; the third sample in the above content is analyzed by mercury injection test to obtain the mercury saturation data set, and the mercury saturation data set is drawn according to the mercury saturation data set Figure 3a The measured pore throat radius distribution diagram of medium-pressure mercury; then, the cumulative frequency percentage of capillary pressure is interpolated according to the cumulative frequency percentage of each transverse relaxation time to obtain the corresponding relationship set between capillary pressure distribution and transverse relaxation time distribution, and the corresponding relationship set is drawn according to the corresponding relationship set. Figure 3a The mercury injection interpolation pore throat radius distribution diagram in . Then, by taking the logarithm of each pore throat radius and the cumulative volume frequency of each pore throat radius and performing cluster analysis, a cluster data point set is obtained, and the cluster data point set is drawn Figure 3b The scatter plot shown in , the piecewise linear fitting of the scatter plot is obtained Figure 3b The fractal dimension curve shown in , which includes three segmented straight lines, has two turning points. With these two turning points as the dividing points, the pore throat space types of the second sample are determined to be medium pore throat type and small pore throat type. Finally, according to the pore throat space type and content, the pore throat structure of the second sample is determined to be a single-mode pore throat structure.

[0089] Implement the above-mentioned steps S110 to S140, analyze the pre-treated samples by nuclear magnetic resonance and mercury injection testing respectively, and obtain a full-scale pore throat radius data set corresponding to the full-band nuclear magnetic resonance spectrum; analyze the full-scale pore throat radius data set to obtain the turning point of the fractal dimension curve; the turning point of the fractal dimension curve can determine the pore throat space type; then, quantitatively identify different modal pore throat structures of the pre-treated samples according to the pore throat space type and content;

[0090] On the one hand, the full-scale pore throat radius data set is used to characterize the pore throat radius of the pre-processed samples in full scale, which not only effectively avoids the defect that the conventional mercury injection test cannot characterize the small pore throat radius, but also can accurately and comprehensively characterize the pore throat structure of the pre-processed samples, making the measured pore throat radius data more precise and accurate;

[0091] On the other hand, by analyzing the full-scale pore throat radius data set, the turning point of the fractal dimension curve of the pre-processed sample is obtained. The turning point of the fractal dimension curve can characterize the fractal characteristics of the pore throat structure, and then the spatial type of the pore throat structure can be obtained more accurately, thereby improving the accuracy of quantitative identification of different modal pore throat structures of the pre-processed samples.

[0092] In another embodiment of the present application, in order to further improve the accuracy of quantitatively identifying different modal pore throat structures of the pre-processed sample, as Figure 4 As shown, after the above step S140, step S410 is also included.

[0093] Step S410, using casting thin sections to verify the different modal pore throat structures of the quantitatively identified pre-processed samples.

[0094] In the exemplary embodiment, the cast thin slice is made by injecting colored liquid glue into the pore space of the sandstone reservoir under vacuum pressure, and grinding it after the liquid glue solidifies. This production method can make the material structure of the cast thin slice more compact, have better mechanical properties, and have higher dimensional accuracy.

[0095] The quantitative identification results of conglomerate reservoirs can be further improved by verifying the different modal pore throat structures of the quantitatively identified conglomerate reservoirs using cast thin sections.

[0096] In addition, the present application also provides an oil and gas production method, which may include:

[0097] Based on the method for identifying different modal pore throat structures of conglomerate reservoirs described in any of the above embodiments of the present application, the microscopic pore throat structure of the reservoir sample is quantitatively identified; the macroscopic distribution characteristics of reservoirs with different modal pore throat structures are determined through the well location coordinates of samples with different modal pore throat structures, so as to accurately identify reservoirs with different modal pore throat structures; based on the reservoir type identification results, a comprehensive evaluation of reservoir characteristics is performed to clarify the reservoir storage and seepage capacity; based on the comprehensive evaluation results of reservoir characteristics, corresponding mining strategies are formulated for different types of reservoirs to develop oil fields with higher efficiency.

[0098] After introducing the method of the exemplary embodiment of the present application, next, refer to Figure 5 A device for quantitatively identifying pore throat structure of a conglomerate reservoir according to an exemplary embodiment of the present application is described. The device 500 for quantitatively identifying pore throat structure of a conglomerate reservoir comprises: a first processing module 510, a second processing module 520, a third processing module 530, and a quantitative identification module 540;

[0099] Wherein, the first processing module 510 can be used to obtain the full-scale pore throat radius of the pre-processed sample based on nuclear magnetic resonance and mercury injection testing;

[0100] The second processing module 520 can be used to determine the turning point of the fractal dimension curve of the pre-processed sample based on the full-scale pore throat radius;

[0101] The third processing module 530 can be used to determine the pore throat space type of the pre-processed sample based on the turning point of the fractal dimension curve;

[0102] The quantitative identification module 540 can be used to quantitatively identify different modal pore throat structures of the pre-processed sample based on the pore throat space type and the content corresponding to the pore throat space type.

[0103] As an optional implementation, the first processing module 510 is also used to: perform a low-field nuclear magnetic resonance scan on the pre-processed sample to obtain a transverse relaxation time data set of the pre-processed sample; perform a mercury injection test on the sample after the low-field nuclear magnetic resonance scan to obtain a mercury saturation data set; perform interpolation processing on the mercury saturation data set according to the transverse relaxation time data set to obtain a corresponding relationship set between the full-band capillary pressure distribution and the transverse relaxation time distribution; perform data fitting on the corresponding relationship set to obtain a full-scale pore throat radius corresponding to the full-band nuclear magnetic resonance spectrum.

[0104] As an optional implementation, the first processing module 510 is also used to: determine the cumulative frequency percentage of each transverse relaxation time according to the percentage of the amplitude corresponding to each transverse relaxation time in the transverse relaxation time data set to the sum of all amplitudes; determine the cumulative frequency percentage of the mercury saturation data set according to each capillary pressure in the mercury saturation data set; interpolate the cumulative frequency percentage of the capillary pressure in the mercury saturation data set according to the cumulative frequency percentage of each transverse relaxation time, and obtain a corresponding relationship set of capillary pressure distribution and transverse relaxation time distribution under the same cumulative frequency percentage.

[0105] As an optional implementation, the second processing module 520 is also used to: take the logarithm of each pore throat radius and the cumulative volume frequency of each pore throat radius in the full-scale pore throat radius data set to obtain the logarithm of each pore throat radius and the logarithm of the cumulative volume frequency of each pore throat radius; obtain a scattered data set based on the logarithm of each pore throat radius and the logarithm of the cumulative volume frequency of each pore throat radius; perform cluster analysis on the scattered data set to obtain a set of clustered data points with similar characteristics; perform piecewise linear fitting on the set of clustered data points to obtain a fractal dimension curve; analyze the fractal dimension curve to obtain the turning point of the fractal dimension curve.

[0106] As an optional embodiment, the pore throat space types include large pore throat type, medium pore throat type and small pore throat type; the above-mentioned third processing module 530 is also used to: analyze the turning point of the fractal dimension curve, and determine the turning point of the fractal dimension curve as the dividing point of the fractal dimension curve; based on the dividing point of the fractal dimension curve, determine that the pore throat space type of the pre-processed sample is a large pore throat type, a medium pore throat type and / or a small pore throat type.

[0107] As an optional embodiment, the above-mentioned quantitative identification module 540 is also used for: if the pore throat space types of the pre-processed sample are large pore throat type, medium pore throat type and small pore throat type, and the content of large pore throat type is greater than the first preset threshold, then the pre-processed sample is quantitatively identified as a complex modal pore throat structure; if the pore throat space types of the pre-processed sample are medium pore throat type and small pore throat type, and the content of medium pore throat type is greater than the second preset threshold, and the content of small pore throat type is less than the third preset threshold, then the pre-processed sample is quantitatively identified as a bimodal pore throat structure; if the pore throat space types of the pre-processed sample are medium pore throat type and small pore throat type, and the content of medium pore throat type is less than the second preset threshold, and the content of small pore throat type is greater than the third preset threshold, then the pre-processed sample is quantitatively identified as a single modal pore throat structure.

[0108] As an optional implementation, the above-mentioned device 500 for quantitatively identifying pore throat structures of conglomerate reservoirs is also used to verify different modal pore throat structures of quantitatively identified pre-processed samples using cast thin sections.

[0109] After introducing the method and apparatus of the exemplary embodiment of the present application, next, reference is made to Figure 6 For a description of the computer-readable storage medium of the exemplary embodiment of the present application, please refer to Figure 6 , the computer-readable storage medium shown is a CD 60, on which a computer program (i.e., a program product) is stored. When the computer program is executed by the processor, the steps described in the above method implementation are implemented, for example, the pre-treated samples are analyzed based on nuclear magnetic resonance and mercury injection tests respectively to obtain a full-scale pore throat radius data set corresponding to the full-band nuclear magnetic resonance spectrum; the full-scale pore throat radius data set is analyzed to obtain the turning point of the fractal dimension curve; based on the turning point of the fractal dimension curve, the pore throat space type of the pre-treated sample is determined; based on the pore throat space type and content, different modal pore throat structures of the pre-treated sample are quantitatively identified; the specific implementation method of each step will not be repeated here.

[0110] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0111] After introducing the method, apparatus and medium of the exemplary embodiments of the present application, next, reference is made to Figure 7 A computing device for model processing according to an exemplary embodiment of the present application.

[0112] Figure 7 A block diagram of an exemplary computing device 70 suitable for implementing embodiments of the present application is shown. The computing device 70 may be a computer system or a server. Figure 7 The computing device 70 shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present application.

[0113] like Figure 7 As shown, the components of the computing device 70 may include, but are not limited to: one or more processors or processing units 701, a system memory 702, and a bus 703 connecting different system components (including the system memory 702 and the processing unit 701).

[0114] The computing device 70 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 70, including volatile and non-volatile media, removable and non-removable media.

[0115] The system memory 702 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 7021 and / or cache memory 7022. The computing device 70 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the ROM 7023 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 is not shown in the Figure 7 As shown in FIG. 7 , a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) can be provided. In these cases, each drive can be connected to bus 703 via one or more data medium interfaces. System memory 702 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of each embodiment of the present application.

[0116] A program / utility 7025 having a set (at least one) of program modules 7024 may be stored, for example, in system memory 702, and such program modules 7024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 7024 generally perform the functions and / or methods of the embodiments described herein.

[0117] The computing device 70 may also communicate with one or more external devices 704 (e.g., a keyboard, a pointing device, a display, etc.). Such communication may be performed via an input / output (I / O) interface 705. In addition, the computing device 70 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 706. Figure 7 As shown, the network adapter 706 communicates with other modules (such as the processing unit 701, etc.) of the computing device 70 via the bus 703. It should be understood that although Figure 7 Not shown, other hardware and / or software modules may be used in conjunction with computing device 70 .

[0118] The processing unit 701 executes various functional applications and data processing by running the program stored in the system memory 702, for example, analyzing the pre-processed samples based on nuclear magnetic resonance and mercury injection testing to obtain a full-scale pore throat radius data set corresponding to the full-band nuclear magnetic resonance spectrum; analyzing the full-scale pore throat radius data set to obtain the turning point of the fractal dimension curve; determining the pore throat space type of the pre-processed sample based on the turning point of the fractal dimension curve; and quantitatively identifying different modal pore throat structures of the pre-processed sample based on the pore throat space type and content. The specific implementation of each step is not repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the pore throat structure quantitative identification device of the conglomerate reservoir are mentioned in the above detailed description, this division is only exemplary and not mandatory. In fact, according to the embodiment of the present application, the features and functions of two or more units / modules described above can be concretized in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be concretized.

[0119] In the description of the present application, it should be noted that the terms "first", "second" and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0120] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0124] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0125] Finally, it should be noted that the above embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

[0126] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

Claims

1. A method for quantitatively identifying pore throat structure of conglomerate reservoirs, characterized in that: The method comprises: The pre-treated samples were analyzed based on NMR and mercury injection tests, and a full-scale pore throat radius data set corresponding to the full-band NMR spectrum was obtained. The full-scale pore throat radius data set is analyzed to obtain the turning point of the fractal dimension curve; Determining the pore throat space type of the pre-treated sample based on the turning point of the fractal dimension curve; Based on the pore throat space type and the content corresponding to the pore throat space type, different modal pore throat structures of the pre-treated sample are quantitatively identified.

2. The method for quantitatively identifying pore throat structure of conglomerate reservoir according to claim 1, characterized in that: The pre-treated samples are analyzed based on nuclear magnetic resonance and mercury injection testing to obtain a full-scale pore throat radius data set corresponding to the full-band nuclear magnetic resonance spectrum, including: Performing a low-field nuclear magnetic resonance scan on the pre-processed sample to obtain a transverse relaxation time data set of the pre-processed sample; Performing a mercury injection test on the sample after the low-field nuclear magnetic resonance scanning to obtain a mercury saturation data set; interpolating the mercury saturation data set according to the transverse relaxation time data set to obtain a corresponding relationship set between full-band capillary pressure distribution and transverse relaxation time distribution; Data fitting is performed on the corresponding relationship set to obtain the full-scale pore throat radius corresponding to the full-band nuclear magnetic resonance spectrum.

3. The method for quantitatively identifying pore throat structure of conglomerate reservoir according to claim 2, characterized in that: The mercury saturation data set is interpolated according to the transverse relaxation time data set to obtain a corresponding relationship set between the full-band capillary pressure distribution and the transverse relaxation time distribution, including: Determine the cumulative frequency percentage of each transverse relaxation time according to the percentage of the amplitude corresponding to each transverse relaxation time in the transverse relaxation time data set to the sum of all amplitudes; Determining a cumulative frequency percentage of the mercury saturation data set based on each capillary pressure in the mercury saturation data set; The cumulative frequency percentage of the capillary pressure in the mercury saturation data set is interpolated according to the cumulative frequency percentage of each transverse relaxation time to obtain a corresponding relationship set of capillary pressure distribution and transverse relaxation time distribution under the same cumulative frequency percentage.

4. The method for quantitatively identifying pore throat structure of conglomerate reservoir according to claim 1, characterized in that: The analysis of the full-scale pore throat radius data set to obtain the turning point of the fractal dimension curve includes: Taking the logarithm of each pore throat radius and the cumulative volume frequency of each pore throat radius in the full-scale pore throat radius data set, obtaining the logarithm of each pore throat radius and the logarithm of the cumulative volume frequency of each pore throat radius; Based on the logarithm of each pore throat radius and the logarithm of the cumulative volume frequency of each pore throat radius, a scattered point data set is obtained; Performing cluster analysis on the scattered data set to obtain a set of clustered data points with similar characteristics; Perform piecewise linear fitting on the cluster data point set to obtain the fractal dimension curve; The fractal dimension curve is analyzed to obtain the turning point of the fractal dimension curve.

5. The method for quantitatively identifying pore throat structure of conglomerate reservoir according to claim 1, characterized in that: The pore throat space types include large pore throat type, medium pore throat type and small pore throat type; Determining the pore throat space type of the pre-treated sample based on the turning point of the fractal dimension curve includes: Analyzing the turning point of the fractal dimension curve to determine the turning point of the fractal dimension curve as the dividing point of the fractal dimension curve; Based on the dividing point of the fractal dimension curve, the pore throat space type of the pre-treated sample is determined to be a large pore throat type and / or a medium pore throat type and / or a small pore throat type.

6. The method for quantitatively identifying pore throat structure of a conglomerate reservoir according to claim 1, characterized in that: The quantitative identification of different modal pore throat structures of the pre-treated sample based on the pore throat space type and the content corresponding to the pore throat space type includes: If the pore throat space types of the pre-processed sample are large pore throat type, medium pore throat type and small pore throat type, and the content of the large pore throat type is greater than a first preset threshold, then the pre-processed sample is quantitatively identified as a complex modal pore throat structure; If the pore throat space types of the pre-treated sample are the mesopore throat type and the small pore throat type, and the content of the mesopore throat type is greater than the second preset threshold, and the content of the small pore throat type is less than the third preset threshold, then the pre-treated sample is quantitatively identified as a bimodal pore throat structure; If the pore throat space types of the pre-treated sample are the mesopore throat type and the small pore throat type, and the content of the mesopore throat type is less than the second preset threshold and the content of the small pore throat type is greater than the third preset threshold, then the pre-treated sample is quantitatively identified as a single-modal pore throat structure.

7. The method for quantitatively identifying pore throat structure of a conglomerate reservoir according to claim 1, characterized in that: After quantitatively identifying different modal pore throat structures of the pre-treated sample based on the pore throat space type and content, the method further includes: Cast thin sections were used to verify the quantitatively identified different modal pore throat structures of the pre-treated samples.

8. A device for quantitatively identifying pore throat structure of conglomerate reservoir, characterized in that: The device comprises: The first processing module is used to obtain the full-scale pore throat radius of the pre-processed sample based on nuclear magnetic resonance and mercury injection testing; A second processing module is used to determine the turning point of the fractal dimension curve of the pre-processed sample based on the full-scale pore throat radius; A third processing module is used to determine the pore throat space type of the pre-processed sample based on the turning point of the fractal dimension curve; A quantitative identification module is used to quantitatively identify different modal pore throat structures of the pre-processed sample based on the pore throat space type and the content corresponding to the pore throat space type.

9. A computer-readable storage medium, characterized in that: The method comprises instructions, which, when executed on a computer, enable the computer to execute the method for quantitatively identifying the pore-throat structure of a sandstone reservoir as claimed in any one of claims 1 to 7.

10. A computing device, characterized in that The computing device comprises: at least one processor, memory, and input-output unit; The memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the method for quantitatively identifying pore throat structure of a conglomerate reservoir according to any one of claims 1 to 7.

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