A method and device for determining the pore size distribution of a reservoir

Abstract

The present invention provides a method and device for determining the pore size distribution of a reservoir. The method includes obtaining a reservoir sample; performing nuclear magnetic resonance on the reservoir sample to obtain a T2 distribution spectrum; extracting pores from the reservoir sample to obtain a plurality of pores, and calculating a comprehensive shape factor of the reservoir sample according to the shape parameters of each pore, where the shape parameters at least include the maximum diameter and area of the pore; performing X-ray diffraction analysis on the reservoir sample to determine the surface relaxation rate of the reservoir sample; and converting the T2 distribution spectrum according to the comprehensive shape factor and the surface relaxation rate to obtain the pore size distribution of the reservoir sample. The method provided by the present invention can calculate the comprehensive shape factor of the reservoir sample according to the maximum diameter and area of the pore and obtain the surface relaxation rate of the reservoir sample according to the X-ray diffraction result, so as to convert the T2 distribution spectrum to obtain the pore size distribution, improve the accuracy of the obtained pore size distribution, and greatly reduce the cost, providing support for the exploration and development of reservoir oil and gas resources.

Description

Technical Field

[0001] This document relates to the technical field of oil and gas exploration, and particularly to a method and device for determining the pore size distribution of a reservoir. Background Art

[0002] The pore structure is an important factor restricting oil and gas exploration. It not only affects the oil and gas storage capacity of the reservoir, but also affects the ease of flow of fluids such as oil and gas in the reservoir. In recent years, the exploration of tight oil and gas and shale oil and gas has become more and more extensive, and the influence of the pore structure on the exploration and development of these reservoirs such as tight oil and gas and shale oil and gas has become more obvious. Therefore, the quantitative characterization of the reservoir pore structure is crucial for oil and gas exploration and development.

[0003] Among the current analysis methods for reservoir pore structure, nuclear magnetic resonance technology is a very important analysis method. Nuclear magnetic resonance technology can obtain the pore structure of a sample by acquiring the nuclear magnetic signal of the fluid filled in the sample without damaging the sample. Therefore, compared with other analysis methods for reservoir pore structure, nuclear magnetic resonance technology has the advantage of being non-destructive. Since nuclear magnetic resonance technology analyzes the reservoir pore structure mainly by converting the nuclear magnetic T2 spectrum distribution of a water-saturated sample into a pore size distribution curve of the pores and then quantitatively evaluating the pore structure, the conversion process is shown as follows:

[0004] r = C × T2;

[0005] where r is the pore diameter, T2 is the surface relaxation time, which is obtained through nuclear magnetic resonance experiments; C is the conversion coefficient. It can be seen that if the T2 spectrum distribution of the nuclear magnetic resonance experiment is to be accurately converted into the pore size distribution of the reservoir pores, an accurate determination of the value of C is required. However, the existing methods for measuring the conversion coefficient, including the method of calculating based on the reservoir surface relaxation rate and pore space shape, and the method of directly assigning values according to different reservoir lithologies, all have the problem of low accuracy. Therefore, the existing nuclear magnetic resonance technology still cannot accurately analyze the reservoir pore structure, restricting the in-depth exploration and development of oil and gas in reservoirs such as shale oil and gas and tight sandstone.

[0006] In view of this, this document aims to provide a method and device for determining the pore size distribution of a reservoir. Summary of the Invention

[0007] Aiming at the above problems of the prior art, the purpose of this document is to provide a method and device for determining the pore size distribution of a reservoir, so as to solve the problem that the inaccurate acquisition of the conversion coefficient in the prior art leads to inaccurate analysis and determination of the pore size distribution of the reservoir pores.

[0008] To solve the above technical problems, the specific technical solutions of this document are as follows:[[]]END]]

[0009] In a first aspect, the present disclosure provides a method for determining the pore size distribution of a reservoir, the method comprising:

[0010] Obtaining a reservoir sample;

[0011] Performing nuclear magnetic resonance on the reservoir sample to obtain a T2 distribution spectrum of the reservoir sample;

[0012] Performing pore extraction on the reservoir sample to obtain a plurality of pores, and calculating a comprehensive shape factor of the reservoir sample according to the shape parameters of each pore, the shape parameters including at least the maximum diameter and area of the pore;

[0013] Performing X-ray diffraction analysis on the reservoir sample, and determining the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis result;

[0014] Converting the T2 distribution spectrum according to the comprehensive shape factor and the surface relaxation rate to obtain the pore size distribution of the reservoir sample.

[0015] Further, performing pore extraction on the reservoir sample to obtain a plurality of pores, and calculating a comprehensive shape factor of the reservoir sample according to the shape parameters of each pore, includes:

[0016] Obtaining an image of the reservoir sample by scanning electron microscopy;

[0017] Identifying a plurality of pores and their shape parameters in the image according to a preset gray threshold;

[0018] Calculating a shape factor of each pore according to the shape parameters of each pore;

[0019] Calculating a comprehensive shape factor of the reservoir sample according to the shape factor of each pore and the area of each pore.

[0020] Specifically, the shape factor of each pore is calculated by the following formula:

[0021]

[0022] where F i is the shape factor of the i-th pore; D imax is the maximum diameter of the i-th pore; S i is the area of the i-th pore.

[0023] Preferably, the shape parameters further include the minimum diameter and perimeter of the pore, and the shape factor of each pore is calculated by the following formula:

[0024]

[0025] where F iis the shape factor of the i-th pore; D imax is the maximum diameter of the i-th pore; D imin is the minimum diameter of the i-th pore; L i is the perimeter of the i-th pore; S i is the area of the i-th pore.

[0026] Specifically, the comprehensive shape factor of the reservoir sample is calculated by the following formula:

[0027]

[0028] where F is the comprehensive shape factor; F i is the shape factor of the i-th pore; S i is the area of the i-th pore; the value range of i is from 1 to n, and n is the number of identified pores.

[0029] Preferably, before calculating the comprehensive shape factor of the reservoir sample according to the shape factor of each pore and the area of each pore, the method further includes:

[0030] Screen the identified pores according to a preset area threshold, and remove the pores with pore area smaller than the area threshold.

[0031] Specifically, determining the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis results includes:

[0032] According to the X-ray diffraction results, determine the content of each mineral in the reservoir sample, and the minerals include at least one or a combination of several of pyrite, quartz, potassium feldspar, plagioclase, spinel, carbonate rock minerals, clay content, and siderite;

[0033] Calculate the surface relaxation rate of the reservoir sample according to the content of each mineral, the weight coefficient of each mineral, and the constant coefficient.

[0034] Furthermore, the surface relaxation rate of the reservoir sample is calculated by the following formula:

[0035]

[0036] where ρ is the surface relaxation rate of the reservoir sample, with the unit of μm / s; a0 is the constant coefficient; x j is the j-th mineral; a j is the weight coefficient of the j-th mineral; the value range of j is from 1 to N, and N is the number of determined minerals;

[0037] Among them, a0 is 15.3; x1 is pyrite, a1 is -1.72; x2 is quartz, a2 is -2.16; x3 is potassium feldspar, a3 is 0.18; x4 is plagioclase, a4 is 1.36; x5 is spinel, a5 is 1.93; x6 is carbonate rock mineral, a6 is 1.90; x7 is clay, a7 is -0.37; and x8 is siderite, a8 is 1.35.

[0038] Preferably, before pore extraction of the reservoir sample, the method further includes:

[0039] extracting the reservoir sample to remove soluble organic matter therein; and

[0040] performing argon ion polishing treatment on the reservoir sample after extraction treatment.

[0041] In a second aspect, the present invention also provides a device for determining the pore size distribution of a reservoir, the device includes:

[0042] an acquisition module for acquiring a reservoir sample;

[0043] a T2 distribution spectrum acquisition module for performing nuclear magnetic resonance on the reservoir sample to obtain the T2 distribution spectrum of the reservoir sample;

[0044] a comprehensive shape factor calculation module for extracting multiple pores from the reservoir sample and calculating the comprehensive shape factor of the reservoir sample according to the shape parameters of each pore, the shape parameters at least including the maximum diameter and area of the pore;

[0045] a surface relaxation rate determination module for performing X-ray diffraction analysis on the reservoir sample and determining the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis result;

[0046] a pore size distribution acquisition module for converting the T2 distribution spectrum according to the comprehensive shape factor and the surface relaxation rate to obtain the pore size distribution of the reservoir sample.

[0047] Adopting the above technical solutions, a method and a device for determining the pore size distribution of a reservoir provided in the present invention calculate the comprehensive shape factor of the reservoir sample according to the identified maximum diameter and area of the pores and obtain the surface relaxation rate of the reservoir sample according to the X-ray diffraction result of the reservoir, and convert the T2 distribution spectrum obtained by nuclear magnetic resonance to finally determine the pore size distribution of the reservoir, which can improve the accuracy of determining the pore size distribution of the reservoir and reduce costs; provide support for the analysis of the pore size distribution of the reservoir, and is conducive to the in-depth exploration and development of reservoir oil and gas resources.

[0048] In order to make the above and other purposes, features and advantages of this article more obvious and understandable, the following provides preferred embodiments in conjunction with the accompanying drawings and makes a detailed description as follows. Brief Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the embodiments of this article or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of this article. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0050] Figure 1 The schematic diagram of the steps of a method for determining the pore size distribution of a reservoir provided by an embodiment of this article is shown;

[0051] Figure 2 The schematic diagram of the steps of calculating the comprehensive shape factor of a reservoir sample in an embodiment of this article is shown;

[0052] Figure 3 The image of a reservoir sample under a scanning electron microscope is shown;

[0053] Figure 4 The image of a reservoir sample after gray threshold processing is shown;

[0054] Figure 5 The schematic diagram of the steps of determining the surface relaxation rate of a reservoir sample in an embodiment of this article is shown;

[0055] Figure 6 The T2 distribution spectrum of a reservoir sample obtained by nuclear magnetic resonance is shown;

[0056] Figure 7 The schematic diagram of the pore size distribution of a reservoir sample is shown;

[0057] Figure 8 The schematic structural diagram of a device for determining the pore size distribution of a reservoir provided by an embodiment of this article is shown;

[0058] Figure 9 The schematic structural diagram of a computer device provided by an embodiment of this article is shown.

[0059] Explanation of the Reference Signs in the Drawings:

[0060] 81, Acquisition Module;

[0061] 82, T2 Distribution Spectrum Acquisition Module;

[0062] 83, Comprehensive Shape Factor Calculation Module;

[0063] 84, Surface Relaxation Rate Determination Module;

[0064] 85. Aperture distribution acquisition module;

[0065] 902. Computer device;

[0066] 904. Processor;

[0067] 906. Memory;

[0068] 908. Driving mechanism;

[0069] 910. Input / output module;

[0070] 912. Input device;

[0071] 914. Output device;

[0072] 916. Rendering device;

[0073] 918. Graphical user interface;

[0074] 920. Network interface;

[0075] 922. Communication link;

[0076] 924. Communication bus. Detailed implementation manners

[0077] Next, the technical solutions in the embodiments of this article will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this article. Obviously, the described embodiments are only a part of the embodiments of this article, rather than all the embodiments. Based on the embodiments in this article, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this article.

[0078] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this article described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or equipment that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or equipment.

[0079] In the prior art, nuclear magnetic resonance technology is commonly used to analyze the pore structure of reservoirs. However, this technology requires converting the T2 spectrum of the obtained reservoir using a conversion coefficient. The accuracy of the conversion coefficient will directly affect the accuracy of the determination of the pore structure. Generally, the methods for obtaining the conversion coefficient are as follows:

[0080] Calculated through the surface relaxation rate and pore shape factor of the reservoir. The problem with this method is that the surface relaxation rate is related to the types of reservoir rock minerals. The surface relaxation rates of reservoirs with different mineral types vary greatly, and the mineral composition of the reservoir is very complex. Therefore, it is difficult to accurately measure the surface relaxation rate of the reservoir. The pore shape factor is mainly related to the pore type and pore shape of the reservoir. The pores in the reservoir are very small, have complex shapes, and are numerous, resulting in difficulty in characterizing the pore shape factor. The current mainstream method for determining the pore shape factor is to directly assign values to the pore shape factor according to the pore shape: assign a shape factor of 2 to columnar pores and a shape factor of 3 to spherical pores, etc. However, this direct assignment method cannot truly reflect the complex pore shapes in the reservoir, resulting in a low accuracy of the pore shape factor of the reservoir. Ultimately, it affects the accuracy of the conversion coefficient value.

[0081] Directly assign values to the conversion coefficient according to the different lithologies of the reservoir: for example, assign a conversion coefficient of 10 to sandstone reservoirs and a conversion coefficient of 7 to tight sandstone reservoirs, etc. In addition, it can also be determined by the analogy method. The analogy method is to perform shape matching and comparison on the T2 spectrum obtained by nuclear magnetic resonance and the pore size distribution curve obtained by mercury intrusion test (or nitrogen adsorption experiment), and then convert the corresponding T2 value and the pore size obtained by mercury intrusion (or nitrogen adsorption experiment) to obtain the conversion coefficient. Although the empirical method and the analogy method can avoid the problem of difficult accurate acquisition of the surface relaxation rate and pore shape factor, they also have the following drawbacks:

[0082] Due to the heterogeneity of the reservoir, a unified conversion coefficient cannot be used to convert the T2 spectrum to the pore size distribution for samples with similar lithologies; the pore size distributions of different samples vary greatly, and there are also certain differences in the conversion coefficient values. Calculating the conversion coefficients of all samples with the same lithology according to the same value ultimately leads to large errors; in order to improve the accuracy of the conversion coefficient, both the empirical method and the analogy method require multiple samples and multiple experiments for comprehensive determination, which is time-consuming and laborious, and the constant pressure mercury intrusion experiment and nitrogen adsorption experiment are expensive, and the cost of this method is very high.

[0083] To solve the above problems, the embodiments of this article provide a method for determining the pore size distribution of a reservoir, which can improve the accuracy of obtaining the pore size distribution of the reservoir and reduce the cost of determining the pore size distribution. Figure 1It is a schematic diagram of the steps of a method for determining the pore size distribution of a reservoir provided by an embodiment of this article. This specification provides the method operation steps as described in the embodiment or flowchart, but based on routine or non-creative labor, it may include more or fewer operation steps. The step order listed in the embodiment is only one way among the execution orders of numerous steps and does not represent the only execution order. When the actual system or device product executes, it can be executed in the method order shown in the embodiment or the accompanying drawings or executed in parallel. Specifically, as Figure 1 shown, the method may include:

[0084] S110: Obtain a reservoir sample.

[0085] S120: Perform nuclear magnetic resonance on the reservoir sample to obtain the T2 distribution spectrum of the reservoir sample.

[0086] Specifically, obtain at least a part of the reservoir sample; perform centrifugation on the at least a part to remove the retained fluid therein; soak the at least a part after centrifugation to make it reach a saturated state; perform nuclear magnetic resonance on the first at least a part that has reached the saturated state to obtain the T2 distribution spectrum.

[0087] S130: Extract pores from the reservoir sample to obtain a plurality of pores, and calculate the comprehensive shape factor of the reservoir sample according to the shape parameters of each pore. The shape parameters include at least the maximum diameter and area of the pore.

[0088] S140: Perform X-ray diffraction analysis on the reservoir sample, and determine the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis result.

[0089] S150: Convert the T2 distribution spectrum according to the comprehensive shape factor and the surface relaxation rate to obtain the pore size distribution of the reservoir sample.

[0090] A method for determining the pore size distribution of a reservoir provided by an embodiment of this specification calculates the comprehensive shape factor of the reservoir sample according to the identified maximum diameter and area of the pores, and obtains the surface relaxation rate of the reservoir sample according to the X-ray diffraction result of the reservoir. The T2 distribution spectrum obtained by nuclear magnetic resonance is converted to finally determine the pore size distribution of the reservoir, which can improve the accuracy of determining the pore size distribution of the reservoir and greatly reduce the cost. It provides support for the analysis of the pore size distribution of reservoirs (especially tight and shale reservoirs), which is beneficial to the in-depth exploration and development of reservoir oil and gas resources.

[0091] Before extracting pores from the reservoir sample in step S130, the method further includes:

[0092] Perform pretreatment on the reservoir sample for pore extraction.

[0093] The pretreatment includes:

[0094] Cut the reservoir sample to a size of 1 cm 3 size;

[0095] Perform Soxhlet extraction on the cut reservoir sample to remove the soluble organic matter therein; and

[0096] Perform argon ion polishing on the reservoir sample after the extraction treatment to make its surface flat and easy to observe;

[0097] After plugging a conductive film on the surface of the polished reservoir sample, place it in the observation chamber of a scanning electron microscope for subsequent pore extraction steps.

[0098] As Figure 2 shown, in the embodiment of this specification, in step S130, performing pore extraction on the reservoir sample to obtain a plurality of pores, and calculating the comprehensive shape factor of the reservoir sample according to the shape parameters of each pore may further include:

[0099] S210: Obtain an image of the reservoir sample through a scanning electron microscope.

[0100] Specifically, a Zeiss CrossBeam 540 field emission scanning electron microscope can be selected to perform a scanning electron microscope analysis experiment on the sample. Place the sample in the observation chamber of the instrument, perform imaging through backscattered electrons, with an acceleration voltage of 8 kV, to obtain an image of the reservoir sample under the scanning electron microscope, as Figure 3 shown.

[0101] S220: Identify a plurality of pores and their shape parameters in the image according to a preset gray threshold.

[0102] Specifically, the image file can be imported into the image processing software Image J and converted into an 8-bit image; use a preset threshold to extract the image of the reservoir sample, and an image of the pores of the reservoir sample can be obtained (as Figure 4 shown); use the Analyze-Set measurements function in the image processing software Image to determine the shape parameters of each identified pore, that is, determine the maximum diameter and area of each pore. Among them, the maximum diameter (denoted as D imax , unit: μm) refers to the maximum distance between two parallel lines tangent to the pore edge; the area (denoted as S i , unit: μm 2 ), that is, as Figure 4 the area of each pore (set of black pixel points) in it.

[0103] The preset gray threshold can be determined by the following method:

[0104] Using the Threshold function of the image processing software Image J, points with different gray values in the image can be selected;

[0105] Adjust the gray value of the image, and determine the gray value range of the pores according to the change of the area corresponding to the pores in the image under different gray values;

[0106] According to the determined gray value range, determine the gray threshold between the pores and the minerals. Specifically, after adjusting the gray value of the image, it is found that the gray value range of the pores is 0-139, then the gray threshold between the pores and the minerals can be determined to be 139, and then the pores in the image can be identified based on this gray threshold.

[0107] S230: Calculate the shape factor of each pore according to the shape parameters of each pore.

[0108] That is, according to the maximum diameter and area of each pore, calculate the shape factor of each pore, and the calculation formula is as follows:

[0109]

[0110] Among them, F i is the shape factor of the i-th pore; D imax is the maximum diameter of the i-th pore; S i is the area of the i-th pore.

[0111] As shown in Table 1, it is for Figure 4 40 identified pores and their corresponding shape parameters and the shape factors calculated according to the shape parameters.

[0112] Table 1

[0113]

[0114]

[0115] S240: Calculate the comprehensive shape factor of the reservoir sample according to the shape factor of each pore and the area of each pore.

[0116] From Figure 4It can be seen that the pore shapes in the reservoir sample are irregular: the pores in the middle of the image have prominent edges and corners and are distorted in shape, the pores in the upper right part of the image are developed in flakes, and there are a few pores in the lower left part of the image that are developed in a circular shape. However, when calculating the shape factor by formula (1), the two shape parameters mainly considered are the maximum diameter and area of the pores, which is more suitable for the calculation of pores with relatively regular shapes, such as spherical and circular pores. Therefore, there is still a problem that the shape factor of each pore calculated according to the above formula (1) is not accurate enough.

[0117] In view of this, preferably, in the embodiments of the present specification, in addition to the maximum diameter (D imax ) and area (S i ) of each pore, the shape parameters also include the minimum diameter of each pore (denoted as D imin , unit: μm) and the perimeter (denoted as L i , unit: μm); wherein, the minimum diameter refers to the minimum distance between two parallel lines tangent to the pore edge; the perimeter is the peripheral length of each pore (set of black pixel points) in Figure 4 .

[0118] Then preferably, the shape factor of each pore can be calculated by the following formula:

[0119]

[0120] wherein, F i is the shape factor of the i-th pore; D imax is the maximum diameter of the i-th pore; D imin is the minimum diameter of the i-th pore; L i is the perimeter of the i-th pore; S i is the area of the i-th pore.

[0121] The corresponding shape parameters of the 40 pores identified and the shape factors calculated according to formula (2) are shown in Table 2.

[0122] Table 2

[0123]

[0124]

[0125]

[0126] In step S240, according to the shape factor of each pore and the area of each pore, the comprehensive shape factor of the reservoir sample is calculated, specifically, the comprehensive shape factor is calculated by the following formula:

[0127]

[0128] Where F is the comprehensive shape factor; F i is the shape factor of the ith pore, S i is the area of the i-th pore; the value of i ranges from 1 to n, and n is the number of identified pores.

[0129] Specifically, the shape factor shown in Table 1 obtained by formula (1) can be substituted into formula (3) to obtain a comprehensive shape factor F of 0.93; or the shape factor shown in Table 2 obtained by formula (2) can be substituted into formula (3) to obtain a comprehensive shape factor F of 0.79.

[0130] There is an existing method of directly obtaining the comprehensive shape factor based on the pore shape assignment. For example, the comprehensive shape factor of columnar pores is assigned a value of 2, the comprehensive shape factor of spherical pores is assigned a value of 3, and so on. That is, the perfect circular pore has the largest comprehensive shape factor, which is 3; the more irregular the shape of the pore and the more prominent the edges and corners, the smaller the comprehensive shape factor is, and it approaches 0.

[0131] According to the comprehensive shape factor calculation method provided in the embodiments of this specification, the obtained comprehensive shape factor is 0.93 or 0.79. Combined with the above-mentioned existing geological cognition method, it can be known that the pore distribution of the corresponding reservoir is mainly irregular pores with more prominent edges, which is consistent with the obtained reservoir pore image; and compared with the existing geological cognition method, the calculated comprehensive shape factor of the reservoir pore is more accurate.

[0132] It should be noted that the same reservoir sample can be cut into multiple pieces with a size of 1 cm. 3 samples to increase the richness of the observed samples; then, these multiple samples were respectively subjected to the following Figure 2 The steps shown are used to obtain the comprehensive shape factor of each sample; finally, the average value of the comprehensive shape factors of these multiple samples is obtained as the comprehensive shape factor of the reservoir sample to improve the accuracy of the calculation of the comprehensive shape factor of the reservoir.

[0133] It should be noted that the pore space of reservoirs, especially mudstone, is complex, with mainly micron- and nano-scale pores, and micron-scale pores are predominant, that is, micron-scale pores occupy the vast majority of the entire mudstone pores; however, the flow of underground fluids such as oil and gas in mudstone is mainly dominated by micron-scale pores with good connectivity, while the fluid fluidity in nano-scale pores is poor, so the effectiveness of nano-scale pores is poor.

[0134] In order to reflect the pore shape characteristics of the effective pores in the reservoir (shale) sample as much as possible, before step S240: calculating the comprehensive shape factor of the reservoir sample according to the shape factor of each pore and the area of each pore, the method further includes:

[0135] Screen the identified pores according to a preset area threshold, and remove the pores with pore areas smaller than the area threshold.

[0136] That is, according to the shape factors of the pores with areas greater than or equal to the area threshold and the areas of the pores, the comprehensive shape factor is calculated.

[0137] The area threshold can be set to 0.2 μm 2 , which can not only accurately reflect the shape characteristics of the reservoir pores, but also reduce the number of pores, playing a role in reducing the calculation amount and improving the calculation efficiency. Thus, those shown in Table 1 and Table 2 are all the pores after screening treatment.

[0138] Specifically, before performing X-ray diffraction analysis on the reservoir sample in step S140, the method further includes:

[0139] Preprocess the reservoir sample for X-ray diffraction analysis.

[0140] The preprocessing includes:

[0141] Obtain at least a part of the reservoir sample and grind it to less than 200 mesh;

[0142] Put the ground powder sample into the groove of the XRD sample stage and compact the surface of the groove with a smooth flat glass.

[0143] Thus, in the embodiments of this specification, different parts of the same reservoir sample can be obtained for nuclear magnetic resonance, pore extraction, and X-ray diffraction analysis respectively, so that the pore size distribution result can be obtained with greatly improved processing efficiency, saving time costs.

[0144] As Figure 5 shown, in the embodiments of this specification, in step S140, determining the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis result may further include:

[0145] S510: According to the X-ray diffraction result, determine the content of each mineral in the reservoir sample, where the mineral includes at least one or a combination of several of pyrite, quartz, potassium feldspar, plagioclase, spinel, carbonate rock minerals, clay content, and siderite.

[0146] Specifically, the composition of the mineral corresponding to each characteristic peak can be determined according to the respective characteristic peaks in the X-ray diffraction pattern; the content of each mineral can be determined according to the semi-quantitative method based on the area enclosed by the characteristic peak curve and the coordinate axis.

[0147] S520: Calculate the surface relaxation rate of the reservoir sample based on the content of each mineral, the weight coefficient of each mineral, and the constant coefficient.

[0148] Specifically, the formula for calculating the surface relaxation rate of the reservoir sample is as follows:

[0149]

[0150] where ρ is the surface relaxation rate of the reservoir sample, with the unit of μm / s; a0 is the constant coefficient; x j is the j-th mineral; a j is the weight coefficient of the j-th mineral; the value range of j is from 1 to N, and N is the number of determined minerals;

[0151] Specifically, a0 is 15.3; x1 is pyrite, a1 is -1.72; x2 is quartz, a2 is -2.16; x3 is potassium feldspar, a3 is 0.18; x4 is plagioclase, a4 is 1.36; x5 is spinel, a5 is 1.93; x6 is carbonate rock mineral, a6 is 1.90; x7 is clay, a7 is -0.37; and x8 is siderite, a8 is 1.35.

[0152] Preferably, in the embodiments of this specification, the weight coefficient a j of the above-mentioned j-th mineral can be determined by the principal component analysis method. The larger the value of the weight coefficient a j , the greater the influence of the mineral x j corresponding to this weight coefficient a j on the surface relaxation rate of the reservoir sample; the constant coefficient a0 is the constant coefficient obtained by fitting. Moreover, after determining the weight coefficients of each mineral by the principal component analysis method, for any reservoir sample, the content of each mineral determined by X-ray diffraction analysis can be directly substituted into formula (4) to calculate the surface relaxation rate of the reservoir sample, that is, there is no need to repeatedly determine the weight coefficients of each mineral, improving the processing efficiency.

[0153] In a specific embodiment, X-ray diffraction analysis is performed on the reservoir sample in a certain place to obtain the composition and content of each mineral therein, and the corresponding weight coefficients of each mineral are shown in Table 3:

[0154] Table 3

[0155] Mineral Serial Number Mineral Content (%) Weight Coefficient 1 Pyrite 2 -1.72 2 Quartz 25 -2.06 3 Potassium Feldspar 2.6 0.18 4 Plagioclase 14.8 1.36 5 Spinel 0 1.93 6 Dolomite 42.9 1.90 7 Clay 12.7 0.37 8 Siderite 0 1.35

[0156] Then, according to Table 3 and formula (4), the surface relaxation rate ρ of this reservoir sample is 55 μm / s.

[0157] It should be noted that the ground powder samples can be taken multiple times for X-ray diffraction analysis to obtain the surface relaxation rates corresponding to multiple powder samples; the average value of the surface relaxation rates of these multiple powder samples is obtained as the surface relaxation rate of the reservoir sample, so as to improve the accuracy of the surface relaxation rate and the subsequent pore size distribution.

[0158] Thus, step S150: converting the T2 distribution spectrum according to the comprehensive shape factor F and the surface relaxation rate ρ to obtain the pore size distribution of the reservoir sample can be, converting the T2 distribution spectrum according to the following formula:

[0159] r = ρ × F × T2 (5)

[0160] where r is the pore size distribution of the reservoir sample.

[0161] As Figure 6 shown, it is the T2 distribution spectrum of the reservoir sample obtained by nuclear magnetic resonance; substituting the calculated comprehensive shape factor and surface relaxation rate of the reservoir sample into formula (5), the converted pore size distribution is as Figure 7 shown.

[0162] From Figure 7 it can be seen that there are mainly three types of pores with different sizes in the reservoir sample:

[0163] The diameter of the first type of pores is mainly distributed in the range of 1 - 60 nm, and the peak value is at 12 nm. The volume proportion of this type of pores among all types of pores is the largest, indicating that the reservoir mainly develops micropores of about 12 nm.

[0164] The diameter of the second type of pores is mainly distributed in the range of 60 - 700 nm, and the peak value is at 200 nm, with a medium volume proportion.

[0165] The diameter of the third type of pores is mainly distributed in the range of 700 nm - 10 μm, and the peak value is 2 μm, with the smallest volume proportion.

[0166] Through the above pore size distribution results of the pores, the pore structure and composition of the reservoir can be understood more accurately, and then more appropriate resource calculation methods can be adopted according to the pore structure and composition, and a mining plan more suitable for this reservoir can be designed.

[0167] As Figure 8 shown, it is a device for determining the pore size distribution of a reservoir provided by an embodiment of this specification. The device includes:

[0168] An acquisition module 81, configured to acquire a reservoir sample;

[0169] A T2 distribution spectrum acquisition module 82, configured to perform nuclear magnetic resonance on the reservoir sample to obtain the T2 distribution spectrum of the reservoir sample;

[0170] The comprehensive shape factor calculation module 83 is configured to extract pores from the reservoir sample to obtain a plurality of pores, and calculate the comprehensive shape factor of the reservoir sample according to the shape parameters of each pore, where the shape parameters at least include the maximum diameter and area of the pore;

[0171] The surface relaxation rate determination module 84 is configured to perform X-ray diffraction analysis on the reservoir sample, and determine the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis result;

[0172] The pore size distribution acquisition module 85 is configured to convert the T2 distribution spectrum according to the comprehensive shape factor and the surface relaxation rate to obtain the pore size distribution of the reservoir sample.

[0173] The beneficial effects achieved by the device provided in the embodiments of this specification are consistent with those achieved by the above method, and will not be elaborated here.

[0174] As Figure 9 shown, a computer device provided in an embodiment of this article is described. The reservoir pore size distribution determination device in this specification may be the computer device in this embodiment and execute the above method in this article. The computer device 902 may include one or more processors 904, such as one or more central processing units (CPUs), and each processing unit may implement one or more hardware threads. The computer device 902 may also include any memory 906, which is used to store any type of information such as code, settings, data, etc. Non-limitingly, for example, the memory 906 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory devices, hard disks, optical discs, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 902. In one case, when the processor 904 executes the associated instructions stored in any memory or combination of memories, the computer device 902 may perform any operation of the associated instructions. The computer device 902 also includes one or more drive mechanisms 908 for interacting with any memory, such as a hard disk drive mechanism, an optical disc drive mechanism, etc.

[0175] The computer device 902 may further include an input / output module 910 (I / O) for receiving various inputs (via the input device 912) and for providing various outputs (via the output device 914). A specific output mechanism may include a presentation device 916 and an associated graphical user interface (GUI) 918. In other embodiments, the input / output module 910 (I / O), the input device 912, and the output device 914 may not be included, and it may only be a computer device in the network. The computer device 902 may further include one or more network interfaces 920 for exchanging data with other devices via one or more communication links 922. One or more communication buses 924 couple the components described above together.

[0176] The communication link 922 may be implemented in any way, for example, via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 922 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0177] Corresponding to the method as Figure 1 , Figure 2 and Figure 5 shown, embodiments herein further provide a computer-readable storage medium having a computer program stored thereon, and when the computer program is run by a processor, it executes the steps of the above method.

[0178] Embodiments herein further provide a computer-readable instruction, wherein when the processor executes the instruction, the program therein causes the processor to execute the method as Figure 1 , Figure 2 and Figure 5 shown.

[0179] Embodiments herein further provide a computer program product including at least one instruction or at least one segment of a program, and the at least one instruction or the at least one segment of the program is loaded and executed by a processor to implement the method as Figure 1 , Figure 2 and Figure 5 shown.

[0180] It should be understood that in various embodiments herein, the magnitudes of the sequence numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments herein.

[0181] It should also be understood that in the embodiments herein, the term "and / or" is merely a relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. Additionally, in this text, the character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0182] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this text.

[0183] 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 foregoing method embodiments and will not be elaborated herein.

[0184] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and 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. Additionally, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be in electrical, mechanical, or other forms of connection.

[0185] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments herein.

[0186] In addition, the functional units in each embodiment herein can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0187] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution herein, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments herein. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0188] Specific embodiments are applied in this article to elaborate on the principles and implementation manners of this article. The description of the above embodiments is only used to help understand the method and its core idea herein; at the same time, for those of ordinary skill in the art, according to the idea herein, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this article.

Claims

1. A method for determining the pore size distribution of a reservoir, characterized in that Including: Obtain a reservoir sample; Perform nuclear magnetic resonance on the reservoir sample to obtain the T2 distribution spectrum of the reservoir sample; Perform pore extraction on the reservoir sample to obtain multiple pores, and calculate the comprehensive shape factor of the reservoir sample according to the shape parameters of each pore. The shape parameters at least include the maximum diameter, area, minimum diameter, and perimeter of the pore. The comprehensive shape factor is calculated by the following formula: Among them, F is the comprehensive shape factor; F i is the shape factor of the i-th pore; S i is the area of the i-th pore; the value range of i is from 1 to n, and n is the number of pores identified; Among them, D imax is the maximum diameter of the i-th pore; D imin is the minimum diameter of the i-th pore; L i is the perimeter of the i-th pore; Perform X-ray diffraction analysis on the reservoir sample, and determine the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis result. The surface relaxation rate is calculated by the following formula: where ρ is the surface relaxation rate of the reservoir sample; a0 is a constant coefficient; x j is the j-th mineral; a j is the weight coefficient of the j-th mineral; j ranges from 1 to N, where N is the number of identified minerals; Convert the T2 distribution spectrum according to the comprehensive shape factor and the surface relaxation rate to obtain the pore size distribution of the reservoir sample. The pore size distribution is: r = ρ × F × T2; r is the pore size distribution of the reservoir sample, and T2 is the T2 distribution spectrum.

2. The method according to claim 1, wherein Perform pore extraction on the reservoir sample to obtain multiple pores, and calculate the comprehensive shape factor of the reservoir sample according to the shape parameters of each pore. Further: Obtain an image of the reservoir sample through a scanning electron microscope; Identify multiple pores and their shape parameters in the image according to a preset gray threshold; Calculate the shape factor of each pore according to the shape parameters of each pore; Calculate the comprehensive shape factor of the reservoir sample according to the shape factor of each pore and the area of each pore.

3. The method according to claim 2, wherein The shape factor of each pore is calculated by the following formula: Among them, F i is the shape factor of the i-th pore; D imax is the maximum diameter of the i-th pore; S i is the area of the i-th pore.

4. The method according to claim 2, wherein Before calculating the comprehensive shape factor of the reservoir sample according to the shape factor of each pore and the area of each pore, the method further includes: Screen the identified pores according to a preset area threshold, and remove the pores with a pore area smaller than the area threshold.

5. The method according to claim 1, wherein Determine the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis result. Further including: Determine the content of each mineral in the reservoir sample according to the X-ray diffraction result. The minerals at least include one or a combination of several of pyrite, quartz, potassium feldspar, plagioclase, spinel, carbonate rock minerals, clay content, and siderite; Calculate the surface relaxation rate of the reservoir sample according to the content of each mineral, the weight coefficient of each mineral, and the constant coefficient.

6. The method according to claim 5, wherein The various minerals and their corresponding weight coefficients are: a0 is 15.3; x1 is pyrite, a1 is -1.72; x2 is quartz, a2 is -2.16; x3 is potassium feldspar, a3 is 0.18; x4 is plagioclase, a4 is 1.36; x5 is spinel, a5 is 1.93; x6 is carbonate rock minerals, a6 is 1.90; x7 is clay, a7 is -0.37; and x8 is siderite, a8 is 1.

35.

7. The method according to claim 1, characterized in that Before performing pore extraction on the reservoir sample, the method further includes: Perform extraction on the reservoir sample to remove the soluble organic matter therein; and Perform argon ion polishing treatment on the reservoir sample after the extraction treatment.

8. A device for determining the pore size distribution of a reservoir, characterized in that, The device includes: An acquisition module for acquiring a reservoir sample; A T2 distribution spectrum acquisition module for performing nuclear magnetic resonance on the reservoir sample to obtain the T2 distribution spectrum of the reservoir sample; A comprehensive shape factor calculation module is used to extract pores from the reservoir sample to obtain a plurality of pores, and calculate the comprehensive shape factor of the reservoir sample according to the shape parameters of each pore. The shape parameters at least include the maximum diameter and area of the pore. The comprehensive shape factor is calculated by the following formula: Among them, F is the comprehensive shape factor; F i is the shape factor of the i-th pore; S i is the area of the i-th pore; the value range of i is from 1 to n, and n is the number of pores identified; Among them, D imax is the maximum diameter of the i-th pore; D imin is the minimum diameter of the i-th pore; L i is the perimeter of the i-th pore; A surface relaxation rate determination module is used to perform X-ray diffraction analysis on the reservoir sample, and determine the surface relaxation rate of the reservoir sample according to the X-ray diffraction analysis result. The surface relaxation rate is calculated by the following formula: where ρ is the surface relaxation rate of the reservoir sample; a0 is a constant coefficient; x j is the j-th mineral; a j is the weight coefficient of the j-th mineral; the value of j ranges from 1 to N, and N is the number of minerals determined; An aperture distribution acquisition module is used to convert the T2 distribution spectrum according to the comprehensive shape factor and the surface relaxation rate to obtain the aperture distribution of the reservoir sample. The aperture distribution is: r = ρ × F × T2; r is the pore size distribution of the reservoir sample, and T2 is the T2 distribution spectrum.

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