CN118171933A - A waterflooding development effect evaluation method combining TOPSIS method and RSR method - Google Patents

Abstract

The invention belongs to the field of oil and gas field development, and relates to a water injection development effect evaluation method combining a TOPSIS method and an RSR method. The method comprises the following steps: evaluating the oil reservoir type according to the water-flooding development effect, determining a proper evaluation index, and collecting water-flooding development data of a target oil reservoir; calculating the distance between each evaluation object and the maximum value and the distance between each evaluation object and the minimum value through a TOPSIS method, and sequencing the target oil reservoirs according to the normalized score; calculating the weighted rank sum ratio of each evaluation object by using an RSR method, and sequencing the target oil reservoirs according to the weighted rank sum ratio; calculating weights of the two methods by an entropy weight method, and carrying out weighted fuzzy combination on the two methods; and finally, obtaining a comprehensive evaluation result of the oil reservoir water flooding development effect. The evaluation method has the advantages of simple operation, objective information consideration, improved reliability of the evaluation result, and capability of rapidly, effectively and accurately evaluating the oil reservoir development effect and evaluating various different oil reservoir development effects.

Description

A waterflooding development effect evaluation method combining TOPSIS method and RSR method

Technical Field

The invention belongs to the field of oil and gas field development and relates to a water injection development effect evaluation method combining a TOPSIS method and a RSR method.

Background technique

Water injection development is a common development method for complex fault-block reservoirs, which can effectively improve the recovery rate of crude oil. In the process of oilfield water injection, development effect evaluation is an indispensable part. It can deeply study the potential tapping technology, find out the factors affecting the development effect of oilfields, analyze the existing technical problems, take corresponding measures in time, improve the development technology, and obtain better benefits. At present, there is no systematic and complete evaluation standard for the evaluation of water injection development effect. Therefore, the optimization of water injection development effect evaluation method is one of the key research objects in the field of oilfield water injection development in the future.

The evaluation methods for oil reservoir waterflooding development effects at home and abroad are divided into six categories: state comparison method, recoverable reserves evaluation method, comprehensive evaluation method, analogy method, numerical simulation evaluation method and application of fluid potential principle to study the total potential area of waterflooding oil development; among them, the comprehensive evaluation method is further divided into system dynamic analysis method, fuzzy comprehensive evaluation method, grey theory analysis method and application of fuzzy evaluation and unascertained measurement model to comprehensively evaluate its development effect.

After searching, Chinese patent number CN104794361A discloses a comprehensive evaluation method for the development effect of water-flooded oil reservoirs. Although the invention adopts a dynamic assignment method combining entropy weight method and hierarchical analysis method to calculate weights, the dynamic weight coefficient is determined according to the actual production status of the evaluated oil reservoir, so the difficulty of development effect evaluation operation and the subjectivity of weights are increased, thereby affecting the credibility of the evaluation results; the invention can only be used for the qualitative evaluation of the development effect of a single oil reservoir. Secondly, when using the fuzzy comprehensive evaluation method, a large number of evaluation indicators will result in super-fuzziness, poor resolution, and even failure of evaluation. To this end, we propose a comprehensive evaluation method for the development effect of oil reservoir water injection.

Summary of the invention

The content of the present invention is to fuzzily combine the TOPSIS method and the RSR method from the perspective of comprehensive evaluation method to obtain the evaluation of the effect of water injection development in complex fault-block reservoirs. The fuzzy combination of the TOPSIS method and the RSR method not only retains the advantages of the TOPSIS method being simple and flexible in application and the advantages of the RSR method being strong in comprehensive ability, but also overcomes the disadvantages of the TOPSIS method being easily affected by outliers when used alone or the disadvantage of the RSR method being easy to lose some information when used alone. In addition, the entropy weight method is used to allocate the weights of _Si and WRSR to obtain the final comprehensive evaluation results of reservoir water injection development, which can achieve more accurate and reasonable evaluation results of the effect of reservoir water injection development.

In order to achieve the above object, the present invention adopts the following technical solutions:

A comprehensive evaluation method for water drive reservoir development effect, the specific steps of the evaluation method are as follows:

(1) Evaluate reservoir types based on waterflooding effects, determine appropriate evaluation indicators, and collect waterflooding data for target reservoirs.

(2) The normalized original matrix is established by TOPSIS method, and the maximum value vector and the minimum value vector are calculated respectively. Then, the normalized results of the distance between each evaluation unit and the maximum value and the minimum value are obtained. Finally, the relative closeness of each evaluation unit to the maximum value is calculated and the advantages and disadvantages are evaluated accordingly.

(3) The original matrix is established by the RSR method to calculate the high-quality index and the low-quality index to obtain the RSR, and then the probability unit and regression equation are calculated to study the distribution of the RSR. Finally, the RSR value is used to rank the evaluation objects;

(4) Based on the above two calculation results, the weights of _Si and WRSR are calculated by the entropy weight method, and the two methods are weighted fuzzy combined to obtain a comprehensive evaluation value and sort them accordingly.

Furthermore, in step (1), appropriate evaluation indicators are determined:

The evaluation indicators were selected according to the SY/T6219-1996 oilfield development level classification document issued by China National Petroleum Corporation and the actual situation of the study area.

Furthermore, the calculation method for establishing the normalized original matrix in step (2) is as follows:

Therefore, the normalized matrix is obtained:

Where x is the original matrix, x _nm is the element of the nth row and mth column of the original matrix x, X is the matrix of the normalized original matrix x, _Xij is the element of the nth row and mth column of the normalized matrix X, Z is the matrix after the normalization of the normalized matrix X, and Zij is the element of the nth row and mth column of the normalized matrix Z.

Furthermore, the maximum value vector and the minimum value vector in step (2) are calculated as follows:

in, is the maximum _Zij value in the mth column,/> is the minimum _Zij value in the mth column, Z ⁺ is the maximum value vector, and ^Z- is the minimum value vector.

Furthermore, the calculation method of the distance between each evaluation object and the maximum value and the minimum value in step (2) is as follows:

in, is the maximum distance, /> is the minimum distance.

Furthermore, the method for calculating the relative closeness of each evaluation unit to the maximum value in step (2) is as follows:

Where: Si _is the relative closeness of each evaluation unit to the maximum value.

Furthermore, the calculation method of the high-quality index in step (3) is as follows:

Wherein, X _MAX is the maximum value among the p rating indicators of the f samples to be evaluated; X _MIN is the minimum value among the p rating indicators of the f samples to be evaluated.

Where i=1,2,…,f; j=1,2,…,p.

Furthermore, the calculation method of the low-quality index in step (3) is as follows:

Furthermore, the calculation method of the rank sum ratio in step (3) is as follows:

Furthermore, the calculation method of the downward cumulative frequency in step (3) is as follows:

in, is the average rank, average frequency/> It is obtained by sorting the RSR index from small to large and listing the frequency.

Furthermore, the weighted rank sum ratio in step (3) is calculated as follows:

WRSR＝a+b*Probit

Among them, Probit is the probability unit, and Probit is the conversion of the downward cumulative frequency into the required probability unit based on the standard normal distribution table.

Furthermore, in step (4), the entropy weight method is used to calculate the weights of _Si and WRSR. The specific calculation steps are as follows:

Ⅰ. Preprocessing of indicators:

First, determine the indicators and normalize them ( _Si and WRSR are both positive indicators, so the normalization in this paper is for positive indicators)

Among them, x _ij is the jth indicator of the ith sample, x _1j , x _2j , L, x _nj are the first, second, …, nth samples in the jth indicator, and _Xij is the normalized result of the jth indicator of the ith sample.

Ⅱ. Calculation of specific gravity:

Among them, _Pij is the proportion of the j-th indicator value of the i-th sample.

III. Entropy calculation:

k＝1/ln(2)

Where, e _j is the entropy value of the jth indicator; if p _ij = 0, then let

IV. Weight calculation:

Among them, _Wj is the weight of the j-th indicator.

Furthermore, the calculation method of the comprehensive evaluation value in step (4) is as follows:

Comprehensive evaluation value = _Si * _W1 + WRSR*(1- _W1 )

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a flow chart of the TOPSIS method.

FIG2 is a flow chart of the RSR method.

Figure 3 is a flow chart of the comprehensive evaluation method for reservoir waterflooding development effects.

Specific implementation steps

The present invention is described in further detail below in conjunction with the accompanying drawings.

Step 1: Identify the right metrics

Evaluate reservoir types based on waterflooding effects, determine appropriate evaluation indicators, and collect waterflooding development data for target reservoirs.

Step 2: Determine the relative closeness of each evaluation unit to the maximum value

The calculation process of TOPSIS method is shown in Figure 1.

The calculation method to establish the normalized original matrix is as follows:

Therefore, the normalized matrix is obtained:

The maximum and minimum vectors are calculated as follows:

The calculation method of the distance between each evaluation object and the maximum value and the minimum value is as follows:

The relative closeness of each evaluation unit to the maximum value is calculated as follows:

Step 3: Determine the weighted rank sum ratio of each evaluation unit

The calculation flow of the RSR method is shown in Figure 1.

The calculation method of high-quality index is as follows:

The calculation method of low-quality index is as follows:

The rank sum ratio is calculated as follows:

The downward cumulative frequency is calculated as follows:

The weighted rank sum ratio is calculated as follows:

WRSR＝a+b*Probit

Step 4: Weighted fuzzy union and sorting

The calculation method of the indicator preprocessing is as follows:

The specific gravity is calculated as follows:

The entropy value is calculated as follows:

k＝1/ln(2)

The weights are calculated as follows:

The calculation method of the comprehensive evaluation value is as follows:

Comprehensive evaluation value = _Si * _W1 + WRSR*(1- _W1 )

Example

Next, a certain oil field block is selected to evaluate the water injection development effect using the method of the present invention.

The development effect of 5 complex fault-block waterflooding reservoirs in the middle water-cut period in this oilfield was evaluated, and various factors affecting the water-flooding development decision of the oilfield were comprehensively analyzed. Seven indicators, including comprehensive decline rate, natural decline rate, water-flooding reserve utilization degree, water-flooding reserve control degree, water-cut rise rate, oil production rate and pressure maintenance level, were selected for comprehensive evaluation. The basic data of evaluation indicators of complex fault-block waterflooding reservoirs are shown in Table 1.

Table 1. Basic data table of evaluation indicators for complex fault-block water injection reservoirs

On the basis of the calculation of TOPSIS method and RSR method, the entropy weight method is used to calculate the objective weights of TOPISI and RSR, the results are calculated and ranked, and finally the comprehensive evaluation results are obtained.

(1) TOPSIS method

Due to the different nature of the indicators, some indicators need to be properly processed to make all indicators of the same nature. Commonly used processing methods include difference method and reciprocal method. Because the comprehensive decline rate and water content rise rate are relatively low-quality indicators, this paper uses the reciprocal method (1-X _i ) to convert low-quality indicators into high-quality indicators. After processing x _ij , the high-quality indicator matrix _Xij is obtained, and then the matrix Z is established after standardization.

Determine the maximum and minimum values based on the Z matrix and define the maximum value vector and minimum value vector respectively

Z ⁺ =(0.2804 0.1787 0.1885 0.2071 0.2113 0.0074 0.1968)

Z ^- =(0.1597 0.1278 0.1760 0.1781 0.1957 0.0014 0.1553)

The sorting results are shown in Table 2 by the Euclidean distance and relative proximity _Si of the maximum value vector Z ⁺ and the minimum value vector Z ^— :

Table 2. _Si and ranking table of each reservoir

Oil Reservoir D _i ⁺ D _i ^- S _i Sorting results 1 0.0538 0.0027 0.4910 2 2 0.0573 0.0019 0.4300 5 3 0.0443 0.0059 0.6349 1 4 0.0612 0.0027 0.4584 3 5 0.0593 0.0021 0.4337 4

(2) RSR method

The high-quality index and the low-quality index are known and the data matrix is obtained to calculate the rank value; then the rank sum ratio RSR can be obtained and the ranking results can be sorted to determine the distribution of RSR, as shown in Tables 3 and 4.

Table 3. RSR values and rankings of various reservoirs

Table 4. RSR distribution table

Oil Reservoir RSR f R Downward cumulative frequency Probit 1 0.5340 1.0000 1.0000 20.0000 4.1584 4 0.4131 2.0000 2.0000 40.0000 4.7467 5 0.4328 3.0000 3.0000 60.0000 5.2533 3 0.5223 4.0000 4.0000 80.0000 5.8416 2 0.4550 5.0000 5.0000 95.0000 6.6449

The regression equation obtained by fitting with the 2016 version of Excel is:

WRSR＝-0.0086*probit+0.5173

The WRSR values of the five reservoirs are 0.4815, 0.4602, 0.4671, 0.4765 and 0.4721, respectively.

(3) Weighted fuzzy join and sort

Calculate the weight set W by entropy weight method

W＝{0.7155,0.2845}

The results of fuzzy combination of TOPSIS method and RSR method are shown in Table 5.

Table 5. Fuzzy union

Oil Reservoir S _i WRSR Comprehensive value of evaluation index Sorting 1 0.491 0.4815 0.4883 Ⅱ 2 0.43 0.4602 0.4386 Ⅴ 3 0.6349 0.4671 0.5876 Ⅰ 4 0.4584 0.4765 0.4635 III 5 0.4337 0.4721 0.4446 IV

The present invention is based on a comprehensive evaluation of the water flooding development effect of complex fault block reservoirs. Combined with the actual development of the oil field, it can further analyze the development status and predict the focus of the next development technology direction, thereby achieving the purpose of efficient oil field water flooding development.

Claims (9)

1. A water flooding development effect evaluation method combining TOPSIS method and RSR method, characterized in that it comprises the following steps: Step 1: Evaluate reservoir types based on waterflooding effects, determine appropriate evaluation indicators, and collect waterflooding data for target reservoirs. Step 2: Use the TOPSIS method to establish the normalized original matrix and calculate the maximum value vector and the minimum value vector respectively. Then get the normalized results of the distance between each evaluation unit and the maximum value and the minimum value. Finally, calculate the relative closeness of each evaluation unit to the maximum value and evaluate the advantages and disadvantages accordingly. Step 3: Use the RSR method to establish the original matrix and calculate the high-quality index and the low-quality index to obtain the dimensionless statistic (RSR). Then calculate the probability unit and regression equation to study the distribution of RSR. Finally, the RSR value is used to rank the evaluation objects. Step 4: Based on the above two calculation results, the weights of _Si and WRSR are calculated by entropy weight method, and the two methods are weighted fuzzy combined to obtain the comprehensive evaluation value and sort accordingly. 2. The water injection development effect evaluation method combined with the TOPSIS method and the RSR method according to claim 2 is characterized in that the normalized matrix Z in step 2 is calculated using the following formula: Therefore, the normalized matrix is obtained: Where: x is the original matrix, x _nm is the element of the nth row and mth column of the original matrix x, X is the matrix of the forward transformation of the original matrix x, _Xij is the element of the nth row and mth column of the forward transformation matrix X, Z is the matrix after the normalization of the forward transformation matrix X, and Zij is the element of the nth row and mth column of the normalized matrix Z. Among them, i＝1,2,…,n; j＝1,2,…,m. 3. The water injection development effect evaluation method combined with the TOPSIS method and the RSR method according to claim 1 is characterized in that the maximum value vector and the minimum value vector are calculated in step 2 using the following formula: Where: is the maximum _Zij value in the mth column, /> is the minimum _Zij value in the mth column, Z ⁺ is the maximum value vector, and ^Z- is the minimum value vector. 4. The water injection development effect evaluation method combined with the TOPSIS method and the RSR method according to claim 1 is characterized in that the relative proximity between each evaluation unit and the maximum value is calculated in step 2 using the following formula: In the formula: is the maximum distance, /> is the minimum distance, and Si _is the relative proximity of each evaluation unit to the maximum value. 5. The water injection development effect evaluation method combined with the TOPSIS method and the RSR method according to claim 1 is characterized in that the high-quality index is calculated in step 3 using the following formula: Where: X is the original matrix, _Xfp is the element in the fth row and pth column of the original matrix x. Wherein: X _MAX is the maximum value among the p rating indicators of the f samples to be evaluated; X _MIN is the minimum value among the p rating indicators of the f samples to be evaluated. 6. The water injection development effect evaluation method combined with the TOPSIS method and the RSR method according to claim 1 is characterized in that the downward cumulative frequency is calculated in step 3 using the following formula: Where: is the average rank. The average frequency It is obtained by sorting the RSR index from small to large and listing the frequency. 7. The water injection development effect evaluation method combined with the TOPSIS method and the RSR method according to claim 1 is characterized in that the WRSR value is calculated in step 3 using the following formula: WRSR＝a+b*Probit Where: Probit is the probability unit. Among them, Probit converts the downward cumulative frequency into the required probability unit based on the percentage and probability unit comparison table. 8. The water injection development effect evaluation method combined with the TOPSIS method and the RSR method according to claim 1 is characterized in that the weights of _Si and WRSR are calculated by the entropy weight method in step 4, and the specific calculation steps are as follows: Ⅰ. Preprocessing of indicators: First, determine the indicators and normalize them (Si and WRSR are both positive indicators, so the normalization in this article is for positive indicators): In the formula: x _ij is the jth indicator of the ith sample, x _1j , x _2j , L, x _nj are the first, second, …, nth samples in the jth indicator, and _Xij is the normalized result of the jth indicator of the ith sample. Among them, i＝1,2,3,4,5; j＝1,2. Ⅱ. Calculation of specific gravity: Where: _Pij is the proportion of the j-th indicator value of the i-th sample. III. Entropy calculation: k＝1/ln(2) Where: _ej is the entropy value of the j-th indicator. If p _ij = 0, then let IV. Weight calculation: Where: _Wj is the weight of the j-th indicator. 9. The water flooding development effect evaluation method combining TOPSIS method and RSR method according to claim 1 is characterized in that in step 4, TOPSIS method and RSR method are weighted fuzzy combined, and the formula used is as follows: Comprehensive evaluation value = S _i * W ₁ + WRSR * (1-W ₁ ).