Quantitative evaluation method and device for pore throat heterogeneity of tight sandstone reservoir and storage medium

Abstract

The application discloses a method and device for quantitatively evaluating pore throat heterogeneity of a tight sandstone reservoir and a storage medium, and belongs to the technical field of oil exploration. Hg ) and LgP c , and drawing a cross-plot of the two; intercepting a fractal interval corresponding to different pore throats by using the cross-plot; fitting a trend line of each fractal interval to obtain a fractal dimension corresponding to different fractal intervals; obtaining a relative fractal dimension of each fractal interval relative to the whole reservoir; and obtaining a parameter capable of comprehensively representing the pore throat heterogeneity of the whole reservoir based on the relative fractal dimension. The new parameter obtained by the method for representing the pore throat heterogeneity of the whole reservoir provides technical support for comprehensively and accurately quantitatively representing the micro-pore throat structure heterogeneity of the tight sandstone reservoir.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, specifically to a method, apparatus, and storage medium for quantitative evaluation of pore throat heterogeneity in tight sandstone reservoirs. Background Technology

[0002] my country's terrestrial clastic reservoirs exhibit complex geological conditions, with frequent interbedded sand and mud layers within a terrestrial sedimentary background, leading to rapid sedimentary facies transitions. Under the influence of sedimentation, diagenesis, and geological alteration, strong heterogeneity exists both within and between reservoirs. Reservoir heterogeneity is a key issue for the development of low-porosity, low-permeability oil and gas reservoirs, and is generally analyzed using comprehensive geological analysis, reservoir geological modeling, and laboratory analysis. Existing related technologies and methods have some shortcomings, such as: failing to consider geological characteristics and pore throat distribution features, relying directly on mathematical models without geological principle support; using single evaluation parameters, which are not comprehensive or accurate enough; or basing empirical models on the correlation between different parameters in a specific region and stratigraphic level, limiting the generalization of these models and methods.

[0003] Compared to conventional reservoirs, tight reservoirs have smaller and more unevenly distributed pore throats, resulting in a more complex microstructure. Previous studies have investigated the pore structure of tight reservoirs based on physical properties, pore throat size, and characteristic parameters of capillary pressure curves. Currently, the mercury intrusion porosimetry capillary pressure method is the most widely used.

[0004] In practice, the characteristic technical parameters of reservoir pore structure are determined by analyzing capillary pressure curves. The main working principle involves using mercury intrusion porosimetry to determine the relationship between saturation and throat radius, thereby identifying the distribution of pore throats and obtaining a series of parameters. Currently, reservoir evaluation using pore structure parameters often employs a multi-parameter approach, such as the displacement pressure parameter (P) representing pore size. T ), maximum throat radius (r) max ), median capillary pressure (Pc) 50 ) and average pore throat radius (r 50 ) etc.; pore throat sorting coefficient (S) representing pore heterogeneity p ), pore throat deviation (S) kp ), pore throat peak state (K p ), number of peaks (N), peak value (X), and peak position (R) in pore throat distribution v ) and homogeneity coefficient (α), etc.; mercury removal efficiency (W) representing the connectivity of pore throats. e Minimum unsaturated pore throat volume percentage (S) min ), tortuosity (L), pore structure coefficient pore throat coordination number, tortuosity coefficient, apparent pore throat volume ratio (V) R ) and structural uniformity (α·W eThe numerous parameters and complex acquisition methods make it difficult to develop a unified reservoir evaluation scheme. Furthermore, within the reservoir space of tight sandstone reservoirs, the distribution of pore throats varies across different pore-throat intervals. Parameters such as porosity, throat sorting coefficient, skewness, and tortuosity only represent the heterogeneity of the test sample point under the average pore-throat structure, and cannot comprehensively and accurately reflect the heterogeneity of the microscopic pore structure of tight sandstone reservoirs. In fact, in the quantitative evaluation of microscopic pore structure, many scholars have confirmed through various experiments and methods that sedimentary rock pore structures exhibit fractal characteristics, and fractal theory is an effective means of describing the complexity and heterogeneity of objects. The pore fractal dimension is one of the parameters used to quantitatively describe the size, heterogeneity, and connectivity of pore throats. However, current calculations of the pore fractal dimension are mostly based on single-point analysis of experimental tests to obtain a single dimension, and there is still no unified characteristic parameter that can accurately quantitatively evaluate the microscopic heterogeneity of the pore structure. Summary of the Invention

[0005] This invention aims to address the aforementioned problems in the evaluation of pore throat heterogeneity in tight sandstone pore structure in existing technologies. It proposes a quantitative evaluation method, device, and storage medium for pore throat heterogeneity in tight sandstone reservoirs, fully considering the differences in pore throat distribution characteristics within different pore throat intervals and optimizing the application of fractal dimension parameters. This provides technical support for comprehensively and accurately quantitatively characterizing the heterogeneity of microscopic pore throat structure in tight sandstone reservoirs.

[0006] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows:

[0007] A method for quantitatively evaluating the pore throat heterogeneity of tight sandstone reservoirs includes the following steps:

[0008] Based on core mercury intrusion porosimetry, obtain mercury intrusion experimental data required for evaluating pore throat heterogeneity;

[0009] Processing mercury porosimetry experimental data, Lg(1-S) was calculated. Hg ) and LgP c And draw the intersection diagram of the two;

[0010] Use the intersection diagram to extract the fractal intervals corresponding to different pore throats;

[0011] The trend lines of each fractal interval are fitted to obtain the fractal dimension corresponding to different fractal intervals;

[0012] Obtain the relative fractal dimension of each fractal interval relative to the entire reservoir;

[0013] The relative fractal dimension of each fractal interval is superimposed to obtain a parameter that can comprehensively characterize the overall pore throat heterogeneity of the reservoir.

[0014] Furthermore, the method of using the intersection graph to extract fractal intervals corresponding to different pore throats includes: dividing the interval into n+1 smaller intervals based on the n inflection points of the curve in the intersection graph, with each interval corresponding to a different level of pore throat size.

[0015] Furthermore, the process of fitting the trend lines of each fractal interval to obtain the fractal dimension corresponding to different fractal intervals includes:

[0016] By fitting the scatter points of each fractal interval, n+1 trend lines are obtained. Based on the trend lines, the relationship between Lg(1-S) and Lg(1-S) is obtained. Hgn )~LgP cn The linear formula is obtained; based on the slope in the linear formula, combined with the principle of fractal dimension and the basic principle of conventional mercury intrusion, the fractal dimension corresponding to different fractal intervals is obtained.

[0017] Furthermore, obtaining the relative fractal dimension of each fractal interval relative to the entire reservoir includes:

[0018] Obtain the overall mercury ingress rate of the reservoir;

[0019] Each inflection point on the curve in the intersection graph corresponds to a mercury ingress pressure and a pore throat radius. The pore throat radius is divided into multiple segments according to the fractal interval division method, and the mercury ingress amount in each segment is used to represent the pore throat distribution frequency of each segment.

[0020] The mercury ingress amount in each interval is normalized to obtain the pore throat distribution frequency of each interval, which is the ratio of the number of pore throats in each fractal interval to the total number of pore throats in the reservoir.

[0021] Multiplying the pore throat distribution frequency by the fractal dimension of each fractal interval yields the relative fractal dimension of each fractal interval relative to the reservoir as a whole.

[0022] Furthermore, the parameter that can comprehensively characterize the overall pore throat heterogeneity of the reservoir is the sum of the relative fractal dimensions of each fractal interval relative to the overall reservoir.

[0023] This invention also provides a device for quantitatively evaluating the pore throat heterogeneity of tight sandstone reservoirs, comprising:

[0024] The parameter acquisition module is used to acquire the mercury intrusion test data required for evaluating the pore throat heterogeneity obtained from the core mercury intrusion test.

[0025] The data processing module is used to process mercury porosimetry experimental data and calculate Lg(1-S) Hg ) and LgP c And draw the intersection diagram of the two;

[0026] The fractal dimension acquisition module is used to extract the fractal intervals corresponding to different pore throats in the intersection diagram, fit the trend lines of each fractal interval, and obtain the fractal dimension corresponding to different fractal intervals.

[0027] The relative fractal dimension acquisition module is used to obtain the relative fractal dimension of each fractal interval relative to the entire reservoir.

[0028] The characterization parameter acquisition module is used to obtain parameters that can comprehensively characterize the overall pore throat heterogeneity of the reservoir based on the relative fractal dimension.

[0029] Furthermore, the fractal dimension acquisition module divides the interval into n+1 smaller intervals based on the n inflection points of the curve in the intersection graph, and each of the divided intervals corresponds to the size of the pore throat at different levels.

[0030] Furthermore, the relative fractal dimension acquisition module includes:

[0031] The mercury ingress measurement unit is used to measure the overall mercury ingress of the reservoir.

[0032] The pore throat radius division unit is used to divide the pore throat radius into multiple segments according to the same division method of the fractal interval, and the mercury ingress amount of each segment is used to represent the pore throat distribution frequency of each segment.

[0033] The pore throat distribution frequency acquisition unit is used to normalize the mercury ingress amount in each interval to obtain the pore throat distribution frequency of each interval, that is, the ratio of the number of pore throats in each fractal interval to the total number of pore throats in the reservoir.

[0034] The relative fractal dimension acquisition unit is used to multiply the pore throat distribution frequency with the fractal dimension of each fractal interval to obtain the relative fractal dimension of each fractal interval relative to the reservoir as a whole.

[0035] The present invention also provides a device for quantitatively evaluating the pore throat heterogeneity of tight sandstone reservoirs, including a processor and a memory for storing processor-executable instructions, wherein the instructions, when executed by the processor, implement the steps in the above-mentioned method for quantitatively evaluating the pore throat heterogeneity of tight sandstone reservoirs.

[0036] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method for quantitative evaluation of pore throat heterogeneity in tight sandstone reservoirs.

[0037] In summary, the present invention has the following advantages:

[0038] 1. This invention provides a quantitative characterization method for the pore throat heterogeneity of tight sandstone reservoirs based on mercury intrusion fractals, optimizes the application of fractal dimension parameters, and provides new parameters for characterizing the overall pore throat heterogeneity of reservoirs obtained by this method, providing technical support for the comprehensive and accurate quantitative characterization of the microscopic pore throat structure heterogeneity of tight sandstone reservoirs.

[0039] 2. Based on the characteristics of tight sandstone reservoirs, such as complex micropore structure, small pore throats and uneven spatial distribution, this invention subdivides the pore throats of tight sandstone reservoirs into several pore throat intervals according to the degree of heterogeneity, and fully considers the differences in pore throat distribution within different pore throat intervals. This can make up for the problems of single parameters and failure to consider the comprehensive distribution of pore throats in previous reservoir heterogeneity evaluations.

[0040] 3. The raw data involved in this invention can be directly obtained through experiments, making the operation simple and highly feasible. The method is scalable and replicable, and is applicable to the characterization of microscopic pore throat heterogeneity in tight sandstone reservoirs at different strata in different regions. Attached Figure Description

[0041] Figure 1 Lg(1-S) in well QL18 in Example 2 Hg )~Lg P c Intersection diagram;

[0042] Figure 2 Lg(1-S) in well QL17 in Example 2 Hg )~Lg P c Intersection diagram;

[0043] Figure 3 For example, Lg(1-S) in well QL202 in Example 2 Hg )~Lg P c Intersection diagram;

[0044] Figure 4 Lg(1-S) in well QL205 in Example 2 Hg )~Lg P c Intersection diagram;

[0045] Figure 5 D of the sample used in Example 2 总 Its relationship with the sorting coefficient;

[0046] Figure 6 D of the sample used in Example 2 总 The relationship between it and its penetration rate. Detailed Implementation

[0047] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further clarifies the invention. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0048] Example 1

[0049] This embodiment provides a method for quantitatively evaluating the heterogeneity of pore throats in tight sandstone reservoirs, including the following steps:

[0050] Step 1: Obtain the mercury saturation (S) of tight sandstone reservoirs based on core mercury intrusion porosimetry experiments. Hg ) and its corresponding mercury inlet pressure (P) c Experimental data such as )

[0051] Step 2: Process the experimental data and calculate Lg(1-S) Hg ) and LgP c And draw the intersection diagram of the two;

[0052] Step 3: Using Lg(1-S) Hg )~Lg P c The intersection plot is used to extract fractal intervals corresponding to different apertures, and the fractal dimension corresponding to different intervals is obtained.

[0053] The specific implementation steps include:

[0054] S31, according to Lg(1-S Hg )~Lg P c The curve has n inflection points, and their coordinates on the intersection graph are (Lg P) ci ,Lg(1-S Hgi )), i = 0, 1, 2, 3, ... n, divide the interval into n+1 smaller intervals, each interval corresponding to the size of the throat at different levels;

[0055] S32. Fit the scatter points of each interval to obtain n+1 trend lines, and obtain the relationship between Lg(1-S) and Lg(1-S) based on the trend lines. Hgn )~LgP cn The linear formula:

[0056] Lg(1-S Hgi ) = a i LgP ci +b i (1)

[0057] Among them, i=0,1,2,3,…,n,a i Let b be the slope of the i-th trend line. i The intercept of the i-th trend line;

[0058] S33. The fractal dimension of pores is one of the comprehensive parameters used to quantitatively describe the size, heterogeneity, and connectivity of pore throats. The ideal fractal dimension is between 2 and 3. The closer it is to 3, the stronger the heterogeneity of the pore throat. The closer it is to 2, the stronger the homogeneity.

[0059] Because the pore-throat structure within dense sandstone is highly complex, its distribution typically exhibits multiple fractal intervals, which can be represented by the fractal dimension of these intervals. Based on the principle of fractal dimension and the fundamental principles of conventional mercury intrusion porosimetry, we have:

[0060] Lg(1-S Hgi )=(D i -3)LgP ci +b i (2)

[0061] Among them, D i Let i be the fractal dimension corresponding to the i-th interval, i = 0, 1, 2, 3, ..., n;

[0062] It can be seen that the absolute fractal dimension of each interval is:

[0063] D i =a i +3; (3)

[0064] Step 4: Using the above formula (3), the degree of heterogeneity of different pore throat intervals can be obtained. The n+1 intervals divided in step S31 correspond to n+1 ranges of pore throat intervals, and the overall heterogeneity of the reservoir depends on the heterogeneity of the n+1 different pore throat intervals. Therefore, it is also necessary to consider the distribution of reservoir pore throats and obtain the fractal dimension of each interval relative to the overall reservoir.

[0065] The specific implementation steps include:

[0066] S41. As the mercury inlet pressure increases, mercury is injected sequentially from the large orifice throat to the small orifice throat, and the amount of mercury injected gradually increases. The amount of mercury injected is obtained by the following formula:

[0067]

[0068] Among them, V Hg This refers to the amount of mercury injected (ml). V represents the porosity (%) of the sample. r Sample volume (cm³) 3 );

[0069] S42. According to different throat radii (R1, R2...R... X The minimum orifice throat radius corresponding to the maximum mercury inlet pressure is R. X， Therefore, the mercury ingress amount can be subdivided into mercury ingress amounts in X-1 stages. The subdivided mercury ingress amounts corresponding to the X-1th to Xth orifice throat radii are calculated as follows:

[0070] V Hgx～x-1 =(V Hgx -V Hgx-1 ) / LOG 10 (R X-1 / R X (5)

[0071] Among them, V Hgx～x-1 V represents the mercury injection volume (ml) corresponding to the radius of the (X-1)th to the Xth pore throat. Hgx This represents the amount of mercury injected (ml) corresponding to the radius of the Xth orifice throat;

[0072] S43, according to step three Lg(1-S Hg )-LgP c The curve has n inflection points, each corresponding to a mercury ingress pressure and a pore throat radius. Therefore, the pore throat radius can be divided into n+1 segments. The mercury ingress rate of each n+1 segment can be used to represent the pore throat distribution frequency of that segment. The mercury ingress rate of each segment is denoted as V. Hgi i = 0, 1, 2, 3, ..., n; the overall distribution frequency of reservoir pore throat is considered to be 1, and the mercury ingress rate V in the interval is... Hgi Normalization, the normalized interval mercury ingress corresponds to the pore throat distribution frequencies of n+1 intervals, denoted as f1, f2, ..., f n+1 And there are

[0073]

[0074] S44. The ratio of the number of pore throats represented by each fractal interval to the total number of pore throats in the sample is f. i Therefore, the fractal dimension D of each interval relative to the whole can be calculated. i :

[0075] D i =D i ×f i (7)

[0076] Where i = 1, 2, 3, ..., n, D i Let D represent the fractal dimension of the i-th interval relative to the (n+1)-th total intervals. In other words, the fractal dimension corresponding to the 1st fractal interval should be D. 1 =D1×f1, the fractal dimension corresponding to the (n+1)th fractal interval should be D. n +1 =D n+1 ×f n+1 ;

[0077] Step 5, using the formula <7> Based on this, we obtain parameter D that can comprehensively characterize the heterogeneity of the reservoir's overall pore throat across n+1 fractal intervals. 总 :

[0078]

[0079] Example 2

[0080] The following specific application example illustrates a quantitative evaluation method for pore-throat heterogeneity in tight sandstone reservoirs according to this scheme. In this embodiment, the selected samples mainly come from different sandstone groups of the Shaximiao Formation in the central Sichuan Basin. Through rock thin section analysis, scanning electron microscopy, high-pressure mercury intrusion porosimetry, and porosity-permeability saturation analysis, it is concluded that the sandstone reservoirs in this region have strong heterogeneity. Therefore, the specific scheme of this invention is illustrated using the evaluation of heterogeneity in tight sandstone reservoirs of the Shaximiao Formation in the Sichuan Basin as an example:

[0081] The first step involves conducting mercury intrusion porosimetry (MIP) experiments on selected sandstone samples to directly obtain the basic data required for evaluating pore throat heterogeneity. This data primarily includes: porosity (%) and sample volume (cm³). 3 ), Mercury inlet pressure P c (MPa), mercury saturation S Hg (%), throat radius r (μm);

[0082] The second step involves preliminary processing of the raw mercury porosimetry experimental data, and plotting Lg(1-S) plots. Hg )~Lg P c Intersection diagram. (e.g.) Figures 1 to 4 As shown, based on the original mercury inlet pressure P c Mercury saturation S Hg The data were used to calculate Lg(1-S) values ​​for wells QL18, QL17, QL202, and QL205. Hg )~Lg P c Cross plot. The pore throat is divided into different intervals based on the number of inflection points on the cross plot. Each interval contains a different distribution of pore throats and a different degree of heterogeneity, corresponding to different pore throat fractal intervals. For example, the Lg(1-SHg)~Lg Pc cross plot of well QL18 has 2 inflection points, dividing the interval into 3 intervals, each corresponding to a different level of pore throat size;

[0083] Step 3: Transfer QL18 well Lg(1-S) from the previous step Hg )~Lg P c The trend lines of each fractal interval in the cross plot are fitted to obtain the fractal dimension corresponding to different intervals. Taking well QL18 as an example, the specific implementation steps include:

[0084] S31. Using the scatter points of the three fractal intervals of the three pore throats, obtain three trend lines, and fit the trend lines to obtain the relationship between Lg(1-S) and Lg(1-S). Hgn )~Lg P cnThe linear formulas, fitted with trend lines according to the increasing mercury saturation, are as follows:

[0085] Lg(1-S Hg1 )=-0.0013LgP c1 -0.0024;

[0086] Lg(1-S Hg2 )=-0.0723LgP c2 -0.0653;

[0087] Lg(1-S Hg3 )=-0.036LgP c3 -0.1469;

[0088] S32. Based on the fractal dimension principle and the basic principle of conventional mercury intrusion porosimetry, we have:

[0089] Lg(1-S Hgi )=(D i -3)LgP ci +b i ;

[0090] Where, D i Let i be the fractal dimension corresponding to the i-th interval, i = 0, 1, 2, 3, ..., n;

[0091] It can be seen that the absolute fractal dimensions of the three fractal intervals of the sandstone sample from well QL18 are:

[0092] From D1-3=-0.0013, we get D1=2.9987;

[0093] From D²-3 = -0.0723, we get D² = 2.9277;

[0094] From D3-3=-0.036, we get D3=2.9640;

[0095] Step 4: Based on the above steps, the heterogeneity of the three pore-throat intervals in the QL18 well sandstone sample is obtained. The overall heterogeneity of the QL18 well reservoir depends on the heterogeneity of the three different pore-throat intervals. Therefore, the distribution of pore throats in the QL18 well sample reservoir also needs to be considered to obtain the relative fractal dimension of each interval. Specific implementation steps include:

[0096] S41. As the mercury injection pressure increases, mercury is injected sequentially from large pore throats to small pore throats, and the amount of mercury injected gradually increases. As shown in Table 1, the mercury injection pressure in well QL18 increased from 0.0043 MPa to 200.05026 MPa, corresponding to a pore throat radius decreasing from 171.0159 μm to 0.0037 μm. The maximum mercury injection pressure corresponds to the minimum pore throat radius. Table 1 shows that the mercury injection pressure in well QL18 went through 23 stages from low to high. The cumulative mercury injection amount in each stage can be obtained by multiplying the mercury saturation of the corresponding stage by the pore volume, where the pore volume can be obtained by multiplying the porosity and sample volume in the original mercury intrusion porosimetry data. Therefore, the cumulative mercury injection amount corresponding to different pressures can be calculated.

[0097] Table 1. Mercury intrusion porosimetry data processing of sample No. 1 from well QL18

[0098]

[0099] The detailed mercury ingress amounts for the 23 mercury ingress stages in wells S42 and QL18 can be calculated using formulas. For example, in the 9th stage of mercury ingress in well QL18 (Table 1), the mercury ingress pore throat radius increased from 4.5015 μm to 3.0088 μm, and the cumulative mercury ingress amount increased from 0 ml to 0.02388 ml. The formula for calculating the detailed mercury ingress amount for this stage is: V Hg9～8 = (0.02388-0) / LOG10(4.5015 / 3.0088), and the calculation of mercury intake for other stages is similar;

[0100] S43, from the above third step Lg(1-S Hg )-LgP c The two inflection points of the curve divide the pore throat radius into three intervals. The cumulative mercury ingress within each of these three intervals is used to represent the distribution frequency of the three pore throat intervals. Therefore, the mercury ingress within the three pore throat intervals of well QL18 are 0.13646 ml, 2.77845 ml, and 1.21389 ml, respectively. After normalization, the frequencies f of the three intervals are... 1、 f 2、 The percentages of f3 were 0.03305 (3.3%), 0.67294 (67.3%), and 0.29401 (29.4%), respectively.

[0101] S44. The relative fractal dimension of each interval relative to the whole sample can be calculated from the above steps:

[0102] D 1 =D1×f1=2.9987×3.3%=0.0991;

[0103] D 2 =D2×f2=2.9277×67.3%=1.9702;

[0104] D 3 =D3×f3=2.9640×29.4%=0.8714;

[0105] Step 5: Obtain parameter D, which can comprehensively characterize the overall pore-throat heterogeneity of the reservoir in the three fractal intervals of well QL18. 总 :

[0106] D 总 =D 1 +D 2 +D 3 =2.9407.

[0107] Applying the above steps to sandstone samples from different wells can yield a series of D... 总 (Table 2):

[0108] Table 2. Calculation of fractal dimension of tight sandstone in Shaximiao Formation, Sichuan Basin.

[0109]

[0110] This further confirms the reliability of the new parameters used in this invention. Specifically, it includes:

[0111] The mercury intrusion porosimetry sorting coefficient, obtained through mercury intrusion porosimetry experiments, is a parameter reflecting the degree of concentration in pore distribution. A lower sorting coefficient indicates a more uniform pore distribution. Due to the heterogeneity of the pore throat, permeability typically varies; the more complex the pore throat structure, the lower the permeability. Table 2 shows the D values ​​for all samples used. 总 There is a good positive correlation between it and its sorting coefficient. Figure 5 There is a negative correlation between ), and penetration rate. Figure 6 Therefore, D 总 It can simultaneously characterize pore distribution and the degree of pore throat heterogeneity, which indirectly confirms the reliability of the new parameters proposed in this invention.

[0112] Example 3

[0113] This embodiment provides a device for quantitatively evaluating the heterogeneity of pore throats in tight sandstone reservoirs, including:

[0114] The parameter acquisition module is used to acquire the mercury intrusion test data required for evaluating the pore throat heterogeneity obtained from the core mercury intrusion test.

[0115] The data processing module is used to process mercury porosimetry experimental data and calculate Lg(1-S) Hg ) and LgP c And draw the intersection diagram of the two;

[0116] The fractal dimension acquisition module is used to extract the fractal intervals corresponding to different pore throats in the intersection diagram, fit the trend lines of each fractal interval, and obtain the fractal dimension corresponding to different fractal intervals.

[0117] The relative fractal dimension acquisition module is used to obtain the relative fractal dimension of each fractal interval relative to the entire reservoir.

[0118] The characterization parameter acquisition module is used to obtain parameters that can comprehensively characterize the overall pore throat heterogeneity of the reservoir based on the relative fractal dimension.

[0119] Furthermore, the fractal dimension acquisition module is also used to divide the interval into n+1 smaller intervals based on the n inflection points of the curve in the intersection diagram. Each of the divided intervals corresponds to the size of the pore throat at different levels.

[0120] Furthermore, the relative fractal dimension acquisition module also includes:

[0121] The mercury ingress measurement unit is used to measure the overall mercury ingress of the reservoir.

[0122] The pore throat radius division unit is used to divide the pore throat radius into multiple segments according to the same division method of the fractal interval, and the mercury ingress amount of each segment is used to represent the pore throat distribution frequency of each segment.

[0123] The pore throat distribution frequency acquisition unit is used to normalize the mercury ingress amount in each interval to obtain the pore throat distribution frequency of each interval, that is, the ratio of the number of pore throats in each fractal interval to the total number of pore throats in the reservoir.

[0124] The relative fractal dimension acquisition unit is used to multiply the pore throat distribution frequency with the fractal dimension of each fractal interval to obtain the relative fractal dimension of each fractal interval relative to the reservoir as a whole.

[0125] The functions performed by each functional module and unit in this embodiment correspond to the methods and steps in Embodiment 1.

[0126] Example 4

[0127] This embodiment provides a device for quantitatively evaluating the pore throat heterogeneity of tight sandstone reservoirs. This device can be used to implement the method for quantitatively evaluating the pore throat heterogeneity of tight sandstone reservoirs described in Embodiment 1 or 2 above. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method steps in Embodiment 1 above.

[0128] Preferably, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the device.

[0129] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can be any conventional processor. The processor is the control center of the device and connects the various parts of the device using various interfaces and lines.

[0130] The memory mainly includes a program storage area and a data storage area. The program storage area can store the operating system, applications required for at least one function, etc., while the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card, a secure digital card, and a flash memory card, or the memory can be other volatile solid-state storage devices.

[0131] Example 5

[0132] This embodiment provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements a quantitative evaluation method for the pore throat heterogeneity of tight sandstone reservoirs disclosed in Embodiment 1 or 2 above.

[0133] In this embodiment, the computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0134] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for quantitatively evaluating the heterogeneity of pore throats in tight sandstone reservoirs, characterized in that, Includes the following steps: Based on core mercury intrusion porosimetry (CIP) experiments, mercury intrusion experimental data required for evaluating pore throat heterogeneity were obtained, including mercury saturation S in tight sandstone reservoirs. Hg and its corresponding mercury inlet pressure P c ; Process the mercury porosimetry experimental data and calculate lg(1-S) Hg ) and lgP c And draw the intersection diagram of the two; Using the intersection diagram to extract the fractal intervals corresponding to different pore throats, including: dividing the interval into n+1 smaller intervals based on the n inflection points of the curve in the intersection diagram, with each interval corresponding to a different level of pore throat size; Fit the trend lines for each fractal interval, according to the formula lg(1-S Hgi ) = (D i -3)lgP ci +b i To obtain the fractal dimension corresponding to different fractal intervals, where D is... i Let b be the fractal dimension corresponding to the i-th interval. i S is the intercept of the i-th trend line. Hgi Let P be the mercury saturation corresponding to the i-th interval. ci The mercury inlet pressure corresponds to the i-th interval; Obtain the relative fractal dimension of each fractal interval relative to the entire reservoir, including: obtaining the mercury ingress rate of the entire reservoir; each inflection point on the curve in the cross plot corresponds to a mercury ingress pressure and pore throat radius. Divide the pore throat radius into multiple segments according to the fractal interval division method, and use the mercury ingress rate of each segment to represent the pore throat distribution frequency of each segment. The mercury ingress rate of each segment is calculated according to the following formula: V Hgx~x-1 = (V Hgx -V Hgx-1 ) / lg(R X-1 / R X ); where V Hgx R represents the cumulative mercury inflow corresponding to the radius of the Xth orifice throat. X The minimum pore throat radius corresponds to the maximum mercury ingress pressure. The mercury ingress amount in each interval is normalized to obtain the pore throat distribution frequency for each interval, which is the ratio of the number of pore throats in each fractal interval to the total number of pore throats in the reservoir. The pore throat distribution frequency is multiplied by the fractal dimension of each fractal interval to obtain the relative fractal dimension of each fractal interval relative to the entire reservoir. The relative fractal dimension of each fractal interval is superimposed to obtain a parameter that can comprehensively characterize the overall pore throat heterogeneity of the reservoir.

2. A device for quantitatively evaluating the heterogeneity of pore throats in tight sandstone reservoirs, characterized in that, include: The parameter acquisition module is used to acquire the mercury intrusion test data required for evaluating the pore throat heterogeneity obtained from the core mercury intrusion test. The data processing module is used to process mercury intrusion porosimetry (MIP) experimental data, including mercury saturation (S) in tight sandstone reservoirs. Hg and its corresponding mercury inlet pressure P c Calculate lg(1-S) Hg ) and lgP c And draw the intersection diagram of the two; The fractal dimension acquisition module is used to extract fractal intervals corresponding to different aperture throats in the intersection diagram, fit the trend lines of each fractal interval, and calculate the fractal dimension based on the formula lg(1-S). Hgi ) = (D i -3)lgP ci +b i To obtain the fractal dimension corresponding to different fractal intervals, where D is... i Let b be the fractal dimension corresponding to the i-th interval. i S is the intercept of the i-th trend line. Hgi Let P be the mercury saturation corresponding to the i-th interval. ci The mercury inlet pressure corresponds to the i-th interval; the fractal dimension acquisition module extracts fractal intervals corresponding to different pore throats in the intersection diagram, including: according to the n inflection points of the curve in the intersection diagram, the interval is divided into n+1 smaller intervals, and each interval after division corresponds to a different level of pore throat size; The relative fractal dimension acquisition module is used to obtain the relative fractal dimension of each fractal interval relative to the entire reservoir. The characterization parameter acquisition module is used to obtain parameters that can comprehensively characterize the overall pore throat heterogeneity of the reservoir based on the relative fractal dimension. The characterization parameter acquisition module is used to superimpose the relative fractal dimension of each fractal interval to obtain parameters that can comprehensively characterize the overall pore throat heterogeneity of the reservoir. The relative fractal dimension acquisition module includes a mercury ingress acquisition unit, a pore throat radius division unit, a pore throat distribution frequency acquisition unit, and a relative fractal dimension acquisition unit. The mercury ingress acquisition unit is used to acquire the overall mercury ingress of the reservoir. The pore throat radius division unit is used to divide the pore throat radius into multiple segments according to the same division method of the fractal interval, according to formula V. Hgx~x-1 = (V Hgx -V Hgx-1 ) / lg(R X-1 / R X Calculate the amount of mercury entering each segment and use this amount of mercury entering to represent the frequency distribution of the pore throat in each segment, where V Hgx R represents the cumulative mercury inflow corresponding to the radius of the Xth orifice throat. X The minimum pore throat radius corresponds to the maximum mercury ingress pressure. The pore throat distribution frequency acquisition unit is used to normalize the mercury ingress amount in each interval to obtain the pore throat distribution frequency corresponding to each interval, that is, the ratio of the number of pore throats in each fractal interval to the total number of pore throats in the reservoir. The relative fractal dimension acquisition unit is used to multiply the pore throat distribution frequency by the fractal dimension of each fractal interval to obtain the relative fractal dimension of each fractal interval relative to the entire reservoir.

3. A quantitative evaluation device for the pore-throat heterogeneity of tight sandstone reservoirs, characterized in that, It includes a processor and a memory for storing processor-executable instructions, which, when executed by the processor, implement the steps of the method as claimed in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 1.

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