CN110570102A - Reservoir evaluation method - Google Patents

Reservoir evaluation method Download PDF

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CN110570102A
CN110570102A CN201910774930.5A CN201910774930A CN110570102A CN 110570102 A CN110570102 A CN 110570102A CN 201910774930 A CN201910774930 A CN 201910774930A CN 110570102 A CN110570102 A CN 110570102A
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reservoir
evaluation
reservoir evaluation
values
characteristic
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张永华
张本书
肖学
李连生
章新文
罗家群
罗曦
严移胜
郑融
张磊
郭毅
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China Petroleum and Chemical Corp
China Petrochemical Corp
Exploration and Development Research Institute of Sinopec Henan Oilfield Branch Co
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Exploration and Development Research Institute of Sinopec Henan Oilfield Branch Co
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Abstract

The invention relates to the technical field of petroleum exploration and development, in particular to a reservoir evaluation method, which obtains reservoir evaluation parameters of reservoir samples in an area to be evaluated, analyzes the correlation among the reservoir evaluation parameters to obtain a correlation coefficient matrix, solves the matrix to obtain a plurality of characteristic values and characteristic vectors, and according to the principle of principal component analysis, the characteristic values and the characteristic vectors with higher occupation ratios have larger effects on the reservoir productivity evaluation, the characteristic values and the characteristic vectors with lower occupation ratios have smaller effects on the reservoir productivity evaluation, and certain interference can be caused on the reservoir productivity evaluation accuracy, so that useful characteristic vectors are reasonably selected for constructing a reservoir productivity evaluation model, the obtained reservoir productivity evaluation model is more accurate, the evaluation value obtained by using the model is more reasonable, the calculation process is simpler and more convenient, and the final evaluation result is more accurate, And is more reliable.

Description

Reservoir evaluation method
Technical Field
The invention relates to the technical field of petroleum exploration and development, in particular to a reservoir evaluation method.
Background
Many oil and gas exploration areas are low-porosity and low-permeability reservoirs, and have considerable geological reserves in development areas of some low-porosity and low-permeability reservoirs, but due to the complex geological conditions, the reservoir heterogeneity is severe, the porosity is low, the permeability is low, the scale of a high-quality reservoir is small, the transverse continuity of an effective reservoir is poor, and the single-well productivity is low, so that the high-efficiency exploration and development are difficult, and the reservoir needs to be finely predicted and evaluated to find the high-quality reservoir.
Conventionally, well logging data and seismic data are utilized to analyze and evaluate reservoirs in target intervals. The performance of the reservoir is reflected by different physical reservoir evaluation parameters, characteristic reservoir evaluation parameters such as energy storage parameters, permeability, shale content, reservoir quality indexes and the like are introduced, the weight of each reservoir evaluation parameter is calculated according to the correlation among the reservoir evaluation parameters, and a comprehensive evaluation prediction index system is constructed to evaluate the reservoir. However, the calculation method of the weight of each reservoir evaluation parameter is complex, and if the data of all the reservoir evaluation parameters are calculated, individual interference may occur, so that the weight calculation result is inaccurate, and the evaluation result of the reservoir is affected.
Disclosure of Invention
The invention aims to provide a reservoir evaluation method which is used for solving the problems of complex reservoir evaluation and low calculation accuracy of the conventional reservoir evaluation.
in order to achieve the above object, the present invention provides a reservoir evaluation method, comprising the steps of:
1) Obtaining reservoir evaluation parameters of each reservoir sample in a region to be evaluated, wherein the reservoir evaluation parameters comprise at least four of porosity, permeability, shale content, reservoir effective thickness, formation resistivity and quality index;
2) Constructing an original data matrix X consisting of reservoir and corresponding reservoir evaluation parameter values, and obtaining a standardized data matrix Z through standardization processing; calculating a correlation coefficient between any two reservoir evaluation parameter values according to the standardized data matrix to obtain a correlation coefficient matrix;
3) Determining each eigenvalue lambda of the matrix of correlation coefficientsmAnd a feature vector K corresponding to the feature valuemCalculating the ratio of each characteristic value in the total amount of the characteristic values, selecting at least one characteristic value from the ratio, and enabling the sum of the ratios of the selected characteristic values to be within a set ratio range to obtain a characteristic vector corresponding to the selected characteristic value;
4) Respectively cross-multiplying the eigenvectors corresponding to the selected eigenvalues by the standardized data matrix Z and summing to obtain a reservoir evaluation model; and obtaining evaluation values of corresponding reservoirs according to the reservoir evaluation model and the reservoir evaluation parameters of the reservoirs, and evaluating each reservoir in the to-be-evaluated area according to the evaluation values of the reservoirs and the classification standard of the reservoir evaluation.
The method has the advantages that a correlation coefficient matrix is obtained by analyzing the correlation among the reservoir evaluation parameters, the matrix is solved to obtain a plurality of characteristic values and characteristic vectors, according to the principle of principal component analysis, the characteristic values with higher occupation ratios and the characteristic vectors have larger effects on the reservoir productivity evaluation, the characteristic values with lower occupation ratios and the characteristic vectors have smaller effects on the reservoir productivity evaluation, and certain interference can be caused on the accuracy of the reservoir productivity evaluation, so that the useful characteristic vectors are reasonably selected for constructing the reservoir productivity evaluation model, the obtained reservoir productivity evaluation model is more accurate, the evaluation value obtained by the model is more reasonable, the calculation process is simpler and more convenient, and the final evaluation result is more accurate and more reliable.
Further, in order to obtain a more accurate evaluation result, the set ratio range is 65% to 85%. Because the influence of the characteristic value with small occupation ratio on the reservoir productivity evaluation model is small, the result is interfered to a certain extent by adding the characteristic value with small occupation ratio, and the calculated amount is increased, so that the residual characteristic values with small occupation ratio are removed.
Further, in order to accurately obtain the classification standard of the reservoir evaluation, the classification standard of the reservoir evaluation is obtained according to the reservoir evaluation obtained by drilling the well at the known well point and the evaluation value obtained by substituting the reservoir evaluation parameters at the known well point into the reservoir evaluation model.
further, for simple and accurate normalization, the normalization process employs a z-score normalization method.
Further, the calculation formula of the correlation coefficient is as follows:
In the formula, rjkIs a correlation coefficient, r, between a reservoir evaluation parameter j and a reservoir evaluation parameter kjk∈[-1,1]，Is the average value of the reservoir evaluation parameter j,Is the average value of the reservoir evaluation parameter k, and n is the reservoirNumber of samples, ZijNormalized values for the jth reservoir evaluation parameter for the ith reservoir sample.
drawings
FIG. 1 is a flow chart of a reservoir evaluation method of the present invention;
fig. 2 is a schematic view of a reservoir evaluation classification of a research area obtained by applying the reservoir evaluation method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention provides a reservoir evaluation method, as shown in figure 1, comprising the following steps:
1) and acquiring reservoir evaluation parameters of each reservoir sample in the area to be evaluated.
In this example, the reservoir evaluation parameters include porosity, permeability, shale content, reservoir effective thickness, formation resistivity, and quality index. As other embodiments, some of the parameters may be used as the reservoir evaluation parameters, and other parameters than these six parameters may be added as the reservoir evaluation parameters.
2) And constructing an original data matrix X consisting of reservoir samples and corresponding reservoir evaluation parameter values, and performing standardization processing to obtain a standardized data matrix Z.
Original data matrix, i.e. X ═ Xij)n×pWherein X isijThe j-th reservoir evaluation parameter value of each i-th reservoir sample is represented, i is 1, 2, …, and n, j is 1, 2, …, p.
Because the evaluation parameter dimensions of all reservoirs are not consistent, the X is standardized by adopting a z-score standardization method, and the average value of each reservoir evaluation parameter is obtainedAnd standard deviation Sjthe method comprises the following specific steps:
subtracting the average value of the evaluation parameters of each reservoir from the observed values of n sample data of n reservoirsThen divided by the standard deviation S of the reservoir evaluation parameterjThe normalized data conforms to the standard normal distribution, i.e. the mean value is 0, the standard deviation is 1, and the transformation function is:
In the formula: zija normalized value representing the jth reservoir evaluation parameter for the ith reservoir sample.
After Z-score normalization, the raw data matrix X is transformed into a normalized matrix Z as follows:
Z＝(Zij)n×p
Wherein i is 1, 2, …, n; j is 1, 2, …, p.
3) And calculating a correlation coefficient between any two reservoir evaluation parameter values according to the standardized data matrix to obtain a correlation coefficient matrix.
in one reservoir sample, the correlation coefficient between the jth reservoir evaluation parameter and the kth reservoir evaluation parameter is calculated by the following formula:
In the formula, rjkIs a correlation coefficient, r, between a reservoir evaluation parameter j and a reservoir evaluation parameter kjk∈[-1,1]，Is the average value of the reservoir evaluation parameter j,The average value of the reservoir evaluation parameter k is shown, and n is the number of reservoir samples.
And obtaining a correlation coefficient matrix R after correlation, wherein the matrix R comprises the following components:
R＝(rjk)p×p
wherein j is 1, 2, …, p; k is 1, 2, …, p; r isjkand the correlation coefficient of the jth reservoir evaluation parameter and the kth reservoir evaluation parameter is shown, and the closer the absolute value of r is to 1, the higher the correlation degree is.
3) Determining each eigenvalue lambda of the matrix of correlation coefficientsmand the number of key reservoir evaluation indexes.
Solving the eigenvalue lambda of the correlation coefficient matrix Rm(m ═ 1, 2, …, g, …, p), and each eigenvalue is ranked in order of magnitude, e.g., λ1≥λ2≥…≥λpCalculating the ratio of each characteristic value in the total quantity of the characteristic values from large to small in turn, wherein the calculation formula is as follows:
The number of the characteristic values is minimized, the sum of the cumulative proportions (d) is within 65-85%, and the number of the characteristic values at the moment is the number of the key reservoir stratum evaluation indexes. The calculation formula is as follows:
At this time, the corresponding characteristic value λ1，λ2，…，λgFor the selected eigenvalue, the corresponding eigenvector is the selected eigenvector Km. The set ratio range adopted in the present embodiment is 65% to 85%, and as another embodiment, the set ratio range may be determined according to actual conditions and different experiences.
4) Respectively cross-multiplying the eigenvectors corresponding to the selected eigenvalues by the standardized data matrix Z and summing to obtain a reservoir evaluation model; and obtaining evaluation values of corresponding reservoirs according to the reservoir evaluation model and the reservoir evaluation parameters of the reservoirs, and evaluating each reservoir in the to-be-evaluated area according to the evaluation values of the reservoirs and the classification standard of the reservoir evaluation.
According to the feature vectors corresponding to the g selected feature values, constructing a reservoir evaluation model as follows:
In the formula (I), the compound is shown in the specification,
Wherein z isiThe matrix formed by the reservoir evaluation parameters representing the ith reservoir, i.e. zi＝[z1,z2,...,zp]，K1representing a characteristic value λ1Corresponding feature vectors, i.e.
Obtaining a reservoir evaluation result by drilling according to the known well point, and substituting the reservoir evaluation parameters of the known well point into the reservoir evaluation model to obtain a reservoir evaluation value; combining the reservoir evaluation result with the reservoir evaluation value to give a classification standard of the reservoir evaluation; in the embodiment, the reservoir is divided into a type I reservoir, a type II reservoir and a type III reservoir, and each type of reservoir corresponds to a range of F values. In other embodiments, the classification may be performed by a conventional technique.
65 reservoir samples of 59 wells in the study area were collected, each reservoir sample having 6 reservoir evaluation parameters, as shown in Table 1. A65X 6-order reservoir property data matrix X is formed according to the data in the table 1.
TABLE 1
After normalization, as shown in table 2:
TABLE 2
a matrix of correlation coefficients, as shown in table 3:
TABLE 3
Quality index Thickness of porosity of Mud content Formation resistivity Permeability rate of penetration
quality index 1.000 0.392 0.817 -0.271 -0.297 0.929
Thickness of 0.392 1.000 0.121 -0.180 0.058 0.298
Porosity of 0.817 0.121 1.000 -0.324 -0.375 0.710
Mud content -0.271 -0.180 -0.324 1.000 -0.271 -0.125
Formation resistivity -0.297 0.058 -0.375 -0.271 1.000 -0.235
Permeability rate of penetration 0.929 0.298 0.710 -0.125 -0.235 1.000
The eigenvalues of the correlation coefficient matrix and their contribution rates and cumulative contribution rates are shown in table 4:
TABLE 4
As the proportion range is set to be 65% -85%, namely the accumulated contribution rate is 71.872%, the requirement can be met, and therefore, the characteristic vectors corresponding to the first two characteristic values are selected to construct the reservoir evaluation model.
Wherein f is1＝0.978*z1+0.406*z2+0.883*z3-0.325*z4-0.368*z5+0.907*z6，
f2＝0.011*z1+0.439*z2-0.099*z3-0.731*z4+0.779*z5-0.058*z6，
reservoir evaluation model is F-0.4942F1+0.22453*f2。
reservoir evaluation can be obtained by drilling according to the known well point, and then reservoir evaluation parameters of the known well point are substituted into the reservoir evaluation model to obtain a reservoir evaluation value; the classification standard of the reservoir evaluation can be given by combining the reservoir evaluation with the reservoir evaluation value; type i reservoir: cs is more than or equal to 0.52; type ii reservoir: cs is more than or equal to 0.34 and less than 0.52; a class III reservoir: cs is more than or equal to 0.25 and less than 0.34, wherein the Cs is a calculation result of the reservoir evaluation model F.
The reservoir of the destination layer is divided into different ranges on the plane according to the reservoir limit value interval, as shown in fig. 2. In fig. 2, the type i reservoir distribution area is the best high-quality reservoir development area, the type ii reservoir distribution area is the better high-quality reservoir development area, and the type iii reservoir distribution area is the worse high-quality reservoir development area. In fig. 2, the development area of the I-type high-quality reservoir is the area of efficient exploration and development.
The present invention has been described in relation to particular embodiments thereof, but the invention is not limited to the described embodiments. In the thought given by the present invention, the technical means in the above embodiments are changed, replaced, modified in a manner that is easily imaginable to those skilled in the art, and the functions are basically the same as the corresponding technical means in the present invention, and the purpose of the invention is basically the same, so that the technical scheme formed by fine tuning the above embodiments still falls into the protection scope of the present invention.

Claims (5)

1. A method for reservoir evaluation, comprising the steps of:
1) obtaining reservoir evaluation parameters of each reservoir sample in a region to be evaluated, wherein the reservoir evaluation parameters comprise at least four of porosity, permeability, shale content, reservoir effective thickness, formation resistivity and quality index;
2) Constructing an original data matrix X consisting of reservoir and corresponding reservoir evaluation parameter values, and obtaining a standardized data matrix Z through standardization processing; calculating a correlation coefficient between any two reservoir evaluation parameter values according to the standardized data matrix to obtain a correlation coefficient matrix;
3) Determining each eigenvalue lambda of the matrix of correlation coefficientsmand a feature vector K corresponding to the feature valuemCalculating the ratio of each characteristic value in the total amount of the characteristic values, selecting at least one characteristic value from the ratio to the minimum ratio, and making the sum of the ratios of the selected characteristic values in a set ratio range to obtain the selected ratioa feature vector corresponding to the feature value;
4) Respectively cross-multiplying the eigenvectors corresponding to the selected eigenvalues by the standardized data matrix Z and summing to obtain a reservoir evaluation model; and obtaining evaluation values of corresponding reservoirs according to the reservoir evaluation model and the reservoir evaluation parameters of the reservoirs, and evaluating each reservoir in the to-be-evaluated area according to the evaluation values of the reservoirs and the classification standard of the reservoir evaluation.
2. A reservoir evaluation method as defined in claim 1, wherein the set proportion ranges from 65% to 85%.
3. A reservoir evaluation method as defined in claim 1 or 2, wherein the classification criteria for reservoir evaluation are reservoir evaluation obtained by drilling a well at a known well point and evaluation values obtained by substituting reservoir evaluation parameters at a known well point into the reservoir evaluation model.
4. A reservoir evaluation method as defined in claim 1 or 2, wherein the normalization process employs a z-score normalization method.
5. A reservoir evaluation method as defined in claim 1 or 2, wherein the correlation coefficient is calculated by the formula:
In the formula, rjkis a correlation coefficient, r, between a reservoir evaluation parameter j and a reservoir evaluation parameter kjk∈[-1,1]，Is the average value of the reservoir evaluation parameter j,Is the average value of reservoir evaluation parameter k, n is the number of reservoir samples, Zijnormalized values for the jth reservoir evaluation parameter for the ith reservoir sample.
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