Recording and measuring coupled mesozoic volcanic reservoir fluid quantitative identification method

Abstract

The invention discloses a recording and measuring coupled mesozoic volcanic reservoir fluid quantitative identification method, which comprises the following steps: determining fluid properties by using test data, selecting logging data and logging data reflecting the fluid properties, and screening out sensitive parameters related to the fluid properties by using a Pearson correlation analysis method; carrying out significance analysis on the sensitivity parameters, and screening out the sensitivity parameters without multiple collinearity; normalizing the sensitivity parameters, and calculating the weight of the sensitivity parameters by using a grey relational degree method; based on the weight of each sensitive parameter, constructing a quantitative evaluation index of the recorded and measured multi-parameter coupling fluid; and establishing a quantitative evaluation standard of the recorded and measured multi-parameter coupling fluid in combination with the test data and the quantitative evaluation index. According to the method, the comprehensive response of logging is combined, multi-parameter coupling is utilized to reflect different fluid properties, operation is easy, application and popularization are easy, and the method is suitable for rapid identification of fluid properties on site.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field exploration technology, specifically relating to a quantitative identification method for fluids in Mesozoic volcanic rock reservoirs using a recording and measurement coupling method. Background Technology

[0002] Mesozoic volcanic reservoir fluids exhibit diverse properties, influenced by factors such as complex lithology. The individual logging or well logging response characteristics of different fluids are not clearly defined, especially between gas-bearing and oil-bearing layers, and between water-bearing and dry layers, where the logging and well logging response characteristics are essentially indistinguishable. Existing methods for identifying volcanic reservoir fluids mainly employ well logging identification, well logging identification, and integrated logging-well logging chart identification. However, these conventional methods lack sufficient data mining depth and accuracy, and cannot effectively identify fluids in Mesozoic buried hill volcanic reservoirs.

[0003] Based on the above situation, there is an urgent need for a method to quantitatively identify fluids by coupling integrated logging and well logging data, so as to better distinguish the properties of different fluids. Summary of the Invention

[0004] This invention addresses the problem in existing technologies where different fluid properties in Mesozoic buried hill volcanic rocks cannot be distinguished from a single logging or well logging response. Its purpose is to provide a quantitative identification method for fluids in Mesozoic volcanic rock reservoirs that combines logging and well logging.

[0005] This invention is achieved through the following technical solution:

[0006] A method for quantitative identification of fluids in Mesozoic volcanic reservoirs using a recording-measuring coupling approach includes the following steps:

[0007] S1. Determine fluid properties using test data, select logging and well logging data that reflect fluid properties, and use Pearson correlation analysis to screen out sensitive parameters related to fluid properties;

[0008] The logging data reflecting fluid properties includes gas logging data and geochemical logging data;

[0009] The gas logging data includes total hydrocarbon multiples (RTg) and methane C1;

[0010] The geochemical logging data includes gaseous hydrocarbon quantity S0, liquid hydrocarbon quantity S1, cracked hydrocarbon quantity S2, the sum of liquid hydrocarbon quantity and cracked hydrocarbon quantity S1+S2, total oil and gas content Pg, gas production index GPI, oil production index OPI, and total oil and gas production index TPI.

[0011] The logging data reflecting fluid properties include resistivity RD, natural gamma ray GR, sonic transit time AC, density, and deep resistivity taken as the natural logarithm ln(RD).

[0012] Based on the heatmap matrix, the sensitive parameters related to fluid properties selected by Pearson correlation analysis are: liquid hydrocarbon content S1, total oil and gas yield index TPI, total hydrocarbon multiple RTg, resistivity RD, and acoustic transit time AC.

[0013] The specific method of using Pearson correlation analysis is as follows: conduct correlation analysis on different fluid properties and logging data to obtain the correlation coefficient R between fluid properties and logging data, and select parameters with correlation coefficient R close to 1 and -1 as sensitivity parameters.

[0014] Let fluid properties be the independent variable X, and well logging and well logging data be the dependent variable Y. The specific steps for calculating the correlation between the two using Pearson correlation analysis are as follows:

[0015] S11. Calculate the covariance between the independent variable X and the dependent variable Y;

[0016] The formula for calculating the covariance is:

[0017] Cov(X,Y)=E[(XE(X))(YE(Y))]…… (1)

[0018] In the formula: Cov(X,Y) is the covariance of X and Y, and E(X) and E(Y) are the expected values ​​of the independent variable X and the dependent variable Y, respectively;

[0019] The expected values ​​E(X) and E(Y) of the independent variable X and dependent variable Y are calculated as follows:

[0020] The distribution law of discrete random variable X is as follows:

[0021] P{X=x k}=p k k = 1, 2, ..., n…… (2)

[0022] If the following series converges absolutely:

[0023]

[0024] Then the expected value E(X) of variable X is:

[0025]

[0026] The calculation method for E(y) is the same as that for E(X);

[0027] S12. Calculate the standard deviations of the independent variable X and the dependent variable Y:

[0028] S121. Calculate the variances Var(X) and Var(Y) of the independent variable X and the dependent variable Y:

[0029] The formula for calculating the variance Var(X) of the independent variable X is as follows:

[0030] Var(X)=E[(XE(X)) 2 ... (5)

[0031] The formula for calculating the variance Var(Y) of the dependent variable Y is as follows:

[0032] Var(Y)=E[(YE(Y)) 2 ... (6);

[0033] S122. Calculate the standard deviations of the independent variable X and the dependent variable Y.

[0034] The formula for calculating the standard deviation of the independent variable X is:

[0035]

[0036] The formula for calculating the standard deviation of the dependent variable Y is:

[0037]

[0038] S13. Calculate the Pearson coefficient R;

[0039] The formula for calculating the Pearson coefficient R is as follows:

[0040]

[0041] When the Pearson coefficient R > 0, it indicates that the independent variable X and the dependent variable Y are positively correlated;

[0042] When the Pearson coefficient R < 0, it indicates that the independent variable X and the dependent variable Y are negatively correlated;

[0043] The closer |R| is to 1, the better the correlation between the independent variable X and the dependent variable Y.

[0044] The closer |R| is to 0, the worse the correlation between the independent variable X and the dependent variable Y.

[0045] S2. Conduct significance analysis on the sensitivity parameters selected in step S1 (Reference: Applied Regression Analysis (3rd Edition), edited by He Xiaoqun and Liu Wenqing, China Renmin University Press, 2011.9) to eliminate overfitting that may be caused by multicollinearity. If there is multicollinearity between two or more sensitivity parameters, select the parameter with the highest Person correlation coefficient.

[0046] Analysis shows that the sensitivity parameters screened in step S1 (liquid hydrocarbon quantity S1, total oil and gas yield index TPI, total hydrocarbon multiple RTg, resistivity RD, and acoustic transit time AC) do not have multicollinearity and can all be used for subsequent parameter construction.

[0047] S3. Normalize the sensitivity parameters selected in step S2, and then use the grey relational analysis method to calculate the weight of the sensitivity parameters.

[0048] Grey Relational Analysis (GRA): Relationship degree refers to the degree of correlation between factors in two systems as they change over time or between different objects. If the two factors change in the same direction, that is, the degree of synchronous change is high, then the degree of correlation between them is high; otherwise, it is low. Therefore, Grey Relational Analysis is a method to measure the degree of correlation between factors based on the similarity or difference between their development trends, that is, "grey relational degree".

[0049] The calculation of the weights of the sensitivity parameters selected in step S2 using the grey relational analysis method specifically includes the following steps:

[0050] S31. Create analysis data columns;

[0051] The analyzed data series includes a reference sequence and a comparison sequence. The reference sequence reflects the characteristics of system behavior, and the comparison sequence is a data sequence composed of factors that affect system behavior.

[0052] The reference sequence is represented as follows:

[0053] X0={X0(k)|k=1,2,…n}…… (10)

[0054] The comparison sequence is represented as follows:

[0055] X i ={X i (k)|k=1,2,…n},i=1,2,…m… (11)

[0056] In the formula: k represents the number of data points in each data sequence, and i represents the number of comparison sequences;

[0057] S32. Use the mean value method to make the data column dimensionless;

[0058] Different comparison sequences have different physical meanings and different data dimensions, which is not conducive to comparison or makes it difficult to obtain correct conclusions. Therefore, before performing grey relational analysis, it is usually necessary to perform dimensionless processing. This application uses the mean value method to perform dimensionless processing on the data sequences.

[0059] The formula for calculating the mean is:

[0060]

[0061] S33. Calculate the absolute difference;

[0062] The absolute difference is calculated using the dimensionless reference sequence and the comparison sequence. The formula is as follows:

[0063]

[0064] In the formula: The reference sequence data after dimensionless processing; The comparison sequence data is after dimensionless processing;

[0065] S34. Calculate the maximum and minimum differences between the two poles;

[0066] The formula for calculating the maximum difference is:

[0067]

[0068] The formula for calculating the minimum difference is:

[0069]

[0070] S35. Calculate the correlation coefficient based on the maximum and minimum differences;

[0071] The formula for calculating the correlation coefficient is:

[0072]

[0073] In the formula, ξ 0,i (k) is the correlation coefficient of the i-th parameter in the k-th column, which is dimensionless; ρ is the resolution coefficient, which is usually taken as 0.2;

[0074] S36. Based on the correlation coefficients calculated for different comparison sequences, use their arithmetic mean to determine the correlation degree of the comparison sequence:

[0075] The formula for calculating the correlation degree is:

[0076]

[0077] In the formula: ξ 0,i (k) is the correlation coefficient of the data in the k-th column of the i-th parameter, dimensionless; γ 0,i The correlation degree of the i-th parameter is dimensionless.

[0078] S37. Calculate the weights of each parameter:

[0079] The weighting coefficient is obtained by calculating the correlation degree of each parameter and then calculating the percentage of the correlation degree of each parameter to the sum of the correlation degrees of all parameters.

[0080] The formula for calculating the weighting coefficient is as follows:

[0081]

[0082] In the formula: w i To obtain the weight of the i-th parameter, γ 0,i Let be the correlation degree of the i-th parameter.

[0083] S4. Based on the weights of each sensitivity parameter determined in step S3, construct a quantitative evaluation index for multi-parameter coupled fluid recording.

[0084] The calculation formula for the quantitative evaluation index of the multi-parameter coupled fluid is as follows:

[0085]

[0086] In the formula: FI is the quantitative evaluation index of multi-parameter coupled fluid recording; w i To calculate the weight of the i-th parameter, x i For the i-th normalized sensitive parameter value;

[0087] S5. Combining the test data with the quantitative evaluation indicators constructed in step S4, establish a quantitative evaluation standard for multi-parameter coupled fluid.

[0088] Specifically, based on the fluid quantitative evaluation index FI constructed by S4, the FI value of the test well is calculated, and then a fluid quantitative evaluation standard is established according to the distribution range of FI values ​​for different fluid properties.

[0089] The beneficial effects of this invention are:

[0090] This invention provides a quantitative identification method for fluids in Mesozoic volcanic reservoirs using a logging-data coupling approach. It comprehensively utilizes the response characteristics of logging and well logging data to different fluid properties to construct a quantitative fluid evaluation method. This invention comprehensively considers data common to most wells (logging and well logging), couples multiple logging and well logging parameters based on sensitivity analysis, constructs quantitative fluid identification indicators, and establishes fluid identification standards in conjunction with test data. This reduces the need for manual intervention and the requirements for expertise, while also addressing challenges related to operability, enabling rapid and accurate identification of fluids in volcanic reservoirs. Attached Figure Description

[0091] Figure 1 This is the result of sensitive parameter analysis of the heatmap matrix based on Person correlation analysis in Embodiment 1 of the present invention;

[0092] Figure 2 This is a statistical histogram of the fluid quantitative evaluation parameter FI calculated for different fluid properties in the test well according to Embodiment 1 of the present invention;

[0093] Figure 3 This is a diagram showing the actual well comprehensive treatment results of Embodiment 1 of the present invention.

[0094] For those skilled in the art, other related figures can be obtained from the above figures without any creative effort. Detailed Implementation

[0095] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0096] Example 1

[0097] A method for quantitative identification of fluids in Mesozoic volcanic reservoirs using a recording-measuring coupling approach includes the following steps:

[0098] S1. Determine fluid properties using test data, select logging and well logging data that reflect fluid properties, and use Pearson correlation analysis to screen out sensitive parameters related to fluid properties. Figure 1 );

[0099] The logging data reflecting fluid properties includes gas logging data and geochemical logging data;

[0100] The gas logging data includes total hydrocarbon multiples (RTg) and methane C1;

[0101] The geochemical logging data includes gaseous hydrocarbon content S0, liquid hydrocarbon content S1, cracked hydrocarbon content S2, S1+S2, total oil and gas content Pg, gas production index GPI, oil production index OPI, and total oil and gas production index TPI.

[0102] The logging data reflecting fluid properties include resistivity RD, natural gamma ray GR, sonic transit time AC, density, and ln(RD);

[0103] Based on the heatmap matrix, the sensitive parameters related to fluid properties selected by Pearson correlation analysis are: liquid hydrocarbon quantity S1, total oil and gas yield index TPI, total hydrocarbon multiple RTg, resistivity RD, and acoustic transit time AC.

[0104] S2. Conduct significance analysis on the sensitivity parameters selected in step S1 (Reference: Applied Regression Analysis (3rd Edition), edited by He Xiaoqun and Liu Wenqing, China Renmin University Press, September 2011) to eliminate overfitting that may be caused by multicollinearity.

[0105] Analysis shows that there is no multicollinearity among the five sensitive parameters selected in step S1 (liquid hydrocarbon quantity S1, total oil and gas yield index TPI, total hydrocarbon multiple RTg, resistivity RD, and acoustic transit time AC) (Table 1), and all of them can be used for subsequent parameter construction.

[0106] Table 1: Statistical table of significance analysis results for the five selected sensitivity parameters;

[0107]

[0108] S3. Normalize the sensitivity parameters selected in step S2, and then use the grey relational analysis method to calculate the weights of the sensitivity parameters (Table 2).

[0109] Table 2: Statistical table of weights of five sensitivity parameters obtained from grey relational degree calculation;

[0110] parameter RDnorm S1norm TPInorm RTgnorm ACnorm Weighting coefficient 0.261 0.210 0.264 0.176 0.089

[0111] S4. Based on the weights of each sensitivity parameter determined in step S3, construct a quantitative evaluation index for multi-parameter coupled fluid recording.

[0112] The calculation formula for the quantitative evaluation index of the multi-parameter coupled fluid is as follows:

[0113] FI = w RD RD norm -w S1 S1 norm -w TPI TPI norm -w RTg RTg norm -w AC AC norm …(20)

[0114] In the formula: w RD w S1 w TPI w RTg w AC The weights are respectively: resistivity RD, gaseous hydrocarbon quantity S1, total oil and gas yield index TPI, total hydrocarbon multiple RTg, and acoustic transit time AC;

[0115] Substituting Table 2 into formula (20) yields the calculation formula for this embodiment:

[0116] FI = 0.261RD norm -0.21S1 norm -0.264 TPI norm -0.176RTg norm -0.089AC norm …(21)S5. Combining the test data with the quantitative evaluation indicators constructed in S4 above, establish a quantitative evaluation standard for multi-parameter coupled fluid recording. Figure 2 Table 3), the actual processing results (Table 5) are consistent with the test results, and have good field application effects. Figure 3 (Table 4).

[0117] Table 3: Statistical Table of Fluid Quantitative Evaluation Standards;

[0118] air layer oil layer Oil and water in the same layer water layer dry layer FI <-0.46 -0.46～-0.36 -0.32～-0.17 -0.17～-0.08 >-0.08

[0119] Table 4: Actual test data of the treated wells;

[0120]

[0121] Table 5: Interpretation Results of Actual Well Processing

[0122] Serial Number strata Explanation of the paragraph Explanation of conclusions 1 Mesozoic 1693-1832 air layer 2 Mesozoic 1832-1898.45 dry layer 3 Mesozoic 1898.45-2028.6 oil layer 4 Mesozoic 2028.6-2660 dry layer

[0123] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for quantitative identification of fluids in Mesozoic volcanic reservoirs using a recording-measuring coupling method, characterized in that: Includes the following steps: S1. Determine fluid properties using test data, select logging and well logging data that reflect fluid properties, and use Pearson correlation analysis to screen out sensitive parameters related to fluid properties; S2. Conduct a significance analysis on the sensitivity parameters selected in step S1; S3. Normalize the sensitivity parameters selected in step S2, and then use the grey relational analysis method to calculate the weight of the sensitivity parameters. S4. Based on the weights of each sensitivity parameter determined in step S3, construct a quantitative evaluation index for multi-parameter coupled fluid recording. S5. Combining the test data with the quantitative evaluation indicators constructed in step S4, establish a quantitative evaluation standard for multi-parameter coupled fluid.

2. The method for quantitative identification of fluids in Mesozoic volcanic reservoirs using recording and measurement coupling as described in claim 1, characterized in that: The logging data reflecting fluid properties includes gas logging data and geochemical logging data; the gas logging data includes total hydrocarbon multiple RTg and methane C1; the geochemical logging data includes gaseous hydrocarbon quantity S0, liquid hydrocarbon quantity S1, cracked hydrocarbon quantity S2, the sum of liquid hydrocarbon quantity and cracked hydrocarbon quantity S1+S2, total oil and gas content Pg, gas production index GPI, oil production index OPI, and total oil and gas production index TPI; the logging data reflecting fluid properties includes resistivity RD, natural gamma ray GR, sonic transit time AC, density, and deep resistivity taken as the natural logarithm ln(RD).

3. The method for quantitative identification of fluids in Mesozoic volcanic reservoirs using recording and measurement coupling as described in claim 1, characterized in that: The specific method of using Pearson correlation analysis is as follows: conduct correlation analysis on different fluid properties and logging data to obtain the correlation coefficient R between fluid properties and logging data, and select parameters with correlation coefficient R close to 1 and -1 as sensitivity parameters.

4. The method for quantitative identification of fluids in Mesozoic volcanic reservoirs using recording and measurement coupling as described in claim 1, characterized in that: In the significance analysis of step S2, if there is multicollinearity among two or more sensitivity parameters, the sensitivity parameter with the highest Person correlation coefficient is selected to participate in subsequent steps.

5. The method for quantitative identification of fluids in Mesozoic volcanic reservoirs using recording and measurement coupling as described in claim 1, characterized in that: The calculation of the weights of the sensitivity parameters selected in step S2 using the grey relational analysis method specifically includes the following steps: S31. Create analysis data columns; The analyzed data series includes a reference sequence and a comparison sequence. The reference sequence reflects the characteristics of system behavior, and the comparison sequence is a data sequence composed of factors that affect system behavior. The reference sequence is represented as follows: X0={X0(k)|k=1,2,…n}……(10) The comparison sequence is represented as follows: X i ={X i (k)|k=1,2,…n},i=1,2,…m……(11) In the formula: k represents the number of data points in each data sequence, and i represents the number of comparison sequences; S32. Use the mean value method to perform dimensionless processing on the data column; Different comparison sequences have different physical meanings and different data dimensions, which is not conducive to comparison or makes it difficult to obtain correct conclusions. Therefore, before performing grey relational analysis, it is usually necessary to perform dimensionless processing. This application uses the mean value method to perform dimensionless processing on the data sequences. The formula for calculating the mean is: S33. Calculate the absolute difference; The absolute difference is calculated using the dimensionless reference sequence and the comparison sequence. The formula is as follows: In the formula: This is the reference sequence data after dimensionless processing; The comparison sequence data is after dimensionless processing; S34. Calculate the maximum and minimum differences between the two poles; The formula for calculating the maximum difference is: The formula for calculating the minimum difference is: S35. Calculate the correlation coefficient based on the maximum and minimum differences; The formula for calculating the correlation coefficient is: In the formula, ξ 0,i (k) is the correlation coefficient of the i-th parameter in the k-th column, which is dimensionless; ρ is the resolution coefficient, which is usually taken as 0.2; S36. Based on the correlation coefficients calculated for different comparison sequences, use their arithmetic mean to determine the correlation degree of the comparison sequence: The formula for calculating the correlation degree is: In the formula: ξ 0,i (k) is the correlation coefficient of the data in the k-th column of the i-th parameter, dimensionless; γ 0,i The correlation degree of the i-th parameter is dimensionless. S37. Calculate the weights of each parameter: The weighting coefficient is obtained by calculating the correlation degree of each parameter and then calculating the percentage of the correlation degree of each parameter to the sum of the correlation degrees of all parameters. The formula for calculating the weighting coefficient is as follows: In the formula: w i To obtain the weight of the i-th parameter, γ 0,i Let be the correlation degree of the i-th parameter.

6. The method for quantitative identification of fluids in Mesozoic volcanic reservoirs using recording and measurement coupling as described in claim 1, characterized in that: The calculation formula for the quantitative evaluation index of the multi-parameter coupled fluid is as follows: In the formula: FI is the quantitative evaluation index of multi-parameter coupled fluid recording; w i To calculate the weight of the i-th parameter, x i Let be the value of the sensitivity parameter for the i-th normalization.

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