Quantitative prediction method for content of carbonate and terrestrial clastic mixed rock cement

Abstract

The invention discloses a quantitative prediction method for the content of carbonate and terrestrial clastic mixed rock cement, which comprises the following steps: S1, acquiring logging parameters of the depth of a stratum where a measured mixed rock sample is located, and observing the measured value of the content of the measured mixed rock cement in the measured mixed rock sample through a rock slice lens; s2, fitting the logging parameters with the measured value of the content of the cement in the mixed rock, and constructing a prediction model of the content of the cement in the mixed rock; s3, well logging parameters of the depth of the stratum where a to-be-measured mixed rock sample is located are obtained, and the mixed rock cement content of the to-be-measured mixed rock sample is obtained through prediction in combination with the mixed rock cement content prediction model. According to the method, the content of the cement in the mixed rock can be accurately predicted through the established mixed rock cement content prediction model, and technical support is provided for development of carbonate and terrestrial clastic mixed rock.

Description

Technical Field

[0001] The invention relates to the technical field of carbonate oil and gas reservoir exploration and development, and in particular to a method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks. Background Art

[0002] As a special type of sedimentation, mixed sedimentation of terrigenous clastics and carbonates is not only a special sign for understanding the paleoclimate, sea (lake) level changes, sedimentation rate and sedimentation dynamics in the basin, but also may be a high-quality oil and gas reservoir in the deep layer. In recent years, mixed sedimentation of terrigenous clastics and carbonates has been discovered in some continental basins in my country, and remarkable results have been achieved in the exploration of these mixed rock reservoirs. However, the research on mixed rock reservoirs is very weak, in which the filling of pores between rock skeleton particles by cementing materials will lead to a decrease in porosity and permeability, making the reservoir performance worse. Therefore, in the study of reservoir quality, in addition to judging the composition of the cementing material of the reservoir, it is also necessary to quantitatively judge the content of the cementing material of the reservoir.

[0003] At present, the research on quantitative prediction of cement content at home and abroad has always been accustomed to studying terrigenous clastic rocks and carbonate rocks as two independent systems, and has not linked the two together, resulting in less research on quantitative prediction methods for cement content in mixed sedimentary rocks. At present, quantitative prediction of cement content in mixed sedimentary rocks is mainly obtained by observation of rock thin sections under a coring well microscope, but this method can only measure the cement content of samples in the coring well section, and cannot obtain the cement content of non-coring wells. In addition, coring wells are relatively limited, small in number, and expensive. Summary of the invention

[0004] In view of the above problems, the present invention aims to provide a method for quantitatively predicting the cement content of carbonate and terrigenous clastic mixed rocks.

[0005] The technical solution of the present invention is as follows:

[0006] A method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks comprises the following steps:

[0007] S1: obtaining the logging parameters of the stratum depth where the measured mixed rock sample is located, wherein the measured mixed rock sample has been observed by a rock thin section microscope to obtain the measured value of the mixed rock cement content;

[0008] S2: Fitting the logging parameters with the measured values ​​of cement content in the mixed sedimentary rock to construct a prediction model for cement content in the mixed sedimentary rock;

[0009] S3: obtaining the logging parameters of the stratum depth where the mixed rock sample to be tested is located, and predicting the mixed rock cement content of the mixed rock sample to be tested in combination with the mixed rock cement content prediction model.

[0010] Preferably, in step S1, the logging parameters include acoustic time difference, compensated neutron, compensated density, natural gamma and resistivity.

[0011] Preferably, step S1 or step S1 and step S3 further include a step of preprocessing the logging parameters to remove abnormal values ​​in the logging parameters.

[0012] Preferably, the pretreatment specifically comprises the following sub-steps:

[0013] The average value of the original data of each logging parameter is obtained respectively, and the deviation and probability error between the original data of the logging parameter and the average value are calculated by the following formula:

[0014]

[0015] Where: D is the deviation; x i is the value of the original data of the i-th logging parameter; is the average value of the original data of the logging parameters; p is the probability error; n is the total number of original data of each logging parameter;

[0016] The D / p value of each logging parameter is calculated respectively. If the D / p value is greater than the D / p standard value, the corresponding logging parameter is an abnormal value.

[0017] Preferably, the D / p standard value is obtained by the following standard:

[0018] Among n original data, the deviation values ​​of all original data are obtained. If the deviation value of a certain original data is D1, when the probability of occurrence of all deviations equal to or greater than D1 is less than 1 / (2n), then the original data is an abnormal value.

[0019] Preferably, step S2 further includes the step of reducing the dimension of the logging parameters to obtain the reduced-dimensional parameters; when fitting, the reduced-dimensional parameters are fitted with the measured values ​​of the cement content of the mixed rock.

[0020] Preferably, the dimension reduction of the logging parameters specifically includes the following sub-steps:

[0021] The raw data of the logging parameters are standardized using the following formula to obtain a standardized data matrix:

[0022]

[0023] Where: x i ′ j is the standardized x ij ;x ij is the parameter of row i and column j; is the average value of all parameters in the jth column; n is the total number of original data of each logging parameter;

[0024] The correlation coefficient is calculated according to the standardized data matrix to obtain a correlation coefficient matrix; the correlation coefficient is calculated by the following formula:

[0025]

[0026] Where: R ij is the correlation coefficient; m is the total number of rows / columns of the standardized data matrix; k represents the kth row / column of the standardized data matrix; x ik is the parameter of the i-th row and k-th column in the standardized data matrix; is the average value of all parameters in the i-th row of the standardized data matrix; x jk is the parameter of the jth column and the kth row in the standardized data matrix; is the average value of all parameters in the jth column of the standardized data matrix;

[0027] Establish a feature matrix |R-λE|=0, and obtain the eigenvalue λ of the feature matrix, cumulatively calculate the sum of the eigenvalues ​​from large to small, select the parameter whose sum of eigenvalues ​​is greater than the eigenvalue threshold as the parameter after dimensionality reduction, and set the expression of the parameter after dimensionality reduction to:

[0028] F i =x i AC+x i+1 CNL+x i+2 DEN+x i+3 GR+x i+4 RT (i=1、2、3……) (5)

[0029] Where: F i is the i-th dimension reduction parameter; x i 、x i+1 、x i+2 、x i+3 、x i+4 is the corresponding eigenvalue λ i The characteristic vector of; AC is the acoustic time difference; CNL is the compensation neutron; DEN is the compensation density; GR is the natural gamma; RT is the resistivity;

[0030] Establish the equation system (R-λE)X=0, and find the non-zero solution of the equation system. The non-zero solution obtained is the corresponding eigenvalue λ i The eigenvector x i 、x i+1 、x i+2 、x i+3 、x i+4, substitute the feature vector into the expression of the reduced dimension parameter to obtain the reduced dimension parameter.

[0031] Preferably, the eigenvalue threshold is 85% of the total eigenvalues.

[0032] Preferably, when there are only two parameters after dimension reduction and only one fitting is performed, the formula for one fitting is:

[0033] Y=aX1+bX2+c (6)

[0034]

[0035] In the formula: Y is the dependent variable parameter, dimensionless; a, b, c are all fitting coefficients, dimensionless; X1, X2 are independent variable parameters, dimensionless; Y i is the i-th data of the dependent variable parameter; X 1i , X 2i They are the i-th data of the first independent variable parameter and the second independent variable parameter respectively; are the average values ​​of the dependent variable parameter, the first independent variable parameter, and the second independent variable parameter respectively;

[0036] The prediction model of cement content of the mixed rock obtained by one fitting is:

[0037] Q1=aF1+bF2+c (10)

[0038] Where: Q1 is the predicted value of cement content of mixed sedimentary rock after a first fitting, dimensionless; F1 and F2 are the parameters after dimensionality reduction, dimensionless.

[0039] Preferably, when there are only two parameters after the dimension reduction, and a secondary fitting is performed on the basis of the primary fitting, the formula for the secondary fitting is:

[0040] Y=d+eX (11)

[0041]

[0042] Where: e and d are both fitting coefficients, dimensionless; X i is the i-th data of the independent variable parameter; is the mean value of the independent variable parameter;

[0043] The prediction model of cement content of the mixed rock obtained by quadratic fitting is:

[0044] Q2=e(aF1+bF2+c)+d (14)

[0045] Where: Q2 is the predicted value of cement content of mixed sedimentary rock by quadratic fitting, dimensionless.

[0046] The beneficial effects of the present invention are:

[0047] The cement content prediction model of mixed sedimentary rocks established in the present invention can accurately predict the cement content of mixed sedimentary rocks, providing technical support for the development of mixed sedimentary rocks with carbonates and terrigenous clastics. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 It is a correlation diagram between the measured value of cement content in mixed sedimentary rock and the first fitting predicted value in a specific embodiment. DETAILED DESCRIPTION

[0050] The present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, in the absence of conflict, the embodiments in this application and the technical features in the embodiments can be combined with each other. It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as those generally understood by those of ordinary skill in the art to which this application belongs. The words "including" or "comprising" and the like used in the disclosure of the present invention mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0051] The present invention provides a method for quantitatively predicting the cement content of carbonate and terrigenous clastic mixed rocks, comprising the following steps:

[0052] S1: Obtaining the logging parameters of the stratum depth where the measured mixed rock sample is located, wherein the measured mixed rock sample is observed by a rock thin section microscope to obtain the measured value of the mixed rock cement content.

[0053] In a specific embodiment, the logging parameters include acoustic wave time difference, compensated neutron, compensated density, natural gamma and resistivity.

[0054] In a specific embodiment, this step also includes the step of preprocessing the logging parameters to remove abnormal values ​​in the logging parameters. Optionally, the preprocessing specifically includes the following sub-steps:

[0055] The average value of the original data of each logging parameter is obtained respectively, and the deviation and probability error between the original data of the logging parameter and the average value are calculated by the following formula:

[0056]

[0057] Where: D is the deviation; x i is the value of the original data of the i-th logging parameter; is the average value of the original data of the logging parameters; p is the probability error; n is the total number of original data of each logging parameter;

[0058] The D / p value of each logging parameter is calculated respectively. If the D / p value is greater than the D / p standard value, the corresponding logging parameter is an abnormal value.

[0059] In a specific embodiment, the D / p standard value is obtained by the following standard: among n raw data, the deviation values ​​of all raw data are obtained. If the deviation value of a certain raw data is D1, when the probability of occurrence of all deviations equal to or greater than D1 is less than 1 / (2n), then the raw data is an abnormal value. The standard of this embodiment can be used to establish a D / p standard value table corresponding to the number of raw data n, and the results are shown in Table 1:

[0060] Table 1D / p standard value table

[0061] n 5 10 15 20 50 100 D / p 2.5 2.9 3.2 3.3 3.8 4.2

[0062] It should be noted that n in Table 1 is only some conventional values, and the D / p standard values ​​corresponding to other n values ​​can be obtained through the standards of the above embodiments.

[0063] It should be noted that the purpose of preprocessing the logging parameters is mainly to remove outliers in the logging parameters, reduce errors, and avoid outliers affecting the prediction accuracy of the final model. In addition to the preprocessing method in the above embodiment, other methods for identifying and removing outliers in the prior art can also be applied to the present invention, such as box plot detection method, mathematical statistical method (mean, standard deviation, etc.), discrete point test method, etc.

[0064] S2: Fitting the logging parameters with the measured values ​​of the cement content of the mixed rock to construct a prediction model for the cement content of the mixed rock.

[0065] In a specific embodiment, this step also includes the step of reducing the dimension of the logging parameters to obtain the parameters after dimension reduction; when fitting, the parameters after dimension reduction are fitted with the measured values ​​of the cement content of the mixed rock.

[0066] In the above embodiment, by reducing the dimension of the logging parameters, the parameters required for modeling can be reduced, and only a small number of parameters can be used to include the information of previous multiple parameters, which simplifies the prediction model of the cement content of mixed sedimentary rocks and can also make accurate predictions.

[0067] In a specific embodiment, the dimension reduction of the logging parameters specifically includes the following sub-steps:

[0068] The raw data of the logging parameters are standardized using the following formula to obtain a standardized data matrix:

[0069]

[0070] Where: x i ′ j is the standardized x ij ;x ij is the parameter of row i and column j; is the average value of all parameters in the jth column; n is the total number of original data of each logging parameter;

[0071] The correlation coefficient is calculated according to the standardized data matrix to obtain a correlation coefficient matrix; the correlation coefficient is calculated by the following formula:

[0072]

[0073] Where: R ij is the correlation coefficient; m is the total number of rows / columns of the standardized data matrix; k represents the kth row / column of the standardized data matrix; x ik is the parameter of the i-th row and k-th column in the standardized data matrix; is the average value of all parameters in the i-th row of the standardized data matrix; x jk is the parameter of the jth column and the kth row in the standardized data matrix; is the average value of all parameters in the jth column of the standardized data matrix;

[0074] Establish a feature matrix |R-λE|=0, and obtain the eigenvalue λ of the feature matrix, cumulatively calculate the sum of the eigenvalues ​​from large to small, select the parameter whose sum of eigenvalues ​​is greater than the eigenvalue threshold as the parameter after dimensionality reduction, and set the expression of the parameter after dimensionality reduction to:

[0075] F i =x i AC+ x i+1 CNL+x i+2 DEN+x i+3 GR+x i+4 RT (i=1、2、3……) (5)

[0076] Where: F i is the i-th dimension reduction parameter; x i 、x i+1 、x i+2 、x i+3 、xi+4 is the corresponding eigenvalue λ i The characteristic vector of; AC is the acoustic time difference; CNL is the compensation neutron; DEN is the compensation density; GR is the natural gamma; RT is the resistivity;

[0077] Establish the equation system (R-λE)X=0, and find the non-zero solution of the equation system. The non-zero solution obtained is the corresponding eigenvalue λ i The eigenvector x i 、x i+1 、x i+2 、x i+3 、x i+4 , substitute the feature vector into the expression of the reduced dimension parameter to obtain the reduced dimension parameter.

[0078] In a specific embodiment, the eigenvalue threshold is 85% of the total eigenvalue. It should be noted that the eigenvalue threshold affects the dimensionality reduction effect. The larger the eigenvalue threshold, the worse the dimensionality reduction effect, but it contains more information about the previous multi-parameters; the smaller the eigenvalue threshold, the better the dimensionality reduction effect, but it contains less information about the previous multi-parameters, which will affect the prediction accuracy of the established model. The specific value of the eigenvalue threshold can be selected according to the target prediction accuracy.

[0079] In a specific embodiment, when there are only two parameters after dimension reduction and only one fitting is performed, the formula for one fitting is:

[0080] Y=aX1+bX2+c (6)

[0081]

[0082] In the formula: Y is the dependent variable parameter, dimensionless; a, b, c are all fitting coefficients, dimensionless; X1, X2 are independent variable parameters, dimensionless; Y i is the i-th data of the dependent variable parameter; X 1i , X 2i They are the i-th data of the first independent variable parameter and the second independent variable parameter respectively; are the average values ​​of the dependent variable parameter, the first independent variable parameter, and the second independent variable parameter respectively;

[0083] The prediction model of cement content of the mixed rock obtained by one fitting is:

[0084] Q1=aF1+bF2+c (10)

[0085] Where: Q1 is the predicted value of cement content of mixed sedimentary rock after a first fitting, dimensionless; F1 and F2 are the parameters after dimensionality reduction, dimensionless.

[0086] In a specific embodiment, when there are only two parameters after the dimension reduction, and a secondary fitting is performed on the basis of the primary fitting, the formula for the secondary fitting is:

[0087] Y=d+eX (11)

[0088]

[0089] Where: e and d are both fitting coefficients, dimensionless; X i is the i-th data of the independent variable parameter; is the mean value of the independent variable parameter;

[0090] The prediction model of cement content of the mixed rock obtained by quadratic fitting is:

[0091] Q2=e(aF1+bF2+c)+d (14)

[0092] Where: Q2 is the predicted value of cement content of mixed sedimentary rock by quadratic fitting, dimensionless.

[0093] In the above embodiment, the present invention performs a secondary fitting on the basis of the primary fitting, which can further reduce the error and improve the prediction accuracy of the cement content prediction model of the mixed sedimentary rock.

[0094] S3: obtaining the logging parameters of the stratum depth where the mixed rock sample to be tested is located, and predicting the mixed rock cement content of the mixed rock sample to be tested in combination with the mixed rock cement content prediction model.

[0095] In a specific embodiment, taking a certain study area as an example, the quantitative prediction method for the content of cement in carbonate and terrigenous clastic mixed rock of the present invention is used to predict the content of mixed rock cement in the mixed rock sample to be tested, which specifically includes the following steps:

[0096] (1) Obtain the logging parameters of the stratum depth where the measured mixed rock samples are located and the measured values ​​of the mixed rock cement content. The results are shown in Table 2:

[0097] Table 2 Logging parameters of the measured mixed rock samples and measured values ​​of cement content in mixed rock

[0098]

[0099]

[0100] (2) preprocessing the logging parameters to remove abnormal values ​​in the logging parameters;

[0101] First, calculate the average value of each parameter in Table 2 The deviation D of each parameter from the corresponding mean value and its D 2; Secondly, calculate the probability error p of each parameter; then calculate the D / p value of each parameter; finally, compare the D / p value of each parameter with the D / p standard value. If its value is greater than the standard value, it is an abnormal value and is discarded. The calculation results are shown in Table 3 and Table 4:

[0102] Table 3 Preprocessing calculation results Table 1

[0103]

[0104]

[0105] Table 4 Preprocessing calculation results Table 2

[0106]

[0107]

[0108] In this embodiment, the total number n of raw data of each logging parameter is 20. According to the acquisition standard of the D / p standard value, the corresponding D / p standard value is 3.3. It can be seen from Tables 3 and 4 that sample No. 10 has D / p=3.3=3.3, and sample No. 19 has D / p=3.5>3.3. The RT data is abnormal, so the RT in samples No. 10 and No. 19 is an external value. In order to reduce the error of the prediction equation, samples 10 and 19 are discarded.

[0109] (3) performing dimension reduction on the logging parameters to obtain dimension-reduced parameters;

[0110] First, the parameter data are standardized to eliminate the influence of the parameters on the level and dimension, and the correlation coefficient matrix of the standardized data is obtained. The results are shown in Table 5:

[0111] Table 5 Correlation coefficient matrix

[0112] parameter AC CNL DEN GR RT AC 1 0.727 -0.782 0.844 -0.798 CNL 0.727 1 -0.735 0.825 -0.811 DEN -0.782 -0.735 1 -0.866 0.76 GR 0.844 0.825 -0.866 1 -0.859 RT -0.798 -0.811 0.76 -0.859 1

[0113] Next, we build the feature matrix. The result is as follows:

[0114]

[0115] The eigenvalues ​​of the characteristic matrix are obtained, and the result is λ1=4.205; λ 2＝ 0.299; λ3=0.232; λ 4＝ 0.167;λ5=0.097.

[0116] in Therefore, the first two parameters after dimension reduction can represent the original five parameters of AC, CNL, DEN, GR, and RT.

[0117] Then establish the system of equations, the result is as follows:

[0118]

[0119] The non-zero solution of the equation system is obtained, and the result is x1=0.905; x2=0.893; x3=-0.904; x4=0.96; x5=-0.923; x6=-0.223; x7=0.383; x8=0.271; x9=-0.049; x 10 =-0.164.

[0120] Finally, the obtained non-zero solution is substituted into the expression of the dimension-reduced parameter shown in formula (5) to obtain the dimension-reduced parameter. The result is as follows:

[0121] F1=0.905AC+0.893CNL-0.904DEN+0.960GR-0.923RT (18)

[0122] F2=-0.223AC+0.383CNL+0.271DEN-0.049GR-0.164RT (19)

[0123] (4) performing a one-time fitting of the parameters after dimension reduction and the measured value of cement content of the mixed sedimentary rock to construct a one-time fitting prediction model of cement content of the mixed sedimentary rock;

[0124] In this embodiment, the prediction model of cement content in mixed rock obtained by performing a single fitting construction using the fitting formulas shown in equations (6) to (9) is:

[0125] Q1=-0.001F1+0.003F2+0.292 (20)

[0126] The cement content prediction model of the diamictite in formula (20) is used to predict the cement content of the diamictite in the measured diamictite sample. The results are shown in Table 6:

[0127] Table 6 Measured and predicted values ​​of cement content in mixed rock samples

[0128]

[0129]

[0130] It can be seen from Table 6 that the average relative error between the predicted value and the measured value of the mixed rock cement content prediction model of the present invention is 16.22%, which is less than 20%. The correlation between the predicted value and the measured value is as follows: Figure 1 As shown, from Figure 1It can be seen that there is an obvious linear relationship between the prediction model of cement content in mixed rock obtained by the first fitting of the present invention and the measured value, and the correlation R reaches 0.87818, which is a high correlation. It can predict the cement content in mixed rock more accurately.

[0131] It should be noted that, in this embodiment, the prediction model for the cement content of mixed rocks obtained by fitting the parameters after dimensionality reduction can accurately predict the cement content of mixed rocks. Then the prediction model for the cement content of mixed rocks obtained by directly fitting the logging parameters without dimensionality reduction can more accurately predict the cement content of mixed rocks. However, the prediction model without dimensionality reduction requires more parameters and is more complicated.

[0132] (5) performing a secondary fitting on the basis of the primary fitting to construct a secondary fitting prediction model for the cement content of the mixed sedimentary rock;

[0133] It can be seen from Table 6 that there is a certain error between the predicted value of cement content of the mixed rock cement content prediction model obtained by the first fitting and the measured value of cement content. In order to reduce this error, the prediction model of mixed rock cement content is constructed by the second fitting of the fitting formula shown in formulas (11)-(13):

[0134] Q2=0.568(-0.001F1+0.003F2+0.292)+0.077 (21)

[0135] The cement content prediction model of the diamictite in formula (21) is used to predict the cement content of the diamictite sample. The results are shown in Table 7:

[0136] Table 7 Measured values ​​of cement content in mixed sedimentary rocks, two predicted values ​​and relative error analysis results

[0137] sample Measured value Q <![CDATA[First predicted value Q1]]> <![CDATA[Relative error of Q1]]> <![CDATA[Second predicted value Q2]]> <![CDATA[Relative error of Q2]]> 1 17％ 16％ 5.88％ 17％ 0.00％ 2 16％ 11％ 31.25％ 14％ 12.50％ 3 16％ 13％ 18.75％ 15％ 6.25％ 4 15％ 12％ 20.00％ 15％ 0.00％ 5 15％ 13％ 13.33％ 15％ 0.00％ 6 14％ 12％ 14.29％ 15％ 7.14％ 7 21％ 26％ 23.81％ 22％ 4.76％ 8 17％ 18％ 5.88％ 18％ 5.88％ 9 14％ 13％ 7.14％ 15％ 7.14％ 11 19％ 21％ 10.53％ 20％ 5.26％ 12 17％ 14％ 17.65％ 16％ 5.88％ 13 20％ 21％ 5.00％ 20％ 0.00％ 14 21％ 22％ 4.76％ 20％ 4.76％ 15 10％ 6％ 40.00％ 11％ 10.00％ 16 10％ 8％ 20.00％ 12％ 20.00％ 17 22％ 24％ 9.09％ 21％ 4.55％ 18 15％ 13％ 13.33％ 15％ 0.00％ 20 16％ 11％ 31.25％ 14％ 12.50％

[0138] It can be seen from Table 7 that the error between the predicted value of cement content of mixed rock cement content prediction model obtained by quadratic fitting and the measured value of cement content is smaller, with an average error of only 5.92%, which meets the general work needs.

[0139] In order to further verify the accuracy of the prediction model of cement content in mixed sedimentary rocks of the present invention, another 8 groups of mixed sedimentary rock samples to be tested are used for model verification. The logging parameters of the mixed sedimentary rock samples to be tested and the measured values ​​of cement content in mixed sedimentary rocks are shown in Table 8:

[0140] Table 8 Logging parameters of the mixed rock samples to be tested and the measured values ​​of cement content in the mixed rock

[0141]

[0142]

[0143] The results of the first fitting prediction value and the second fitting prediction value of the mixed rock sample to be tested are shown in Table 9:

[0144] Table 9 Predicted values ​​of cement content in the mixed rock samples to be tested

[0145] sample Measured value Q <![CDATA[The first predicted value Q1]]> <![CDATA[Relative error of Q1]]> <![CDATA[Second predicted value Q2]]> <![CDATA[Relative error of Q2]]> D1 16％ 13％ 18.75％ 15％ 5.73％ D2 23％ 24％ 4.35％ 21％ 7.25％ D3 22％ 22％ 0.00％ 20％ 8.20％ D4 22％ 21％ 4.55％ 20％ 10.78％ D5 13％ 9％ 30.77％ 13％ 1.45％ D6 15％ 11％ 26.67％ 14％ 7.01％ D7 19％ 19％ 0.00％ 18％ 2.67％ D8 14％ 11％ 21.43％ 14％ 0.37％

[0146] It can be seen from Table 9 that the average relative error between the predicted value and the measured value predicted by the model after quadratic fitting is only 5.43%, indicating that the prediction accuracy of the present invention is relatively high. The cement content prediction model for mixed sedimentary rocks proposed in the present invention can be applied to the prediction of cement content in mixed sedimentary rocks.

[0147] In summary, the present invention can accurately predict the cement content of the diamictite in non-coring wells by establishing a diamictite cement content prediction model. Compared with the prior art, the present invention has significant progress.

[0148] The above description is only a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A quantitative prediction method for cement content of carbonate and terrigenous clastic mixed rocks, characterized in that: The following steps are involved: S1: obtaining the logging parameters of the stratum depth where the measured mixed rock sample is located, wherein the measured mixed rock sample has been observed by a rock thin section microscope to obtain the measured value of the mixed rock cement content; S2: Fitting the logging parameters with the measured values ​​of cement content in the mixed sedimentary rock to construct a prediction model for cement content in the mixed sedimentary rock; S3: obtaining the logging parameters of the stratum depth where the mixed rock sample to be tested is located, and predicting the mixed rock cement content of the mixed rock sample to be tested in combination with the mixed rock cement content prediction model.

2. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 1, characterized in that: In step S1, the logging parameters include acoustic wave time difference, compensated neutron, compensated density, natural gamma and resistivity.

3. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 1, characterized in that: Step S1 or step S1 and step S3 further include a step of preprocessing the logging parameters to remove abnormal values ​​in the logging parameters.

4. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 3, characterized in that: The preprocessing specifically includes the following sub-steps: The average value of the original data of each logging parameter is obtained respectively, and the deviation and probability error between the original data of the logging parameter and the average value are calculated by the following formula: Where: D is the deviation; x i is the value of the original data of the i-th logging parameter; is the average value of the original data of the logging parameters; p is the probability error; n is the total number of original data of each logging parameter; The D / p value of each logging parameter is calculated respectively. If the D / p value is greater than the D / p standard value, the corresponding logging parameter is an abnormal value.

5. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 4, characterized in that: The D / p standard value is obtained by the following standard: Among n original data, the deviation values ​​of all original data are obtained. If the deviation value of a certain original data is D1, when the probability of occurrence of all deviations equal to or greater than D1 is less than 1 / (2n), then the original data is an abnormal value.

6. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 2, characterized in that: Step S2 also includes the step of reducing the dimension of the logging parameters to obtain the parameters after dimension reduction; when fitting, the parameters after dimension reduction are fitted with the measured values ​​of the cement content of the mixed rock.

7. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 6, characterized in that: The dimension reduction of the logging parameters specifically includes the following sub-steps: The raw data of the logging parameters are standardized using the following formula to obtain a standardized data matrix: Where: x′ ij is the standardized x ij ;x ij is the parameter of row i and column j; is the average value of all parameters in the jth column; n is the total number of original data of each logging parameter; The correlation coefficient is calculated according to the standardized data matrix to obtain a correlation coefficient matrix; the correlation coefficient is calculated by the following formula: Where: R ij is the correlation coefficient; m is the total number of rows / columns of the standardized data matrix; k represents the kth row / column of the standardized data matrix; x ik is the parameter of the i-th row and k-th column in the standardized data matrix; is the average value of all parameters in the i-th row of the standardized data matrix; x jk is the parameter of the jth column and the kth row in the standardized data matrix; is the average value of all parameters in the jth column of the standardized data matrix; Establish a feature matrix |R-λE|=0, and obtain the eigenvalue λ of the feature matrix, cumulatively calculate the sum of the eigenvalues ​​from large to small, select the parameter whose sum of eigenvalues ​​is greater than the eigenvalue threshold as the parameter after dimensionality reduction, and set the expression of the parameter after dimensionality reduction to: F i =x i AC+x i+1 CNL+x i+2 DEN+x i+3 GR+x i+4 RT (i=1、2、3……) (5) Where: F i is the i-th dimension reduction parameter; x i 、x i+1 、x i+2 、x i+3 、x i+4 is the corresponding eigenvalue λ i The characteristic vector of; AC is the acoustic time difference; CNL is the compensation neutron; DEN is the compensation density; GR is the natural gamma; RT is the resistivity; Establish the equation system (R-λE)X=0, and find the non-zero solution of the equation system. The non-zero solution obtained is the corresponding eigenvalue λ i The eigenvector x i 、x i+1 、x i+2 、x i+3 、x i+4 , substitute the feature vector into the expression of the reduced dimension parameter to obtain the reduced dimension parameter.

8. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 7, characterized in that: The eigenvalue threshold is 85% of the total eigenvalues.

9. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 7, characterized in that: When there are only two parameters after dimension reduction and only one fitting is performed, the formula for one fitting is: Y=aX1+bX2+c (6) In the formula: Y is the dependent variable parameter, dimensionless; a, b, c are all fitting coefficients, dimensionless; X1, X2 are independent variable parameters, dimensionless; Y i is the i-th data of the dependent variable parameter; X 1i , X 2i They are the i-th data of the first independent variable parameter and the second independent variable parameter respectively; are the average values ​​of the dependent variable parameter, the first independent variable parameter, and the second independent variable parameter respectively; The prediction model of cement content of the mixed rock obtained by one fitting is: Q1=aF1+bF2+c (10) Where: Q1 is the predicted value of cement content of mixed sedimentary rock after a first fitting, dimensionless; F1 and F2 are the parameters after dimensionality reduction, dimensionless.

10. The method for quantitatively predicting cement content of carbonate and terrigenous clastic mixed rocks according to claim 9, characterized in that: When there are only two parameters after dimension reduction, and a second fitting is performed on the basis of the first fitting, the formula for the second fitting is: Y=d+eX (11) Where: e and d are both fitting coefficients, dimensionless; X i is the i-th data of the independent variable parameter; is the mean value of the independent variable parameter; The prediction model of cement content of the mixed rock obtained by quadratic fitting is: Q2=e(aF1+bF2+c)+d (14) Where: Q2 is the predicted value of cement content of mixed sedimentary rock by quadratic fitting, dimensionless.

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