Method for evaluating porosity of clastic rock reservoirs and application

Abstract

The application provides a clastic rock reservoir porosity evaluation method and application. First, well logging data is standardized, and the standardized well logging data is used for core homing treatment on core porosity obtained through experimental testing, so that corrected core porosity is obtained. A plurality of models for predicting porosity through three-pore well logging curves are established by using the above data, and accurate prediction of porosity is realized. The model is based on geophysical and mathematical thinking, and combines the influence of reservoir variable skeleton, so that the accuracy of porosity well logging interpretation is greatly improved, the cost of well logging interpretation is reduced, and application in the fields of oil and gas exploration and development and geophysical well logging is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geophysical well logging, and particularly relates to a method for evaluating porosity of clastic rock reservoirs and application. BACKGROUND

[0002] Porosity is one of the important parameter indicators for measuring the quality of reservoirs, and the correctness of the analysis and calculation value of porosity directly affects the accuracy of the calculation value of permeability and oil saturation, so it is particularly important to carry out porosity evaluation.

[0003] How to obtain the porosity parameter value of clastic rock reservoirs? The most direct and reliable way is to measure the porosity of the reservoir by drilling core experiment test method, but it is not possible to core every well due to the high cost of drilling coring, and it is also not possible to ensure that the core of the oil layer is continuously measured for porosity, so the evaluation of the reservoir system is limited. The reservoir modeling method using seismic data can also be used for reservoir porosity prediction, but the seismic data has the characteristics of low vertical resolution, and the reservoir modeling result is greatly affected by the richness of well data, thereby affecting the porosity prediction accuracy.

[0004] Logging data has the advantages of large amount of information, convenient processing, continuous data and systematic distribution, and plays an important role in porosity evaluation. In the early stage, linear equations were established according to single logging curves or empirical formula method was used for porosity prediction, but acoustic time difference, compensated density, compensated neutron, natural gamma ray and other curves will respond to porosity, and only considering a single curve will lead to large error in reservoir porosity prediction; In recent years, nuclear magnetic resonance or ECS logging is mainly used to calculate porosity, although the prediction accuracy is high, but these special logging series not only have high cost, but also have small amount, especially in some old oilfield areas, the related series data are lacking, and it is impossible to predict. Therefore, how to fully utilize the conventional logging means to effectively carry out the porosity evaluation of clastic rock reservoirs and improve the porosity prediction accuracy of the reservoirs to meet the needs of exploration and development is one of the major challenges currently faced. SUMMARY

[0005] In order to solve the above problems in the prior art, the present application provides a method for evaluating the porosity of clastic rock reservoirs, which calculates the porosity of clastic rock reservoirs through the relationship between acoustic time difference, compensated density and compensated neutron three-pore logging curves, and improves the porosity prediction accuracy.

[0006] In a first aspect, the present application provides a method for evaluating the porosity of clastic rock reservoirs, comprising the following steps:

[0007] S1: standardizing acoustic time difference, compensated density and compensated neutron three kinds of logging data, and adopting the standardized logging data to carry out core homing processing on the core porosity obtained by experiment test, to obtain corrected core porosity;

[0008] S2: Calculate porosity Φ1 using the normalized acoustic traveltime data, with the formula:

[0009]

[0010] where Φ1 is the porosity calculated from acoustic traveltime, %;

[0011] AC Ma is the acoustic traveltime of the rock matrix, μs / m;

[0012] AC f is the acoustic traveltime of the formation fluid, μs / m;

[0013] Cp is the acoustic compaction correction coefficient;

[0014] V sh is the shale content of the formation, %;

[0015] AC sh is the acoustic traveltime of the shale, μs / m;

[0016] AC is the acoustic traveltime log value of the target layer, μs / m;

[0017] S3: Calculate porosity Φ2 using the normalized compensated density data, with the formula:

[0018]

[0019] where Φ2 is the porosity calculated from compensated density, %;

[0020] DEN Ma are the density values of the rock matrix, g / cm 3 , respectively;

[0021] DEN f is the density value of the formation fluid, g / cm 3 ;

[0022] V sh is the shale content of the formation, %;

[0023] DEN sh is the density value of the shale, g / cm 3 ;

[0024] DEN is the compensated density log value of the target layer, g / cm 3 ;

[0025] S4: Calculate porosity Φ3 using the normalized compensated neutron data, with the formula:

[0026] Φ3 = (CNL - CNLMa -0.5*V sh *CNL sh )*0.01

[0027] Φ3 - compensated neutron calculated porosity, %;

[0028] CNL Ma - rock matrix neutron value, %;

[0029] V sh - formation shale content, %;

[0030] CNL sh - shale neutron value, %;

[0031] CNL - target layer compensated neutron log value, %;

[0032] S5: multiple regression analysis is performed on Φ1, Φ2, and Φ3 calculated in steps S2-S4 and the core porosity, respectively, to obtain correlation coefficients R1 2 , R2 2 , and R3 2 , and the proportions of R1 2 , R2 2 , and R3 2 in the sum of R1 2 , R2 2 , and R3 2 are calculated, the corresponding proportions are taken as the regression equation coefficients, and multiple regression analysis is performed again on the corrected core porosity to establish a porosity prediction model POR1;

[0033] S6: multiple regression analysis is performed on Φ1, Φ2, and Φ3 calculated in steps S2-S4 together with the corrected core porosity to obtain a porosity prediction model POR2;

[0034] S7: multiple regression analysis is performed directly on the normalized acoustic travel time, compensated density, and compensated neutron three-porosity logging curves and the corrected core porosity to obtain a porosity prediction model POR3;

[0035] S8: comparative analysis is performed on the porosity prediction models POR1, POR2, and POR3 and the corrected core porosity, the best porosity prediction model POR with the best correlation is found as the final porosity model, and a clastic rock reservoir porosity prediction curve is obtained through the final porosity prediction model POR to evaluate the physical properties of the clastic rock reservoir.

[0036] As a specific embodiment of the present application, in the step S1, the standardization processing in the step S1 is performed by a histogram translation method, preferably comprising: providing the marker layer logging data in the study area, establishing a histogram, and establishing a histogram comparison and correction with all well site data based on the standard histogram, wherein the difference from the standard data is the correction amount.

[0037] As a specific embodiment of the present application, in the step S1, the core homing processing in the step S1 comprises: representing the experimentally measured core analysis porosity in the form of a histogram on a logging chart, and performing a comparison and analysis with the logging data, and correcting the depth corresponding to the experimentally measured core porosity data based on the depth corresponding to the logging data.

[0038] As a specific embodiment of the present application, in the step S1, the core homing processing further comprises extracting effective reservoir core porosity; the effective reservoir core porosity refers to the core porosity that has matching logging data at the corresponding depth after core homing.

[0039] It should be noted that in actual production operations, some data with large errors caused by human factors or non-human factors are invalid data, and the invalid data should be excluded to avoid affecting the calculation results.

[0040] The porosities Φ1, Φ2, and Φ3 are calculated, and the core porosity is experimentally measured by the core analysis method in GB / T 29172-2012; in actual operations, the experimentally measured core porosity value may have errors, and can be further corrected by using the core data after core homing.

[0041] As a specific embodiment of the present application, in the step S5, the Φ1, Φ2, and Φ3 calculated in the steps S2-S4 are subjected to multiple regression analysis with the core porosity, to obtain correlation coefficients R1 2 , R2 2 , and R3 2 , and R1 2 , R2 2 , and R3 2 occupy R1 2 , R2 2 , and R3 2The proportion of the sum of the weights was used as the coefficients of the regression equation, and a multiple regression analysis was performed again with the corrected core porosity to establish the porosity prediction model POR1. When using well logging data to predict porosity, the oil and gas in the pores will adversely affect the neutron, density, and sonic logging responses. This is mainly because oil and gas are lighter than water, so density logging results will indicate too high porosity (assuming the pore space is filled with water). Because there are fewer hydrogen atoms in light hydrocarbons than in water, neutron logging results will indicate very low porosity. Similarly, because sound waves propagate slower in light hydrocarbons than in water, sonic logging will also indicate particularly high porosity. Therefore, considering the combined effects of sonic transit time, compensated density, and compensated neutron on core porosity, the porosity prediction model POR1 was established.

[0042] In a specific embodiment of the present invention, in steps S5, S6, and S7, the multiple regression analysis is independently performed by using linear function regression analysis to analyze the correlation between multiple independent variables and the dependent variable. This method can eliminate the error caused by predicting reservoir physical parameters using a single curve. The independent variables in steps S5 and S6 include Φ1, Φ2, and Φ3, and the independent variables in step S7 are the standardized acoustic transit time, compensated density, and compensated neutrons. The dependent variable is the corrected core porosity. In a specific embodiment of the present invention, multiple regression, i.e., using linear function regression analysis to analyze the correlation between multiple independent variables and the dependent variable, can eliminate the error caused by predicting reservoir physical parameters using a single curve.

[0043] Specifically, for a dependent variable y, there is a set of independent variables X = (x1, x2, ..., x...). k x1, x2, ..., x k Let ε be a column vector, and consider the following linear relationship between them:

[0044] y = β0 + β1x1 + ... + β k x k +ε

[0045] The least squares method is typically used to estimate the partial regression coefficients (β0, β1, β2, ..., β). k The basic principle is to find a suitable set of partial regression coefficients and establish a multiple linear regression equation that minimizes the sum of squared residuals between the observed values ​​of the response variable and the estimated values ​​of the regression equation, i.e., satisfies:

[0046]

[0047] In a specific embodiment of the present invention, in steps S5, S6, and S7, the multiple regression analysis independently yields the correlation coefficient R. 2 .

[0048] It should be noted that the correlation coefficient R 2 In the multiple regression analysis from the formula:

[0049] R 2 = SSR / SST = 1-SSE / SST

[0050] SST = SSR + SSE

[0051] Wherein, SST (total sum of squares) is the total deviation square sum, SSR (regression sum of squares) is the regression square sum, SSE (error sum of squares) is the residual square sum.

[0052] The advantage of multiple regression analysis is that the greater the goodness of fit, the higher the degree of explanation of the dependent variable by the independent variable, the higher the percentage of the total variation caused by the independent variable, and the more concentrated the observation points near the regression line, so that R 2 The value range: 0-1; and R 2 The closer to 1, the higher the goodness of fit.

[0053] As a specific embodiment of the present application, in the step S8, the correlation is preferably compared with the calculated porosity and the core porosity, and the correlation coefficient R 2 The closer to 1, the better the correlation.

[0054] In a second aspect, the present application provides an evaluation method for the porosity of the clastic rock reservoir in the field of oil and gas exploration and development, geophysical logging technology.

[0055] As a specific embodiment of the present application, the reservoir porosity represents the capacity (volume) of the reservoir to contain oil and gas, and is one of the important parameters for oil layer identification, oil saturation calculation and fine evaluation of oil reservoir, and is the basis for accurate description of the reservoir, so accurate prediction of porosity is of great significance for oil and gas exploration and development and geophysical logging technology.

[0056] Compared with the prior art, the present application has the beneficial effects that:

[0057] 1. The evaluation method for the porosity of the clastic rock reservoir of the present application uses conventional logging data which is rich and easy to obtain, and comprehensive development of the prediction is beneficial to more accurate prediction of the whole area;

[0058] 2. The evaluation method for the porosity of the clastic rock reservoir of the present application considers the sensitivity of different logging curves to porosity, and optimizes the prediction method for the porosity of the clastic rock reservoir by optimizing different prediction models;

[0059] 3、The method for evaluating the porosity of the clastic rock reservoir in the application has a fast model establishment, is suitable for various stages from exploration to development, and is convenient for geologists to quickly evaluate. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A flow chart of the method for evaluating the porosity of the clastic rock reservoir in the embodiment of the application;

[0061] Figure 2 A sonic traveltime frequency data graph before the well site data in the embodiment 1 is corrected and processed;

[0062] Figure 3 A sonic traveltime frequency data graph after the well site data in the embodiment 1 is corrected and processed;

[0063] Figure 4 A core analysis porosity rod graph in the embodiment 1 of the application;

[0064] Figure 5 A POR1 model and core porosity relationship graph in the embodiment 1 of the application;

[0065] Figure 6 A POR2 model and core porosity relationship graph in the embodiment 1 of the application;

[0066] Figure 7 A POR3 model and core porosity relationship graph in the embodiment 1 of the application;

[0067] Figure 8 A POR1 model and core porosity relationship graph in the embodiment 2 of the application;

[0068] Figure 9 A POR2 model and core porosity relationship graph in the embodiment 2 of the application;

[0069] Figure 10 A POR3 model and core porosity relationship graph in the embodiment 2 of the application. DETAILED DESCRIPTION

[0070] The application will be further described below in conjunction with specific embodiments, but does not constitute any limitation on the application.

[0071] In the embodiments of the application, the processing software used is CIFLog and Excel.

[0072] Embodiment 1

[0073] The embodiment provides a method for evaluating the porosity of a clastic rock reservoir, which is applied to a Chang 7 member tight sandstone reservoir in the southwest of the Ordos Basin, and specific details are as follows:

[0074] The reservoir logging statistics show that the tight sandstone reservoir of Chang 7 member in the southwest of Ordos basin is mainly divided into two types of siltstone and fine sandstone, the reservoir porosity is mainly 4%~12%, the permeability is mainly 0.01mD~0.3mD, and the reservoir belongs to low porosity and low permeability tight reservoir. In this embodiment, 6 wells in the study area are selected, and 246 core analysis test point data are used to calculate the porosity.

[0075] S1: standardizing the acoustic travel time, compensated density and compensated neutron logging data, using the standardized logging data to process the core porosity obtained by the experiment test, and obtaining the corrected core porosity;

[0076] The histogram translation method is used for correction. The acoustic travel time, compensated neutron and compensated density logging curves are standardized. First, the stable marker layer logging data developed in the study area is found, and the histogram is established based on the data. Then, the histogram is compared with all well data, and the difference from the standard mode is used as the correction amount. As shown in Figure 1 , Figure 2 , the well data is corrected.

[0077] The core analysis porosity obtained by the experiment test is shown in the logging chart in the form of a histogram, as shown in Figure 3 , and then compared with the logging data. The corresponding depth of the logging data is used as the standard to correct the corresponding depth of the core porosity data obtained by the experiment test, that is, the core is located. The core porosity data after the core location processing is regarded as the effective clastic rock reservoir core porosity data.

[0078] S2: calculating the porosity Φ1 using the standardized acoustic travel time data.

[0079] S3: calculating the porosity Φ2 using the standardized compensated density data.

[0080] S4: calculating the porosity Φ3 using the standardized compensated neutron data.

[0081] It should be noted that each depth point has a corresponding value, and the value of each depth point is different. The data of steps S2-S4 will not be described here.

[0082] S5: performing multiple regression analysis on Φ1, Φ2 and Φ3 calculated in steps S2-S4 and the core porosity, respectively, to obtain correlation coefficients R1 2 , R2 2 and R3 2 , and R1 2 , R2 2 and R3 2 account for R1 2R2 2 R3 2 The proportion of the sum of the values ​​is used as the coefficients of the regression equation, and then a multiple regression analysis is performed with the corrected core porosity to establish the porosity prediction model POR1. The results of the multiple regression analysis are as follows: Figure 5 As shown, the specific formula is:

[0083] POR1=0.7683*(0.611405056*Φ1+0.32627866*Φ2+0.062316*Φ3)+0.0272

[0084] The calculation yielded the following results:

[0085] Φ1:R1 2 =0.208

[0086] Φ2:R2 2 =0.111

[0087] Φ3:R3 2 =0.0212

[0088] Further multivariate regression analysis yielded the correlation coefficient R of model POR1. 1’ 2 =0.6186

[0089] S6: Combine Φ1, Φ2, and Φ3 calculated in steps S2-S4 with the corrected core porosity and perform a multiple regression analysis to obtain the porosity prediction model POR2. The results of the multiple regression analysis are as follows: Figure 6 As shown, the specific formula is:

[0090] POR2=0.043+0.4916*Φ1+0.4902*Φ2-0.0839*Φ3

[0091] Multiple regression analysis yielded the correlation coefficient R of model POR2. 2’ 2 =0.1812

[0092] S7: Directly perform multivariate regression analysis on the standardized sonic transit time, compensated density, compensated neutron three-porosity logging curves and the corrected core porosity to obtain the porosity prediction model POR3. The multivariate regression analysis results are as follows: Figure 7 As shown, the specific formula is:

[0093] POR3=0.1765+0.001*AC-0.1397*DEN+0.001*CNL

[0094] Multiple regression analysis yielded the correlation coefficient R of model POR3. 3’ 2 =0.5843

[0095] S8: The correlation between the porosity prediction values of different models and the core porosity is compared, and the best model is found to be POR1, which has the highest prediction accuracy.

[0096] S9: The porosity prediction curve of the clastic reservoir is obtained by the porosity prediction model POR1, so as to evaluate the physical property of the clastic reservoir.

[0097] The evaluation result obtained in Example 1 is that the correlation coefficient R square of the porosity prediction model POR1 and the core porosity is 0.6186, as shown in Table 1, which meets the needs of exploration and development.

[0098] Table 1 Correlation coefficient R 2 Correlation with the relationship

[0099] Correlation coefficient R 2 ]] Correlation 0.8-1.0 Very strong correlation 0.6-0.8 Strong correlation 0.4-0.6 Moderate correlation 0.2-0.4 Weak correlation 0.0-0.2 Very weak or no correlation

[0100] Example 2

[0101] The embodiment provides an evaluation method for the porosity of a clastic reservoir, which is applied to a tight sandstone reservoir of the Xujiahe Formation in the northeast of the Sichuan Basin, and specific details are as follows:

[0102] Logging statistics show that the lithology of the tight sandstone reservoir of the Xujiahe Formation in the northeast of the Sichuan Basin is mainly medium sandstone and fine sandstone, and the reservoir porosity is mainly 1.2% to 6%. In this embodiment, data of a total of 85 core analysis test points in the study area are selected for porosity calculation.

[0103] S1: The acoustic travel time, compensated density and compensated neutron logging data are standardized, the core porosity obtained by experimental testing is processed by core homing using the standardized logging data, and the corrected core porosity is obtained; the data processing method is the same as that in Example 1, which is omitted here.

[0104] S2: The porosity Φ1 is calculated using the standardized acoustic travel time data.

[0105] S3: The porosity Φ2 is calculated using the standardized compensated density data.

[0106] S4: The porosity Φ3 is calculated using the standardized compensated neutron data.

[0107] It should be noted that each depth point has a corresponding value, and the values of each depth point are different, and the data of steps S2-S4 are not described here.

[0108] S5: The Φ1, Φ2 and Φ3 calculated in steps S2-S4 are subjected to multiple regression analysis with the core porosity, respectively, to obtain the correlation coefficients R4 2 , R52 , R6 2 , R5 2 , R6 2 , R6 2 , R6 2 , R5 2 , R6 2 , R6 2 = 0.21 2 = 0.2216 2 = 0.1193 4’ 2 = 0.6141 5’ 2 = 0.2302 Figure 8

[0109] POR1 = 0.9771 * (0.381194 * Φ1 + 0.402251 * Φ2 + 0.216555 * Φ3) + 0.0187

[0110]

[0111] Φ1: R4

[0112] Φ2: R5

[0113] Φ3: R6

[0114]

[0115] POR2 = 3.3202 + 0.02846 * Φ1 + 0.07036 * Φ2 - 0.08683 * Φ3 Figure 6

[0116]

[0117]

[0118] Figure 7 POR3 = 0.0002 + 0.0001 * Δt + 0.0001 * G * RhoN

[0119] ​​​​​​​POR3=13.76714+0.00519*AC-0.14389*DEN-3.9549*CNL

[0120] Multiple regression analysis yielded the correlation coefficient R of model POR3. 6’ 2 =0.1228

[0121] Evaluation results obtained in Example 2: The square of the correlation coefficient R between the porosity prediction model POR1 and the core porosity is 0.6141, as shown in Table 1, which meets the requirements of exploration and development.

[0122] In summary, the method for evaluating the porosity of clastic rock reservoirs presented in this invention, as demonstrated by the examples, improves the prediction accuracy of porosity in clastic rock reservoirs, meeting the needs of exploration and development. Furthermore, it can predict reservoir porosity based on conventional logging curves, reducing the cost of logging interpretation. This model, based on geophysical and mathematical thinking and incorporating the influence of reservoir variable frameworks, significantly improves the accuracy of porosity logging interpretation and reduces its cost.

[0123] Any numerical value mentioned in this invention, if there is only a two-unit interval between any minimum and any maximum value, includes all values ​​that increase by one unit each time from the minimum to the maximum value. For example, if the amount of a component, or the value of a process variable such as temperature, pressure, or time, is stated as 50-90, in this specification it means specifically listing values ​​such as 51-89, 52-88… and 69-71 and 70-71, etc. For non-integer values, it may be appropriately considered that a unit is 0.1, 0.01, 0.001, or 0.0001. These are merely some specifically specified examples. In this application, in a similar manner, all possible combinations of numerical values ​​between the listed minimum and maximum values ​​are considered to have been disclosed.

[0124] It should be noted that the embodiments described above are only for explaining the present invention and do not constitute any limitation on the present invention. The present invention has been described with reference to typical embodiments, but it should be understood that the words used therein are descriptive and explanatory terms, not limiting terms. Modifications can be made to the present invention within the scope of the claims, and revisions can be made to the present invention without departing from the scope and spirit of the present invention. Although the present invention described herein relates to specific methods, materials, and embodiments, it does not mean that the present invention is limited to the specific examples disclosed herein; on the contrary, the present invention can be extended to all other methods and applications with the same function.

Claims

1. A method of evaluating porosity of a clastic reservoir, characterized by, The method comprises the following steps: S1: standardizing acoustic travel time, compensated density and compensated neutron logging data, using the standardized logging data to process core data to obtain corrected core porosity; S2: calculating porosity Φ1 using the standardized acoustic travel time data, the calculation formula being: In the formula, Φ1 is the porosity calculated by acoustic travel time, %; AC Ma - is the rock matrix acoustic travel time, μs / m; AC f - interval velocity of the formation fluid, μs / m; Cp is acoustic compaction correction coefficient; V sh - formation shale content, %; AC sh - mudstone interval transit time, μs / m; AC is acoustic travel time logging value of the target layer, μs / m; S3: calculating porosity Φ2 using the standardized compensated density data, the calculation formula being: In the formula, Φ2 is the porosity calculated by compensated density, %; DEN Ma - are respectively the rock skeleton density values, g / cm3; DEN f - is the formation fluid density value, g / cm3; V sh - Shale content of the formation, %; DEN sh - Mudstone density value, g / cm3; DEN is compensated density logging value of the target layer, g / cm³; S4: calculating porosity Φ3 using the standardized compensated neutron data, the calculation formula being: In the formula, Φ3 is the porosity calculated by compensated neutron, %; CNL Ma - rock matrix neutron value, %; V sh - Shale content of the formation, %; CNL sh - mudstone neutron value; CNL is compensated neutron logging value of the target layer, %; S5: multiple regression analysis is performed on Φ1, Φ2, Φ3 calculated in steps S2-S4 and the core porosity respectively to obtain correlation coefficients R1 2 , R2 2 , R3 2 , and the proportion of R1 2 , R2 2 , R3 2 in the sum of R1 2 , R2 2 , R3 2 is calculated respectively, the corresponding proportion is taken as the regression equation coefficient, and multiple regression analysis is performed again on the corrected core porosity to establish a porosity prediction model POR1; S6: performing multiple regression analysis on Φ1, Φ2 and Φ3 obtained in steps S2-S4 and the corrected core porosity to obtain a porosity prediction model POR2; S7: performing multiple regression analysis on the standardized acoustic travel time, compensated density and compensated neutron logging curves and the corrected core porosity to obtain a porosity prediction model POR3; S8: performing comparative analysis on the porosity prediction models POR1, POR2 and POR3 and the corrected core porosity, finding out the best porosity prediction model POR as the final porosity model, and obtaining a clastic rock reservoir porosity prediction curve through the final porosity prediction model POR to evaluate the physical property of the clastic rock reservoir. In the step S1, the standardization processing comprises: providing marker layer logging data in a study area, establishing a histogram, and establishing histogram comparison and correction with all well site data by taking the histogram as a standard, wherein the difference from the standard data is the correction amount.

2. The evaluation method according to claim 1, characterized by In the step S1, the core positioning processing comprises: presenting the experimentally measured core analysis porosity in a log chart in the form of a rod chart, and performing comparative analysis with logging data, taking the depth corresponding to the logging data as a standard to correct the depth corresponding to the experimentally measured core porosity data.

3. The evaluation method according to claim 1 or 2, characterized by, In the step S1, the core positioning processing further comprises extracting effective reservoir core porosity; the effective reservoir core porosity refers to the core porosity that has matching logging data at the corresponding depth after core positioning.

4. The evaluation method according to claim 1 or 2, characterized by, In the steps S5, S6 and S7, the multiple regression analysis is independently a linear function regression analysis of the correlation between multiple independent variables and dependent variables, which can eliminate errors caused by single curve prediction of reservoir physical property parameters; In the steps S5 and S6, the independent variables include Φ1, Φ2 and Φ3, and the independent variable in the step S7 is the standardized acoustic travel time, compensated density and compensated neutron; the dependent variable is the corrected core porosity.

5. The evaluation method according to claim 1 or 2, characterized by, In the steps S5, S6 and S7, the multiple regression analysis each independently obtains a correlation coefficient R 2 .

6. The evaluation method according to claim 1 or 2, characterized by, In the step S8, the best correlation means that the calculated porosity is compared with the corrected core porosity, and the correlation coefficient R 2 The closer to 1, the better the correlation.

7. The use of the method for evaluating the porosity of clastic rock reservoirs according to any one of claims 1 to 6 in the field of oil and gas exploration and development, geophysical well logging.

Images