A method and system for evaluating the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs

Abstract

The present invention discloses a method and system for evaluating the contribution of mineral intragranular pores to the porosity and permeability of a tight sandstone reservoir, and the method steps include: S1. respectively collecting original experimental data of reservoir pores and target mineral intragranular pores; S2. processing the original experimental data to obtain experimental results; S3. analyzing the experimental results to obtain the relationship between the target mineral intragranular pores and the reservoir pores; S4. based on the experimental results of the reservoir pores and the relationship between the target mineral intragranular pores and the reservoir pores, completing the evaluation of the contribution of the target mineral intragranular pores to the reservoir porosity and permeability. The present invention economically and efficiently obtains the distribution of the radius of the target mineral intragranular pores, the percentage of the target mineral intragranular pores in the movable part of the reservoir pores, the distribution of the reservoir pore throat volume controlled by the target mineral intragranular pores, and the contribution distribution of the target mineral intragranular pores to the reservoir permeability, and innovatively realizes the fine characterization of the contribution of the target mineral intragranular pores to the porosity and permeability of the tight sandstone reservoir.

Description

Technical Field

[0001] The present invention relates to the field of quantitative characterization of tight sandstone pore structure, and in particular to a method and system for evaluating contribution of intragranular pores of minerals to porosity and permeability of tight sandstone reservoirs. Background Art

[0002] With the continuous exploitation of conventional oil and gas resources, unconventional oil and gas resources such as tight sandstone oil and gas have occupied an increasingly important position in the world energy landscape. Tight sandstone reservoirs usually refer to reservoirs with a porosity of less than 10% and a matrix permeability of less than 0.1×10 -3 μm 2 In sandstone reservoirs, the pore size is mainly nanometer-sized. Tight sandstones usually undergo strong compaction and diagenesis, which causes the primary pores to shrink significantly and be filled with filling materials. In addition to the residual primary intergranular pores, secondary pores are an important type of pores in tight sandstones, which have an important influence on the porosity and permeability conditions of tight reservoirs. Secondary pores are usually developed inside mineral particles such as feldspar, calcite, and laurelite. They are usually produced by the contact of these minerals with acidic fluids and are called mineral intragranular pores. Mineral intragranular pores increase the complexity of the pore structure of tight sandstone reservoirs and directly affect the migration, accumulation and production of oil and gas. Studying the contribution of intragranular pores of different minerals to the porosity and permeability of tight sandstone reservoirs helps to discover the "sweet spots" in the low porosity and low permeability background of tight sandstone reservoirs.

[0003] At present, the commonly used methods for measuring the pore structure of tight sandstone reservoirs include high-pressure mercury injection (MICP), nuclear magnetic resonance (NMR) and computed tomography (CT). These methods can provide pore structure information of whole rock samples, but cannot identify different minerals, so it is impossible to measure the contribution of intragranular pores of a certain mineral to reservoir porosity and permeability alone. Rock casting thin section observation and scanning electron microscope-energy spectrum analysis (SEM-EDS) can effectively identify minerals and intuitively reflect the morphological characteristics of intragranular pores of minerals, but it is difficult to obtain their relationship with reservoir porosity and permeability. Usually, it is only evaluated by estimating the percentage of intragranular pores of minerals in the total porosity of the whole rock in the thin section of the reservoir rock, and detailed information on the contribution of intragranular pores of minerals to reservoir porosity and permeability cannot be obtained. Summary of the invention

[0004] To achieve the above object, the present invention provides a method for evaluating the contribution of intragranular pores of minerals to the porosity and permeability of tight sandstone reservoirs based on the advantages and disadvantages of various core testing methods, the steps comprising:

[0005] S1. Collect the original experimental data of reservoir pores and target mineral intragranular pores respectively;

[0006] S2. Processing the original experimental data to obtain experimental results;

[0007] S3. Analyze the experimental results to obtain the relationship between the pores in the target mineral particles and the pores in the reservoir;

[0008] S4. Based on the experimental results and the relationship between the pores within the target mineral particles and the reservoir pores, an evaluation is performed on the contribution of the pores within the target mineral particles to the porosity and permeability of the reservoir.

[0009] Preferably, the original experimental data include: reservoir capillary pressure curve data of MICP experiment, T2 data of NMR experiment, and reservoir whole rock BSEM image and target mineral BSEM image of BSEM-EDS experiment;

[0010] The method for obtaining the reservoir capillary pressure curve data comprises: performing a high-pressure mercury injection test on each core sample to obtain a capillary pressure curve of each sample;

[0011] The method for obtaining the reservoir T2 data comprises: performing nuclear magnetic resonance measurement on each core sample saturated with simulated formation water to obtain a relaxation time T2 curve and a cumulative saturation T2 curve of each saturated water sample; performing centrifugal drainage on the tested saturated water sample to make the sample reach a bound water state, performing nuclear magnetic resonance experiment again, obtaining a relaxation time T2 curve and a cumulative centrifugal T2 curve of each sample after centrifugation; and obtaining a T2 cutoff value through the cumulative saturation T2 curve and the cumulative centrifugal T2 curve;

[0012] The method for obtaining the whole-rock BSEM image of the reservoir and the BSEM image of the target mineral comprises: polishing each core sample into a thin slice with a thickness of about 30 μm using argon ions, and spraying carbon powder on the thin slice to improve the conductivity; using the backscattering mode of the field emission scanning electron microscope combined with energy spectrum analysis to perform mineral identification, and obtaining a plurality of whole-rock BSEM images of the reservoir and a plurality of BSEM images of the target mineral.

[0013] Preferably, the experimental results include: a reservoir pore throat volume distribution curve, a reservoir permeability contribution distribution curve, a reservoir pore radius distribution curve, a reservoir movable fluid pore radius lower limit value and a target mineral intragranular pore radius distribution curve;

[0014] The method for obtaining the reservoir pore throat volume distribution curve and the reservoir permeability contribution distribution curve comprises: obtaining the pore throat volume distribution curve controlled by throats of different sizes in each sample based on the capillary pressure curve data of each MICP sample; obtaining the reservoir pore throat volume distribution curve by calculating the average value of the pore throat volume distribution frequency of all MICP samples at the same throat radius statistical node; obtaining the contribution distribution curve of throats of different sizes in each MICP sample to the permeability of each sample based on the Hagen-Poiseuille equation and Darcy's law; obtaining the reservoir permeability contribution distribution curve by calculating the average value of the permeability contribution of each MICP sample at the same throat radius statistical node;

[0015] The method for obtaining the reservoir pore radius distribution curve and the lower limit value of the pore radius of the movable fluid in the reservoir comprises: obtaining the pore radius cumulative distribution curve and the lower limit value of the pore radius of the movable fluid of each NMR sample based on the correlation between the NMR relaxation time T2 and the pore radius; converting the pore radius cumulative distribution curve of each NMR sample into a pore radius distribution curve, wherein, within the throat radius measurement range of the MICP experiment, the pore radius statistical node of the NMR experiment is kept consistent with the throat radius statistical node of the MICP experiment; obtaining the reservoir pore radius distribution curve by calculating the average value of the distribution frequency of all NMR samples at the same pore radius statistical node; obtaining the lower limit value of the pore radius of the movable fluid of the reservoir by calculating the average value of the lower limit values ​​of the pore radius of the movable fluid of all NMR samples;

[0016] The method for obtaining the target mineral intragranular pore radius distribution curve comprises: binarizing the reservoir whole-rock BSEM image and the target mineral BSEM image by threshold segmentation, wherein the gray value of the pore is 0 and the gray value of the mineral is 255; based on the data statistics of the reservoir whole-rock binarized image, obtaining the percentage of the number of pores in the target mineral particles to the total number of pores in the reservoir; extracting the boundary of each intragranular pore in the target mineral binarized image, converting each pore boundary into its best fitting ellipse based on the least squares method, taking half of the minor axis length of the fitting ellipse as the pore radius, and obtaining the target mineral intragranular pore radius data and its cumulative distribution curve; converting the target mineral intragranular pore radius cumulative distribution curve into the target mineral intragranular pore radius distribution curve, so that its pore radius statistical node is consistent with the reservoir pore radius statistical node of the NMR experiment and the MICP experiment.

[0017] Preferably, the relationship between the pores in the target mineral particles and the pores in the reservoir comprises a distribution curve of the percentage of the number of pores in the target mineral particles to the number of pores in the reservoir;

[0018] The method for obtaining the percentage distribution curve of the number of pores in the target mineral particles to the number of reservoir pores includes: based on the reservoir pore radius distribution curve and the target mineral particle pore radius distribution curve, obtaining the percentage of the number of pores in the target mineral particles to the number of reservoir pores at each pore radius statistical node.

[0019] Preferably, the evaluation results include: a distribution curve of reservoir pore throat volume controlled by pores within target mineral particles, a distribution curve of contribution of pores within target mineral particles to reservoir permeability, and a percentage of pores within target mineral particles in the movable part of reservoir pores;

[0020] The method for obtaining the reservoir pore throat volume distribution curve controlled by the pores in the target mineral particles comprises: obtaining the reservoir pore throat volume distribution curve controlled by the pores in the target mineral particles based on the reservoir pore throat volume distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of pores in the reservoir;

[0021] The method for obtaining the contribution distribution curve of the pores in the target mineral particles to the reservoir permeability includes: obtaining the contribution distribution curve of the pores in the target mineral particles to the reservoir permeability based on the reservoir permeability contribution distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of pores in the reservoir;

[0022] The method for obtaining the percentage of the pores within the target mineral particles in the movable part of the reservoir pores includes: obtaining the percentage of the pores within the target mineral particles in the movable part of the reservoir pores based on the reservoir pore radius distribution curve, the pore radius distribution curve within the target mineral particles and the lower limit value of the pore radius of the movable fluid in the reservoir.

[0023] The present invention also provides an evaluation system for the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs. The system is used to implement the above method and comprises: an experimental data acquisition module, an experimental data processing module, an experimental result analysis module and an evaluation module;

[0024] The experimental data acquisition module is used to respectively acquire the original experimental data of the reservoir pores and the pores within the target mineral particles;

[0025] The experimental data processing module is used to process the original experimental data to obtain experimental results;

[0026] The experimental result analysis module is used to analyze the experimental results to obtain the relationship between the pores in the target mineral particles and the pores in the reservoir;

[0027] The evaluation module is used to evaluate the contribution of the pores within the target mineral particles to the porosity and permeability of the reservoir based on the experimental results of the reservoir pores and the relationship between the pores within the target mineral particles and the reservoir pores.

[0028] Preferably, the original experimental data include: reservoir capillary pressure curve data of MICP experiment, reservoir T2 data of NMR experiment, and reservoir whole rock BSEM image and target mineral BSEM image of BSEM-EDS experiment;

[0029] The workflow for obtaining the reservoir capillary pressure curve data includes: performing a high-pressure mercury injection test on each core sample to obtain a capillary pressure curve of each sample;

[0030] The workflow for obtaining the reservoir T2 data includes: performing nuclear magnetic resonance measurement on each core sample saturated with simulated formation water to obtain a relaxation time T2 curve and a cumulative saturation T2 curve of each saturated water sample; centrifuging and draining the saturated water sample after the test to make the sample reach a bound water state, performing nuclear magnetic resonance experiment again, and obtaining a relaxation time T2 curve and a cumulative centrifugal T2 curve of each sample after centrifugation; and obtaining a T2 cutoff value through the cumulative saturation T2 curve and the cumulative centrifugal T2 curve;

[0031] The workflow for obtaining the whole-rock BSEM image of the reservoir and the target mineral BSEM image includes: polishing each core sample into a thin slice with a thickness of about 30 μm using argon ions, and spraying carbon powder on the thin slice to improve the conductivity; using the backscattering mode of the field emission scanning electron microscope combined with energy spectrum analysis to perform mineral identification, and obtaining several BSEM images of the whole-rock reservoir and several BSEM images of the target mineral.

[0032] Preferably, the workflow of the experimental data processing module includes: obtaining a reservoir pore throat volume distribution curve and a reservoir permeability contribution distribution curve; obtaining a reservoir pore radius distribution curve and a lower limit of a reservoir movable fluid pore radius; obtaining a target mineral particle intragranular pore radius distribution curve;

[0033] The workflow for obtaining the reservoir pore throat volume distribution curve and the reservoir permeability contribution distribution curve includes: based on the capillary pressure curve data of each MICP sample, obtaining the pore throat volume distribution curve controlled by throats of different sizes in each sample; by calculating the average value of the pore throat volume distribution frequency of all MICP samples at the same throat radius statistical node, obtaining the reservoir pore throat volume distribution curve; based on the Hagen-Poiseuille equation and Darcy's law, obtaining the contribution distribution curve of throats of different sizes in each MICP sample to the permeability of each sample; by calculating the average value of the permeability contribution of each MICP sample at the same throat radius statistical node, obtaining the reservoir permeability contribution distribution curve;

[0034] The workflow for obtaining the reservoir pore radius distribution curve and the lower limit of the pore radius of the movable fluid in the reservoir includes: obtaining the pore radius cumulative distribution curve and the lower limit of the pore radius of the movable fluid of each NMR sample based on the correlation between the NMR relaxation time T2 and the pore radius; converting the pore radius cumulative distribution curve of each NMR sample into a pore radius distribution curve, wherein, within the throat radius measurement range of the MICP experiment, the pore radius statistical node of the NMR experiment is kept consistent with the throat radius statistical node of the MICP experiment; obtaining the reservoir pore radius distribution curve by calculating the average value of the distribution frequency of all NMR samples at the same pore radius statistical node; obtaining the lower limit of the pore radius of the movable fluid of the reservoir by calculating the average value of the lower limit of the pore radius of the movable fluid of all NMR samples;

[0035] The workflow for obtaining the target mineral intragranular pore radius distribution curve includes: binarizing the reservoir whole-rock BSEM image and the target mineral BSEM image by threshold segmentation, wherein the grayscale value of the pore is 0 and the grayscale value of the mineral is 255; based on the data statistics of the reservoir whole-rock binarized image, obtaining the percentage of the number of pores within the target mineral grains to the total number of pores in the reservoir; extracting the boundary of each intragranular pore in the target mineral binarized image, converting each pore boundary into its best fitting ellipse based on the least squares method, taking half of the minor axis length of the fitting ellipse as the pore radius, and obtaining the target mineral intragranular pore radius data and its cumulative distribution curve; converting the target mineral intragranular pore radius cumulative distribution curve into the target mineral intragranular pore radius distribution curve, so that its pore radius statistical node is consistent with the reservoir pore radius statistical node of the NMR experiment and the MICP experiment.

[0036] Preferably, the workflow of the experimental result analysis module includes: obtaining a distribution curve of the percentage of the number of pores within target mineral particles to the number of reservoir pores based on the reservoir pore radius distribution curve and the target mineral particle intrapore radius distribution curve.

[0037] Preferably, the workflow of the evaluation module includes: based on the reservoir pore throat volume distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of reservoir pores, obtaining the reservoir pore throat volume distribution curve controlled by the pores in the target mineral particles; based on the reservoir permeability contribution distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of reservoir pores, obtaining the contribution distribution curve of the pores in the target mineral particles to the reservoir permeability; based on the reservoir pore radius distribution curve, the pore distribution curve in the target mineral particles and the lower limit value of the pore radius of the movable fluid in the reservoir, obtaining the percentage of the pores in the target mineral particles to the movable part of the reservoir pores.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. Aiming at the difficulty of evaluating the contribution of a certain target mineral intragranular pore to reservoir porosity and permeability by direct measurement in practice, the present invention proposes a method that comprehensively utilizes high-pressure mercury injection (MICP) experiment, nuclear magnetic resonance (NMR) experiment and scanning electron microscope-energy spectrum analysis (BSEM-EDS) experiment. Based on this method, the pore radius distribution curve of the target mineral intragranular pore in the reservoir, the percentage of the target mineral intragranular pore in the movable part of the reservoir pore, the reservoir pore throat volume distribution curve controlled by the target mineral intragranular pore and the contribution distribution curve of the target mineral intragranular pore to the reservoir permeability can be obtained. Compared with the previous technical methods that usually only provide the percentage of the target mineral intragranular pore in the total rock porosity of the reservoir, this method realizes the fine characterization of the contribution of the target mineral intragranular pore to the reservoir porosity and permeability.

[0040] 2. The present invention proposes a method for obtaining a distribution curve of pore radius within mineral particles based on BESM images. The method distinguishes between minerals and pores by binarizing the BSEM image of the target mineral, and then extracts the boundaries of the pores within the mineral particles. Based on the least squares method, each pore boundary is converted into its best fitting ellipse, and half of the length of the minor axis of the fitting ellipse is taken as the pore radius within the mineral particle. Compared with the method of randomly selecting some pores in the BSEM image for manual measurement, the pore radius data within the mineral particles obtained by this method is more comprehensive and closer to the actual situation.

[0041] 3. The MICP experiment, NMR experiment and BSEM-EDS experiment used in the present invention are conventional experimental methods for characterizing reservoir pore structure. Through the technical solution provided by the present invention, detailed information on the contribution of the pores within the target mineral particles to the reservoir porosity and permeability can be obtained only through image processing, data calculation and analysis, without additional cost, and the method is economical and applicable. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a method framework of an embodiment of the present invention;

[0045] Figure 3 A schematic diagram of the distribution of pore radius within target mineral particles and the distribution of pore radius in reservoirs provided in an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a distribution curve of the percentage of the number of pores in target mineral particles to the number of pores in the reservoir provided in an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of a pore throat volume distribution curve controlled by intragranular pores of a target mineral and a reservoir pore throat volume distribution curve provided in an embodiment of the present invention;

[0048] Figure 6 It is a schematic diagram of the contribution distribution curve of the target mineral intragranular pores to the reservoir permeability and the reservoir permeability contribution distribution curve provided in the embodiments of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Embodiment 1

[0052] like Figure 1 and Figure 2 The figure is a schematic diagram of the method flow and a schematic diagram of the framework of an embodiment of the present invention, and the steps include:

[0053] S1. Collect the original experimental data of reservoir pores and pores within target mineral particles respectively.

[0054] In this embodiment, the original experimental data include: reservoir capillary pressure curve data of high-pressure mercury injection (MICP) experiment, reservoir relaxation time (T2) data of nuclear magnetic resonance (NMR) experiment, and BSEM image of reservoir whole rock and BSEM image of target mineral in backscatter scanning electron microscopy-energy dispersive spectrometry (BSEM-EDS) experiment;

[0055] S101. Obtain reservoir capillary pressure curve data, the specific steps are as follows:

[0056] S1011. Perform a high-pressure mercury injection test on each core sample to obtain a capillary pressure curve of each sample.

[0057] S102. Acquire reservoir T2 data. The specific steps are as follows:

[0058] S1021. Perform nuclear magnetic resonance measurement on each core sample saturated with simulated formation water to obtain a relaxation time T2 curve and a cumulative saturation T2 curve of each saturated water sample;

[0059] S1022. Centrifuge and drain the saturated water sample after the test in the above step to make the sample reach a bound water state, perform a nuclear magnetic resonance experiment again, and obtain a relaxation time T2 curve and a cumulative centrifugal T2 curve of each sample after centrifugation;

[0060] S1023. Obtain the T2 cutoff value through the cumulative saturation T2 curve and the cumulative centrifugal T2 curve.

[0061] S103. Obtaining a BSEM image of the reservoir whole rock and a BSEM image of the target mineral, the specific steps are as follows:

[0062] S1031. Each core sample is polished with argon ions into a thin slice with a thickness of about 30 μm, and carbon powder is sprayed on the slice to improve the conductivity;

[0063] S1032. Use the backscattering mode of the field emission scanning electron microscope combined with energy spectrum analysis to identify minerals, and obtain M BSEM images of the entire reservoir rock and N BSEM images of the target minerals.

[0064] S2. Process the original experimental data to obtain experimental results.

[0065] In this embodiment, the experimental results include: reservoir pore throat volume distribution curve, reservoir permeability contribution distribution curve, reservoir pore radius distribution curve, reservoir movable fluid pore radius lower limit value and target mineral intragranular pore radius distribution curve. The specific experimental data processing steps are as follows:

[0066] S201. Use S101 MICP experimental data to obtain reservoir pore throat volume distribution curve and reservoir permeability contribution distribution curve.

[0067] S2011. Based on the capillary pressure curve data of each MICP sample, the pore throat volume distribution curve controlled by throats of different sizes in each sample is calculated:

[0068]

[0069] In the formula, f Mi The radius is r i The pore throat volume distribution frequency of the reservoir controlled by the throat, %; V i The radius is r i The volume of mercury injected controlled by the throat, unit: cm 3 ; V p is the total volume of injected mercury, unit: cm 3 ; S Hgis the maximum mercury saturation, %.

[0070] S2012. The reservoir pore throat volume distribution curve is obtained by calculating the average value of the pore throat volume distribution frequency of all MICP samples at the same throat radius statistical node:

[0071]

[0072] In the formula, F Mi is the radius r in the reservoir i The pore throat volume distribution frequency controlled by the throat, %; f Mi For each MICP sample, the radius is r i The pore throat volume distribution frequency controlled by the throat, %.

[0073] S2013. Based on the Hagen-Poiseuille equation and Darcy's law, the contribution distribution curve of throats of different sizes in each MICP sample to the permeability of each sample was calculated:

[0074]

[0075] In the formula, k Mi The radius is r i Contribution of throat to reservoir permeability, %; r i is the reservoir throat radius, unit: μm; S i The radius is r i The percentage of injected mercury volume controlled by the throat to the total injected mercury volume, %; V i The radius is r i The volume of mercury injected controlled by the throat, unit: cm 3 ; V p is the total volume of mercury injected, unit: cm 3 .

[0076] S2014. The reservoir permeability contribution distribution curve is obtained by calculating the average permeability contribution of all MICP samples at the same throat radius statistical node:

[0077]

[0078] In the formula, K Mi is the radius r in the reservoir i Contribution of the throat to the reservoir permeability, %; k Mi For each MICP sample, the radius is r i Contribution of throat to reservoir permeability, %.

[0079] S202. Use S102 NMR experimental data to obtain the reservoir pore radius distribution curve and the lower limit of the reservoir movable fluid pore radius.

[0080] S2021. Based on the least squares method, the cumulative saturated T2 curve of each NMR sample is fitted with the cumulative distribution curve of the pore throat volume of the corresponding MICP sample to obtain the conversion coefficient between T2 and pore radius.

[0081] S2022. According to the conversion coefficient between T2 and pore radius, the cumulative saturation T2 curve of each sample is converted into the cumulative distribution curve of pore radius, and the T2 cutoff value of each sample is converted into the lower limit value R of the movable fluid pore radius. icutoff .

[0082] S2023. Convert the pore radius cumulative distribution curve of each NMR sample into a pore radius distribution curve, and make the pore radius statistical node of the NMR experiment consistent with the throat radius statistical node of the MICP experiment within the throat radius measurement range of the MICP experiment.

[0083] S2024. Obtain the reservoir pore radius distribution curve by calculating the average distribution frequency of all NMR samples at the same pore radius statistical node ( Figure 3 ):

[0084]

[0085] In the formula, F Ni is the radius r in the reservoir i The distribution frequency of pores, %; f Ni For each NMR sample with radius r i Distribution frequency of pores, %.

[0086] S2025. Calculate the average value of the movable fluid pore radius lower limit of all NMR samples to obtain the movable fluid pore radius lower limit of the reservoir ( Figure 3 ):

[0087]

[0088] In the formula, R cutoff is the lower limit of the pore radius of the reservoir movable fluid, μm; R icutoff is the lower limit of the movable fluid pore radius for each NMR sample, μm.

[0089] S203. Use S103BSEM-EDS experimental data to obtain the pore radius distribution curve within the target mineral particles.

[0090] S2031. Binarize the BSEM image by threshold segmentation, where the grayscale value of the pores is 0 and the grayscale value of the minerals is 255.

[0091] S2032. Based on the statistical results of the whole-rock binary image of the reservoir, the percentage of the number of pores within the target mineral particles to the total number of pores in the reservoir is calculated by the following formula:

[0092]

[0093] Where p is the percentage of the pores in the target mineral particles to the total pores in the reservoir, %; Q i is the number of all pores in the i-th full-rock binary image; q i is the number of pores in the target mineral grains in the i-th whole-rock binary image; M is the number of whole-rock binary images.

[0094] S2033. Extract the boundary of each intragranular pore in the binary image of the target mineral, transform each pore boundary into its best fitting ellipse based on the least squares method, take half of the short axis length of the fitting ellipse as the pore radius, and obtain the intragranular pore radius data of the target mineral and its cumulative distribution curve

[0095] S2034. Convert the cumulative distribution curve of the pore radius within the target mineral particles into the distribution curve of the pore radius within the target mineral particles in the reservoir, so that the statistical nodes of the pore radius are consistent with the statistical nodes of the reservoir pore radius of the NMR experiment and the MICP experiment ( Figure 3 ):

[0096]

[0097] In the formula, F i is the radius r in the target mineral i The distribution frequency of pores, %; n i is the number of pores in the target mineral from the i-1th to the i-th pore radius statistical node (including the i-th pore radius statistical node) (when i=1, n1 is the number of pores from the minimum pore radius in the target mineral grains to the first pore radius statistical node counted in the above S2033); n is the total number of pores in the target mineral; p is the percentage of the number of pores in the target mineral grains to the total number of pores in the reservoir, %.

[0098] S3. Analyze the experimental results to obtain the relationship between the pores within the target mineral particles and the pores in the reservoir.

[0099] In this embodiment, the relationship between the pores in the target mineral particles and the pores in the reservoir includes a distribution curve of the percentage of the number of pores in the target mineral particles to the number of pores in the reservoir.

[0100] S301. Based on the S202 reservoir pore radius distribution curve and the S203 target mineral intragranular pore radius distribution curve ( Figure 3 ), and obtain the percentage distribution curve of the number of pores in the target mineral particles to the number of pores in the reservoir ( Figure 4 ):

[0101]

[0102] In the formula, is the radius r in the target mineral i The number of pores with radius r in the reservoir i The percentage of pore number, %; F i is the radius r in the target mineral i The distribution frequency of pores, %; F Ni is the radius r in the reservoir i Distribution frequency of pores, %.

[0103] S4. Based on the experimental results of reservoir porosity and the relationship between the intragranular pores of the target mineral and the reservoir pores, the contribution of the intragranular pores of the target mineral to the reservoir porosity and permeability is evaluated.

[0104] In this embodiment, the evaluation results include: the reservoir pore throat volume distribution curve controlled by the intragranular pores of the target mineral, the contribution distribution curve of the intragranular pores of the target mineral to the reservoir permeability, and the percentage of the intragranular pores of the target mineral in the movable part of the reservoir pores.

[0105] S401. Based on the reservoir pore throat volume distribution curve S201 and the percentage distribution curve of the number of pores in the target mineral particles to the number of pores in the reservoir S301, the reservoir pore throat volume distribution curve controlled by the pores in the target mineral particles is obtained ( Figure 5 ):

[0106]

[0107] In the formula, C Fi is the radius r in the target mineral i The volume percentage of reservoir pore throats controlled by pores, %; is the radius r in the target mineral i The number of pores with radius r in the reservoir i The percentage of pore number, %; F Mi is the radius r in the reservoir i The volume percentage of reservoir pore throats controlled by pores, %.

[0108] S402. Based on the S201 reservoir permeability contribution distribution curve and the S301 target mineral intragranular pore number to reservoir pore number distribution curve, obtain the target mineral intragranular pore contribution distribution curve to the reservoir permeability ( Figure 6 ):

[0109]

[0110] In the formula, C Ki is the radius r in the target mineral iContribution of pores to reservoir permeability, %; is the radius r in the target mineral i The number of pores with radius r in the reservoir i The percentage of pore number, %; K Mi is the radius r in the reservoir i Contribution of pores to reservoir permeability, %.

[0111] S403. Based on the reservoir pore radius distribution curve S202 and the lower limit of the reservoir movable fluid pore radius, S203 the target mineral particle pore distribution curve, obtain the percentage of the target mineral particle pores in the movable part of the reservoir pores:

[0112]

[0113] In the formula, S m is the percentage of pores in target mineral particles to the movable part of reservoir pores, %; p is the percentage of pores in target mineral particles to the total number of reservoir pores, %; The radius of the target mineral is greater than or equal to R cutoff The probability of pores, %; The radius of the reservoir is greater than or equal to R cutoff The probability of pores, %; R cutoff It is the lower limit of the pore radius of the movable fluid in the reservoir, unit: μm.

[0114] Embodiment 2

[0115] This embodiment also provides an evaluation system for the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs, including: an experimental data acquisition module, an experimental data processing module, an experimental result analysis module and an evaluation module. The experimental data acquisition module is used to collect the original experimental data of reservoir pores and target mineral intragranular pores; the experimental data processing module is used to process the original experimental data to obtain experimental results; the experimental result analysis module is used to analyze the experimental results to obtain the relationship between the target mineral intragranular pores and reservoir pores; the evaluation module is used to complete the evaluation of the contribution of the target mineral intragranular pores to the reservoir porosity and permeability based on the experimental results of the reservoir pores and the relationship between the target mineral intragranular pores and the reservoir pores.

[0116] The original experimental data acquired by the experimental data acquisition module include: reservoir capillary pressure curve data of MICP experiment, reservoir T2 data of NMR experiment, and reservoir whole rock BSEM image and BSEM image of target minerals of BSEM-EDS experiment.

[0117] The workflow for obtaining reservoir capillary pressure curve data includes: performing high-pressure mercury injection testing on each core sample to obtain a capillary pressure curve for each sample;

[0118] The workflow for obtaining reservoir T2 data includes: performing nuclear magnetic resonance measurement on each core sample saturated with simulated formation water to obtain the relaxation time T2 curve and cumulative saturation T2 curve of each saturated water sample; centrifuging and draining the saturated water sample after the test to make the sample reach the bound water state, performing nuclear magnetic resonance experiment again, and obtaining the relaxation time T2 curve and cumulative centrifugal T2 curve of each sample after centrifugation; obtaining the T2 cutoff value through the cumulative saturation T2 curve and the cumulative centrifugal T2 curve;

[0119] The workflow for obtaining whole-rock BSEM images of the reservoir and BSEM images of target minerals includes: polishing each core sample with argon ions into a thin slice with a thickness of about 30 μm, and spraying carbon powder on the slice to improve the electrical conductivity; using the backscattering mode of the field emission scanning electron microscope combined with energy spectrum analysis for mineral identification, and obtaining M whole-rock BSEM images of the reservoir and N BSEM images of the target minerals.

[0120] The workflow of the experimental data processing module includes: obtaining the reservoir pore throat volume distribution curve and the reservoir permeability contribution distribution curve; obtaining the reservoir pore radius distribution curve and the lower limit of the reservoir movable fluid pore radius; obtaining the target mineral particle intragranular pore radius distribution curve;

[0121] The workflow for obtaining the reservoir pore throat volume distribution curve and the reservoir permeability contribution distribution curve includes: based on the capillary pressure curve data of each MICP sample, obtaining the pore throat volume distribution curve controlled by throats of different sizes in each sample; obtaining the reservoir pore throat volume distribution curve by calculating the average value of the pore throat volume distribution frequency of all MICP samples at the same throat radius statistical node; based on the Hagen-Poiseuille equation and Darcy's law, obtaining the contribution distribution curve of throats of different sizes in each MICP sample to the permeability of each sample; obtaining the reservoir permeability contribution distribution curve by calculating the average value of the permeability contribution of each MICP sample at the same throat radius statistical node;

[0122] The workflow for obtaining the reservoir pore radius distribution curve and the lower limit of the reservoir movable fluid pore radius includes: based on the correlation between the NMR relaxation time T2 and the pore radius, obtaining the pore radius cumulative distribution curve and the lower limit of the movable fluid pore radius of each NMR sample; converting the pore radius cumulative distribution curve of each NMR sample into a pore radius distribution curve, wherein, within the throat radius measurement range of the MICP experiment, the pore radius statistical node of the NMR experiment is kept consistent with the throat radius statistical node of the MICP experiment; obtaining the reservoir pore radius distribution curve by calculating the average value of the distribution frequency of all NMR samples at the same pore radius statistical node; obtaining the lower limit of the movable fluid pore radius of the reservoir by calculating the average value of the lower limit of the movable fluid pore radius of all NMR samples;

[0123] The workflow for obtaining the distribution curve of the intragranular pore radius of the target mineral includes: binarizing the BSEM image by threshold segmentation, where the grayscale value of the pore is 0 and the grayscale value of the mineral is 255; based on the data statistics of the whole-rock binary image of the reservoir, obtaining the percentage of the number of intragranular pores of the target mineral to the total number of pores in the reservoir; extracting the boundary of each intragranular pore in the binary image of the target mineral, converting each pore boundary into its best fitting ellipse based on the least squares method, taking half of the minor axis length of the fitting ellipse as the pore radius, and obtaining the intragranular pore radius data of the target mineral and its cumulative distribution curve; converting the cumulative distribution curve of the intragranular pore radius of the target mineral into the intragranular pore radius distribution curve of the target mineral, so that its pore radius statistical node is consistent with the reservoir pore radius statistical node of the NMR experiment and the MICP experiment.

[0124] The workflow of the experimental result analysis module includes: based on the reservoir pore radius distribution curve and the target mineral particle pore radius distribution curve, obtaining the percentage distribution curve of the target mineral particle pore number to the reservoir pore number.

[0125] The workflow of the evaluation module includes: based on the reservoir pore throat volume distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of reservoir pores, obtain the reservoir pore throat volume distribution curve controlled by the pores in the target mineral particles; based on the reservoir permeability contribution distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of reservoir pores, obtain the contribution distribution curve of the pores in the target mineral particles to the reservoir permeability; based on the reservoir pore radius distribution curve, the pore distribution curve in the target mineral particles and the lower limit of the pore radius of the movable fluid in the reservoir, obtain the percentage of the pores in the target mineral particles to the movable part of the reservoir pores.

[0126] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for evaluating the contribution of mineral intragranular pores to the porosity and permeability of a tight sandstone reservoir, characterized in that the steps include: S1. Collect the original experimental data of reservoir pores and target mineral intragranular pores respectively; The original experimental data include: reservoir capillary pressure curve data of MICP experiment, T2 data of NMR experiment, and reservoir whole rock BSEM image and target mineral BSEM image of BSEM-EDS experiment; S2. Processing the original experimental data to obtain experimental results; the experimental results include: reservoir pore throat volume distribution curve, reservoir permeability contribution distribution curve, reservoir pore radius distribution curve, reservoir movable fluid pore radius lower limit value and target mineral particle pore radius distribution curve; the method for obtaining the target mineral particle pore radius distribution curve includes: binarizing the reservoir whole rock BSEM image and the target mineral BSEM image by threshold segmentation, wherein the gray value of the pore is 0 and the gray value of the mineral is 255; based on the data of the reservoir whole rock binarization image According to the statistical results, the percentage of the number of pores in the target mineral particles to the total number of pores in the reservoir is obtained; the boundary of each intragranular pore is extracted from the target mineral binary image, and each pore boundary is converted into its best fitting ellipse based on the least squares method, and half of the short axis length of the fitting ellipse is taken as the pore radius to obtain the target mineral particle pore radius data and its cumulative distribution curve; the target mineral particle pore radius cumulative distribution curve is converted into the target mineral particle pore radius distribution curve, so that its pore radius statistical node is consistent with the reservoir pore radius statistical node of the NMR experiment and the MICP experiment; S3. Analyze the experimental results to obtain the relationship between the pores in the target mineral particles and the pores in the reservoir; S4. Based on the experimental results and the relationship between the pores within the target mineral particles and the reservoir pores, an evaluation is performed on the contribution of the pores within the target mineral particles to the porosity and permeability of the reservoir.

2. The method for evaluating the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs according to claim 1, characterized in that: The method for obtaining the reservoir capillary pressure curve data comprises: performing a high-pressure mercury injection test on each core sample to obtain a capillary pressure curve of each sample; The method for obtaining the reservoir T2 data comprises: performing nuclear magnetic resonance measurement on each core sample saturated with simulated formation water to obtain a relaxation time T2 curve and a cumulative saturation T2 curve of each saturated water sample; performing centrifugal drainage on the tested saturated water sample to make the sample reach a bound water state, performing nuclear magnetic resonance experiment again, obtaining a relaxation time T2 curve and a cumulative centrifugal T2 curve of each sample after centrifugation; and obtaining a T2 cutoff value through the cumulative saturation T2 curve and the cumulative centrifugal T2 curve; The method for obtaining the whole-rock BSEM image of the reservoir and the BSEM image of the target mineral comprises: polishing each core sample into a thin slice with a thickness of about 30 μm using argon ions, and spraying carbon powder on the thin slice to improve the conductivity; using the backscattering mode of the field emission scanning electron microscope combined with energy spectrum analysis to perform mineral identification, and obtaining a plurality of whole-rock BSEM images of the reservoir and a plurality of BSEM images of the target mineral.

3. The method for evaluating the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs according to claim 2, characterized in that: The method for obtaining the reservoir pore throat volume distribution curve and the reservoir permeability contribution distribution curve comprises: obtaining the pore throat volume distribution curve controlled by throats of different sizes in each sample based on the capillary pressure curve data of each MICP sample; obtaining the reservoir pore throat volume distribution curve by calculating the average value of the pore throat volume distribution frequency of all MICP samples at the same throat radius statistical node; obtaining the contribution distribution curve of throats of different sizes in each MICP sample to the permeability of each sample based on the Hagen-Poiseuille equation and Darcy's law; obtaining the reservoir permeability contribution distribution curve by calculating the average value of the permeability contribution of each MICP sample at the same throat radius statistical node; The method for obtaining the reservoir pore radius distribution curve and the lower limit value of the reservoir movable fluid pore radius comprises: obtaining the pore radius cumulative distribution curve and the movable fluid pore radius lower limit value of each NMR sample based on the correlation between the NMR relaxation time T2 and the pore radius; converting the pore radius cumulative distribution curve of each NMR sample into a pore radius distribution curve, wherein, within the throat radius measurement range of the MICP experiment, the pore radius statistical node of the NMR experiment is kept consistent with the throat radius statistical node of the MICP experiment; obtaining the reservoir pore radius distribution curve by calculating the average value of the distribution frequency of all NMR samples at the same pore radius statistical node; and obtaining the movable fluid pore radius lower limit value of the reservoir by calculating the average value of the movable fluid pore radius lower limit value of all NMR samples.

4. The method for evaluating the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs according to claim 1, characterized in that: The relationship between the pores in the target mineral particles and the pores in the reservoir includes a distribution curve of the percentage of the pores in the target mineral particles to the pores in the reservoir; The method for obtaining the percentage distribution curve of the number of pores in the target mineral particles to the number of reservoir pores includes: based on the reservoir pore radius distribution curve and the target mineral particle pore radius distribution curve, obtaining the percentage of the number of pores in the target mineral particles to the number of reservoir pores at each pore radius statistical node.

5. The method for evaluating the contribution of intragranular pores of minerals to the porosity and permeability of tight sandstone reservoirs according to claim 1, characterized in that: The evaluation results include: a distribution curve of reservoir pore throat volume controlled by intragranular pores of target minerals, a distribution curve of contribution of intragranular pores of target minerals to reservoir permeability, and a percentage of intragranular pores of target minerals in the movable part of reservoir pores; The method for obtaining the reservoir pore throat volume distribution curve controlled by the pores in the target mineral particles comprises: obtaining the reservoir pore throat volume distribution curve controlled by the pores in the target mineral particles based on the reservoir pore throat volume distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of pores in the reservoir; The method for obtaining the contribution distribution curve of the pores in the target mineral particles to the reservoir permeability includes: obtaining the contribution distribution curve of the pores in the target mineral particles to the reservoir permeability based on the reservoir permeability contribution distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of pores in the reservoir; The method for obtaining the percentage of the pores within the target mineral particles in the movable part of the reservoir pores includes: obtaining the percentage of the pores within the target mineral particles in the movable part of the reservoir pores based on the reservoir pore radius distribution curve, the pore radius distribution curve within the target mineral particles and the lower limit value of the pore radius of the movable fluid in the reservoir.

6. A system for evaluating the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs, the system being used to implement the method according to any one of claims 1 to 5, characterized in that: include: Experimental data acquisition module, experimental data processing module, experimental result analysis module and evaluation module; The experimental data acquisition module is used to respectively acquire the original experimental data of the reservoir pores and the pores within the target mineral particles; The experimental data processing module is used to process the original experimental data to obtain experimental results; The experimental result analysis module is used to analyze the experimental results to obtain the relationship between the pores in the target mineral particles and the pores in the reservoir; The evaluation module is used to evaluate the contribution of the pores within the target mineral particles to the porosity and permeability of the reservoir based on the experimental results of the reservoir pores and the relationship between the pores within the target mineral particles and the reservoir pores.

7. The evaluation system for the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs according to claim 6, characterized in that: The original experimental data include: reservoir capillary pressure curve data of MICP experiment, reservoir T2 data of NMR experiment, and reservoir whole rock BSEM image and target mineral BSEM image of BSEM-EDS experiment; The workflow for obtaining the reservoir capillary pressure curve data includes: performing a high-pressure mercury injection test on each core sample to obtain a capillary pressure curve of each sample; The workflow for obtaining the reservoir T2 data includes: performing nuclear magnetic resonance measurement on each core sample saturated with simulated formation water to obtain a relaxation time T2 curve and a cumulative saturation T2 curve of each saturated water sample; centrifuging and draining the saturated water sample after the test to make the sample reach a bound water state, performing nuclear magnetic resonance experiment again, and obtaining a relaxation time T2 curve and a cumulative centrifugal T2 curve of each sample after centrifugation; and obtaining a T2 cutoff value through the cumulative saturation T2 curve and the cumulative centrifugal T2 curve; The workflow for obtaining the whole-rock BSEM image of the reservoir and the target mineral BSEM image includes: polishing each core sample into a thin slice with a thickness of about 30 μm using argon ions, and spraying carbon powder on the thin slice to improve the conductivity; using the backscattering mode of the field emission scanning electron microscope combined with energy spectrum analysis to perform mineral identification, and obtaining several BSEM images of the whole-rock reservoir and several BSEM images of the target mineral.

8. The evaluation system for the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs according to claim 7, characterized in that: The workflow of the experimental data processing module includes: obtaining a reservoir pore throat volume distribution curve and a reservoir permeability contribution distribution curve; obtaining a reservoir pore radius distribution curve and a lower limit of a reservoir movable fluid pore radius; obtaining a target mineral particle intragranular pore radius distribution curve; The workflow for obtaining the reservoir pore throat volume distribution curve and the reservoir permeability contribution distribution curve includes: based on the capillary pressure curve data of each MICP sample, obtaining the pore throat volume distribution curve controlled by throats of different sizes in each sample; by calculating the average value of the pore throat volume distribution frequency of all MICP samples at the same throat radius statistical node, obtaining the reservoir pore throat volume distribution curve; based on the Hagen-Poiseuille equation and Darcy's law, obtaining the contribution distribution curve of throats of different sizes in each MICP sample to the permeability of each sample; by calculating the average value of the permeability contribution of each MICP sample at the same throat radius statistical node, obtaining the reservoir permeability contribution distribution curve; The workflow for obtaining the reservoir pore radius distribution curve and the lower limit of the pore radius of the movable fluid in the reservoir includes: obtaining the pore radius cumulative distribution curve and the lower limit of the pore radius of the movable fluid of each NMR sample based on the correlation between the NMR relaxation time T2 and the pore radius; converting the pore radius cumulative distribution curve of each NMR sample into a pore radius distribution curve, wherein, within the throat radius measurement range of the MICP experiment, the pore radius statistical node of the NMR experiment is kept consistent with the throat radius statistical node of the MICP experiment; obtaining the reservoir pore radius distribution curve by calculating the average value of the distribution frequency of all NMR samples at the same pore radius statistical node; obtaining the lower limit of the pore radius of the movable fluid of the reservoir by calculating the average value of the lower limit of the pore radius of the movable fluid of all NMR samples; The workflow for obtaining the target mineral intragranular pore radius distribution curve includes: binarizing the reservoir whole-rock BSEM image and the target mineral BSEM image by threshold segmentation, wherein the grayscale value of the pore is 0 and the grayscale value of the mineral is 255; based on the data statistics of the reservoir whole-rock binarized image, obtaining the percentage of the number of pores within the target mineral grains to the total number of pores in the reservoir; extracting the boundary of each intragranular pore in the target mineral binarized image, converting each pore boundary into its best fitting ellipse based on the least squares method, taking half of the minor axis length of the fitting ellipse as the pore radius, and obtaining the target mineral intragranular pore radius data and its cumulative distribution curve; converting the target mineral intragranular pore radius cumulative distribution curve into the target mineral intragranular pore radius distribution curve, so that its pore radius statistical node is consistent with the reservoir pore radius statistical node of the NMR experiment and the MICP experiment.

9. The evaluation system for the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs according to claim 6, characterized in that: The workflow of the experimental result analysis module includes: based on the reservoir pore radius distribution curve and the target mineral particle pore radius distribution curve, obtaining a distribution curve of the percentage of the number of pores in the target mineral particle to the number of pores in the reservoir.

10. The evaluation system for the contribution of mineral intragranular pores to the porosity and permeability of tight sandstone reservoirs according to claim 6, characterized in that: The workflow of the evaluation module includes: based on the reservoir pore throat volume distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of reservoir pores, obtaining the reservoir pore throat volume distribution curve controlled by the pores in the target mineral particles; based on the reservoir permeability contribution distribution curve and the percentage distribution curve of the number of pores in the target mineral particles to the number of reservoir pores, obtaining the contribution distribution curve of the pores in the target mineral particles to the reservoir permeability; based on the reservoir pore radius distribution curve, the pore distribution curve in the target mineral particles and the lower limit of the pore radius of the movable fluid in the reservoir, obtaining the percentage of the pores in the target mineral particles to the movable part of the reservoir pores.

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