Porosity prediction method based on multivariate two-dimensional pore throat parameter analysis

Abstract

The invention relates to a porosity prediction method based on multivariate two-dimensional pore throat parameter analysis, and belongs to the technical field of petroleum and natural gas exploration and development. The porosity prediction method comprises the following steps: acquiring the three-dimensional porosity and a plurality of different two-dimensional pore throat parameters of a sample; performing single-factor bilateral correlation test on each two-dimensional pore throat parameter and the three-dimensional porosity; selecting two-dimensional pore throat parameters having significant correlation with the three-dimensional porosity as significant correlation two-dimensional pore throat parameters, performing optimal function analysis on the three-dimensional porosity and each significant correlation two-dimensional pore throat parameter, and establishing a binary optimal function relationship between the three-dimensional porosity and each significant correlation two-dimensional pore throat parameter; and obtaining a three-dimensional porosity prediction model based on multivariate two-dimensional pore throat parameters through multivariate linear fitting according to all the obtained binary optimal function relationships. According to the porosity prediction method, the three-dimensional porosity is fitted by comprehensively considering the multivariate two-dimensional pore throat parameters, and the problem that the porosity is insufficient in traditional surface porosity fitting is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas exploration and development, and particularly relates to a porosity prediction method based on multi - dimensional two - dimensional pore throat parameter analysis. Background Technique

[0002] Stratigraphic rocks are porous. In the reservoirs of oil - and - gas - bearing basins, pores are an important oil and gas storage space, and porosity is one of the most important parameters in reservoir characterization and evaluation. Affected by the evolution of various sedimentary, tectonic, diagenetic and other processes in the geological history period, the reservoir shows heterogeneity, manifested in various types of reservoir pore throats, different shapes, uneven scales, complex structures and other aspects.

[0003] Two - dimensional surface porosity and three - dimensional porosity are common quantitative parameters for measuring the pore proportion in rocks. In some practical applications, such as when three - dimensional porosity data is missing or the pore evolution during the hydrocarbon accumulation period is unknown (i.e., three - dimensional porosity is unknown), usually a fitting model between two - dimensional surface porosity and three - dimensional porosity is first established, and the three - dimensional porosity is quantitatively calculated by obtaining the two - dimensional surface porosity of the rock. Since surface porosity and porosity are different manifestations of reservoir pores from two - dimensional and three - dimensional perspectives respectively, using two - dimensional surface porosity to fit three - dimensional porosity has a certain effectiveness and representativeness. However, reservoir physical properties are the macroscopic manifestation of the microscopic pore throat structure. Using only two - dimensional surface porosity to fit three - dimensional porosity still has deficiencies, and microscopic structural differences such as pore throat type, connectivity, sorting, pore throat radius, etc. will also inevitably affect the macroscopic three - dimensional porosity. For example, there are two types of pores in the rock, primary pores and secondary pores. When the surface porosity is the same, for the reservoir mainly composed of inter - granular primary pores, the pore connectivity is better than that of the reservoir mainly composed of intra - granular dissolved pores. The pore shape is relatively regular, while for the reservoir mainly composed of dissolved pores, due to the dissolution effect, the pore edges are mostly in irregular shapes such as cove - shaped and honeycomb - shaped.

[0004] Therefore, how to make up for the above deficiencies of traditional surface porosity fitting porosity is a technical problem that needs to be solved urgently at present. Summary of the Invention

[0005] In view of the above - mentioned technical problems, the present invention provides a porosity prediction method based on multi - dimensional two - dimensional pore throat parameter analysis. By comprehensively considering multi - dimensional two - dimensional pore throat parameters to fit three - dimensional porosity, the problem of insufficient traditional surface porosity fitting porosity is made up, and the fitting accuracy is improved.

[0006] The present invention provides a porosity prediction method based on multi - dimensional two - dimensional pore throat parameter analysis, including the following steps:

[0007] (1) Take sandstone samples from the same reservoir in the study area, and obtain the three - dimensional porosity of each sample and multiple different two - dimensional pore throat parameters;

[0008] (2) Perform a single-factor two-sided correlation test on each two-dimensional pore-throat parameter and the three-dimensional porosity to test the correlation between each two-dimensional pore-throat parameter and the three-dimensional porosity;

[0009] (3) Select the two-dimensional pore-throat parameters that are significantly correlated with the three-dimensional porosity as the significantly correlated two-dimensional pore-throat parameters, perform an optimal function analysis on the three-dimensional porosity and each significantly correlated two-dimensional pore-throat parameter, and establish a binary optimal function relationship between the three-dimensional porosity and each significantly correlated two-dimensional pore-throat parameter;

[0010] (4) According to all the binary optimal function relationships obtained in step (3), through multiple linear fitting, obtain a function relationship between the three-dimensional porosity and the multiple two-dimensional pore-throat parameters with all the significantly correlated two-dimensional pore-throat parameters as the independent variables and the three-dimensional porosity as the dependent variable, as the three-dimensional porosity prediction model based on the multiple two-dimensional pore-throat parameters.

[0011] In some embodiments, in step (1), the two-dimensional pore-throat parameters include the pore face ratio average coordination number R pt , average pore-throat ratio C N , pore shape factor K, average pore radius R p , sorting coefficient S of pore-throat radius p .

[0012] In some embodiments, in step (1), the three-dimensional porosity Φ of the sample is obtained by core gas measurement, and the pore face ratio of the sample is obtained by analyzing the sample cast thin-section image average coordination number R pt , average pore-throat ratio C N , pore shape factor K, average pore radius R p , sorting coefficient S of pore-throat radius p .

[0013] In some embodiments, in step (3), the two-dimensional pore-throat parameters with a correlation coefficient greater than 0.7 are selected as the significantly correlated two-dimensional pore-throat parameters.

[0014] In some embodiments, in step (4), when the binary optimal function relationship between a single two-dimensional pore-throat parameter and the three-dimensional porosity is a non-linear function relationship, then the overall fitting terms other than the constant term in the binary optimal function relationship are used as the independent variables for multiple linear fitting.

[0015] In some embodiments, the two-sided correlation test, the optimal function analysis, and the multiple linear fitting are all performed using the mathematical analysis software SPSS.

[0016] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0017] The porosity prediction method based on multi - variable two - dimensional pore - throat parameter analysis provided by the present invention obtains the multi - variable two - dimensional pore - throat parameters of the reservoir, passes the correlation test and optimal function analysis, fits the comprehensive functional relationship between the two - dimensional pore - throat parameters with significant correlation and the three - dimensional porosity, and establishes a quantitative functional model between the multi - variable two - dimensional pore - throat parameters and the three - dimensional porosity, making up for the deficiencies of the traditional surface porosity - porosity fitting model and improving the accuracy of porosity prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the porosity prediction method based on multi - variable two - dimensional pore - throat parameter analysis provided by the embodiment of the present invention;

[0019] Figure 2 It is the fitting result of the binary optimal function relationship between the three - dimensional porosity and the significantly correlated two - dimensional pore - throat parameters provided by Embodiment 1 of the present invention. Among them, (a) is the fitting result of the three - dimensional porosity Φ and the surface porosity The fitting result, (b) is the fitting result of the three - dimensional porosity Φ and the average coordination number R pt The fitting result, (c) is the fitting result of the three - dimensional porosity Φ and the average pore - throat ratio C N The fitting result, (d) is the fitting result of the three - dimensional porosity Φ and the pore shape factor K;

[0020] Figure 3 It is the fitting effect diagram of the three - dimensional porosity prediction model based on multi - variable two - dimensional pore - throat parameters provided by Embodiment 1 of the present invention;

[0021] Figure 4 It is the error comparison diagram of the fitting results between the three - dimensional porosity prediction model based on multi - variable two - dimensional pore - throat parameters and the traditional surface porosity - three - dimensional porosity fitting model provided by Embodiment 1 of the present invention. Among them, (a) is the error analysis diagram of the fitting result of the three - dimensional porosity prediction model based on multi - variable two - dimensional pore - throat parameters, and (b) corresponds to the error analysis diagram of the fitting result of the traditional surface porosity - three - dimensional porosity fitting model. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] As Figure 1 shown, the embodiment of the present invention provides a porosity prediction method based on multi - variable two - dimensional pore - throat parameter analysis, including the following steps:

[0024] (1) Take sandstone samples from the same reservoir in the study area, and obtain the three - dimensional porosity and multiple different two - dimensional pore - throat parameters of each sample;

[0025] (2) Perform a single-factor two-sided correlation test on each two-dimensional pore-throat parameter and the three-dimensional porosity to test the correlation between each two-dimensional pore-throat parameter and the three-dimensional porosity;

[0026] (3) Select the two-dimensional pore-throat parameters that are significantly correlated with the three-dimensional porosity as the significantly correlated two-dimensional pore-throat parameters, perform an optimal function analysis on the three-dimensional porosity and each significantly correlated two-dimensional pore-throat parameter, and establish a binary optimal function relationship between the three-dimensional porosity and each significantly correlated two-dimensional pore-throat parameter;

[0027] (4) According to all the binary optimal function relationships obtained in step (3), through multiple linear fitting, obtain the function relationship between the three-dimensional porosity and the multiple two-dimensional pore-throat parameters with all the significantly correlated two-dimensional pore-throat parameters as independent variables and the three-dimensional porosity as the dependent variable, as the three-dimensional porosity prediction model based on multiple two-dimensional pore-throat parameters.

[0028] The above porosity prediction method based on the analysis of multiple two-dimensional pore-throat parameters, by obtaining the multiple two-dimensional pore-throat parameters of the reservoir, passing the correlation test and the optimal function analysis, fitting the comprehensive function relationship between the two-dimensional pore-throat parameters with significant correlation and the three-dimensional porosity, and establishing a quantitative function model between the multiple two-dimensional pore-throat parameters and the three-dimensional porosity, makes up for the deficiencies of the traditional surface porosity-porosity fitting model and improves the accuracy of porosity prediction.

[0029] In order to introduce the porosity prediction method based on the analysis of multiple two-dimensional pore-throat parameters provided by the embodiments of the present invention more clearly and in detail, the following will be described in combination with specific embodiments.

[0030] Example 1

[0031] A porosity prediction method based on the analysis of multiple two-dimensional pore-throat parameters, comprising the following steps:

[0032] (1) Obtain the three-dimensional porosity of the sample and multiple different two-dimensional pore-throat parameters

[0033] For the same set of beach-bar sandstone reservoirs in the study area, 29 different sandstone rock samples are selected, and the three-dimensional porosity Φ of all samples is obtained by core gas logging.

[0034] Rock cast thin sections of each sample are made through processes such as cutting, grinding, oil washing, casting impregnation, and polishing. The rock cast thin sections of each sample are observed under an optical microscope. Multiple fields of view are selected for photography under the optical microscope, and computer image analysis software is used to perform image analysis and processing on each field of view image to obtain the surface porosity of each sample Average coordination number R pt 、Average pore-throat ratio C N 、Pore shape factor K, average pore radius Rp , sorting coefficient S of pore throat radius p .

[0035] (2) Single-factor two-sided correlation test

[0036] Using the mathematical analysis software SPSS, a single-factor two-sided correlation test is conducted for each two-dimensional pore throat parameter (specifically including the pore area ratio , average coordination number R pt , average pore throat ratio C N , pore shape factor K, average pore radius R p , sorting coefficient S of pore throat radius p ) and three-dimensional porosity Φ to test the correlation between each two-dimensional pore throat parameter and three-dimensional porosity Φ. The results are shown in Tables 1 - 6.

[0037] Table 1 Results of single-factor two-sided correlation test for pore area ratio

[0038]

[0039] **: Significantly correlated at the.01 level (two-sided).

[0040] Table 2 Results of single-factor two-sided correlation test for average coordination number R pt

[0041]

[0042] **: Significantly correlated at the.01 level (two-sided).

[0043] Table 3 Results of single-factor two-sided correlation test for average pore throat ratio C N

[0044]

[0045] **: Significantly correlated at the.01 level (two-sided).

[0046] Table 4 Results of single-factor two-sided correlation test for pore shape factor K

[0047]

[0048] **: Significantly correlated at the.01 level (two-sided).

[0049] Table 5 Results of single-factor two-sided correlation test for average pore radius R p

[0050]

[0051] **: The correlation is not significant. ​​​​

[0052] Table 6 Sorting coefficient S of pore throat radius p Results of single-factor two-sided correlation test

[0053]

[0054] **: The correlation is not significant.

[0055] (3) Establish the binary optimal functional relationship between three-dimensional porosity and significantly correlated two-dimensional pore throat parameters

[0056] As can be seen from Tables 1 - 6, the areal porosity average coordination number R pt and average pore throat ratio C N and pore shape factor K have a correlation coefficient greater than 0.7 with three-dimensional porosity Φ, and the correlation is significant. Taking the areal porosity average coordination number R pt and average pore throat ratio C N and pore shape factor K as significantly correlated two-dimensional pore throat parameters, the mathematical analysis software SPSS is used to perform optimal function analysis on three-dimensional porosity Φ and each significantly correlated two-dimensional pore throat parameter, and the binary optimal functional relationship between three-dimensional porosity Φ and each significantly correlated two-dimensional pore throat parameter is established. The fitting results of the binary optimal functional relationship are shown in Table 7 and Figure 2 as shown.

[0057] Table 7 Binary optimal functional relationship between three-dimensional porosity and significantly correlated two-dimensional pore throat parameters

[0058]

[0059] (4) Establish a prediction model of three-dimensional porosity based on multiple two-dimensional pore throat parameters

[0060] According to all the binary optimal functional relationships obtained in step (3), assume that the multiple linear regression equation of three-dimensional porosity Φ and multiple two-dimensional pore throat parameters is:

[0061]

[0062] where a, b, c, d are regression coefficients, and C is a constant.

[0063] Perform multiple linear fitting using the mathematical analysis software SPSS. The fitting regression analysis table is shown in Table 8, and the functional relationship between the fitted three-dimensional porosity Φ and multiple two-dimensional pore throat parameters is as follows:

[0064]

[0065] Table 8 Fitting regression analysis table of three-dimensional porosity and multiple two-dimensional pore throat parameters

[0066]

[0067] (5) Verification of fitting effect

[0068] Taking as the three-dimensional porosity prediction model based on multi-dimensional two-dimensional pore throat parameters, the predicted values of the three-dimensional porosity of 29 samples are calculated. By comparing with the measured actual three-dimensional porosity of 29 samples, a fitting effect diagram is drawn (see Figure 3 ). As can be seen from Figure 3 , compared with the traditional surface area ratio-three-dimensional porosity fitting model (R 2 = 0.7179), the fitting degree R 2 of the three-dimensional porosity prediction model based on multi-dimensional two-dimensional pore throat parameters obtained in Example 1 is 0.803, indicating a better fitting effect.

[0069] An error analysis is carried out on the fitting results of the three-dimensional porosity prediction model based on multi-dimensional two-dimensional pore throat parameters obtained in Example 1 and the traditional surface area ratio-three-dimensional porosity fitting model. The analysis results are as Figure 4 shown. As can be seen from Figure 4 , the relative error between the fitting value calculated by using the three-dimensional porosity prediction model based on multi-dimensional two-dimensional pore throat parameters obtained in Example 1 and the actual porosity is distributed between 0.83% - 16.53%, mainly distributed between 0 - 5%, with an average of 7.74%; while the relative error between the fitting value calculated by using the traditional surface area ratio-three-dimensional porosity fitting model and the actual porosity is distributed between 2.77% - 44.82%, with an average of 13.74%. This shows that the fitting effect of the three-dimensional porosity Φ prediction model based on multi-dimensional two-dimensional pore throat parameters obtained in Example 1 is better and the accuracy is higher.

Claims

1. A porosity prediction method based on the analysis of multivariate two-dimensional pore throat parameters, characterized in that It includes the following steps: (1) Take sandstone samples from the same reservoir in the study area, and obtain the three-dimensional porosity and multiple different two-dimensional pore throat parameters of each sample; (2) Conduct a single-factor two-sided correlation test on each two-dimensional pore throat parameter and the three-dimensional porosity to test the correlation between each two-dimensional pore throat parameter and the three-dimensional porosity; (3) Select the two-dimensional pore throat parameters that are significantly correlated with the three-dimensional porosity as the significantly correlated two-dimensional pore throat parameters, conduct an optimal function analysis on the three-dimensional porosity and each significantly correlated two-dimensional pore throat parameter, and establish a binary optimal function relationship between the three-dimensional porosity and each significantly correlated two-dimensional pore throat parameter; (4) According to all the binary optimal function relationships obtained in step (3), through multiple linear fitting, obtain the function relationship between the three-dimensional porosity and the multiple two-dimensional pore throat parameters with all the significantly correlated two-dimensional pore throat parameters as the independent variables and the three-dimensional porosity as the dependent variable, as the three-dimensional porosity prediction model based on the multiple two-dimensional pore throat parameters.

2. The porosity prediction method based on multi-dimensional two-dimensional pore throat parameter analysis according to claim 1, wherein In step (1), the two-dimensional pore-throat parameters include the pore area ratio average coordination number R pt , average pore-throat ratio C N , pore shape factor K, average pore radius R p , sorting coefficient S of pore-throat radius p .

3. The porosity prediction method based on multi - dimensional two - dimensional pore - throat parameter analysis according to claim 2, wherein In step (1), the three-dimensional porosity Φ of the sample is obtained by core gas logging method, and the pore surface ratio of the sample is obtained by analyzing the cast thin section image of the sample. Average coordination number R pt , average pore throat ratio C N , pore shape factor K, average pore radius R p , sorting coefficient S of pore throat radius p .

4. The porosity prediction method based on multi - dimensional two - dimensional pore - throat parameter analysis according to claim 1, characterized in that, In step (3), select the two-dimensional pore throat parameters with a correlation coefficient greater than 0.7 as the significantly correlated two-dimensional pore throat parameters.

5. The porosity prediction method based on multi - dimensional two - dimensional pore - throat parameter analysis according to claim 1, wherein, In step (4), when the binary optimal function relationship between a single two-dimensional pore throat parameter and the three-dimensional porosity is a non-linear function relationship, then use the overall fitting terms other than the constant term in this binary optimal function relationship as the independent variable for multiple linear fitting.

6. The porosity prediction method based on multi-dimensional two-dimensional pore throat parameter analysis according to claim 1, wherein The two-sided correlation test, the optimal function analysis, and the multiple linear fitting are all carried out using the mathematical analysis software SPSS.

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