In view of the existing situation of oilfield development, one kind of method to evaluate the production performance of reservoir management units (RMUs) was presented in this paper. Among the commonly used indicators of oilfield development, select 12 indicators from the three aspects of production task, production technology, and reservoir development. According to the principle of fuzzy analytic hierarchy process (FAHP), this paper introduced one kind of new method to get the weights of indicators. By means of the method of TOPSIS, it is easy to obtain the rankings for all the RMUs through calculating the weighted Euclidean distance between each RMU and the positive or negative ideal RMU. Considering the gap between the differences in RMUs, the production performance appraisal ratings of RMUs are determined by fuzzy clustering. This evaluation method could constantly improve the management level of reservoir units and deepen the delicacy management of oilfield development.
The oilfield companies mostly take the management concept of “Benchmarking" during the process of oilfield development [
Through the analysis, the production performance evaluation indicators of RMU are divided into three aspects of production task, reservoir development, and production technology [
Hierarchical relationship of the evaluation indicators.
The production task [
The Reservoir development [
The production technology [
At present, with regard to determining the weights of evaluation indicators, the analytic hierarchy process (AHP) is a kind of relatively ideal method. While the traditional AHP needs to do the consistency test and constantly adjust the judgment matrix, some scholars put forward the fuzzy analytic hierarchy process (FAHP) [
Assume that
Assume
Firstly, we prove that
Secondly, we prove that
Finally, we prove that
Assume that when when
From the above analysis, the steps for determining the weights can be summarized in the following.
The expert gives out the fuzzy complementary judgment matrix
The quantity scale of
Scale value | Definition | Explanation |
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0.5 | Equally important | Two elements compared, equally important. |
0.6 | Slightly important | Two elements compared, an element is more slightly important than another element. |
0.7 | Obviously important | Two elements compared, an element is more obviously important than another element. |
0.8 | Much more important | Two elements compared, an element is much more strongly important than another element. |
0.9 | Extremely important | Two elements compared, an element is more extremely important than another element. |
0.1, 0.2, 0.3, 0.4 | Converse comparison | If the |
Check whether
In this section, we introduce the method of TOPSIS [
Assume that there are
when the when the when the
Considering the gap between the differences in RMUs, the comprehensive ranking still is not enough. It is necessary to classify the RMUs with fuzzy clustering. Therefore, further we work out the distance matrix of RMUs, denoted by
In order to reasonably determine the number of classification, we introduce a kind of
We can calculate the values of
The statistical data of 12 reservoir management units (RMUs) of an oilfield in the year of 2011 are listed in Table
The statistical data of 12 RMUs in 2011.
RMU |
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1 | 99.8 | 96.3 | −25.8 | −30.3 | −64.2 | 89.3 | 92.4 | 98.5 | 91.5 | 96.1 | 108.4 | 77.6 |
2 | 100.0 | 98.7 | 7.4 | 7.9 | 6.2 | 91.0 | 90.2 | 98.2 | 92.1 | 96.4 | 103.8 | 80.0 |
3 | 99.7 | 96.5 | 10.5 | 15.9 | −1.3 | 92.7 | 93.0 | 98.4 | 93.2 | 95.8 | 97.7 | 82.5 |
4 | 100.3 | 99.0 | 11.1 | 17.0 | 66.2 | 95.3 | 95.2 | 99.0 | 93.9 | 96.7 | 106.7 | 84.8 |
5 | 99.9 | 96.7 | 3.5 | 20.2 | 28.8 | 92.2 | 89.8 | 98.1 | 92.6 | 93.4 | 98.6 | 81.4 |
6 | 99.3 | 97.4 | 1.1 | 2.5 | 20.9 | 87.2 | 92.1 | 98.1 | 91.9 | 96.5 | 96.5 | 76.5 |
7 | 99.5 | 98.2 | 6.0 | 8.5 | 76.0 | 90.7 | 90.5 | 98.4 | 89.4 | 97.0 | 97.4 | 79.3 |
8 | 100.1 | 99.1 | −19.9 | −22.3 | −15.8 | 82.0 | 90.8 | 98.3 | 95.0 | 91.9 | 104.9 | 75.7 |
9 | 100.0 | 97.9 | 20.4 | 21.3 | 37.6 | 92.5 | 74.1 | 99.0 | 90.5 | 95.6 | 96.4 | 74.2 |
10 | 100.0 | 96.6 | 0.7 | 1.2 | 45.9 | 90.4 | 88.5 | 99.0 | 88.3 | 95.3 | 105.6 | 72.2 |
11 | 99.8 | 97.2 | 12.5 | 13.6 | 18.7 | 89.1 | 98.3 | 98.7 | 89.4 | 96.3 | 102.1 | 68.8 |
12 | 99.6 | 97.0 | 52.4 | 79.7 | 64.6 | 86.3 | 69.8 | 98.7 | 88.0 | 86.6 | 106.3 | 69.2 |
According to the basic data in Table
Standardize the above decision data. The 12 indicators are all the benefit type; their standardized decision data are shown in Table
The standardized decision data.
RMU |
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1 | 0.5000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.5489 | 0.7930 | 0.4444 | 0.5000 | 0.9135 | 1.0000 | 0.5500 |
2 | 0.7000 | 0.8571 | 0.4246 | 0.3473 | 0.5021 | 0.6767 | 0.7158 | 0.1111 | 0.5857 | 0.9423 | 0.6167 | 0.7000 |
3 | 0.4000 | 0.0714 | 0.4642 | 0.4200 | 0.4486 | 0.8045 | 0.8140 | 0.3333 | 0.7429 | 0.8846 | 0.1083 | 0.8563 |
4 | 1.0000 | 0.9643 | 0.4719 | 0.4300 | 0.9301 | 1.0000 | 0.8912 | 1.0000 | 0.8429 | 0.9712 | 0.8583 | 1.0000 |
5 | 0.6000 | 0.1429 | 0.3747 | 0.4591 | 0.6633 | 0.7669 | 0.7018 | 0.0000 | 0.6571 | 0.6539 | 0.1833 | 0.7875 |
6 | 0.0000 | 0.3929 | 0.3440 | 0.2982 | 0.6070 | 0.3910 | 0.7825 | 0.0000 | 0.5571 | 0.9519 | 0.0083 | 0.4813 |
7 | 0.2000 | 0.6786 | 0.4067 | 0.3527 | 1.0000 | 0.6541 | 0.7263 | 0.3333 | 0.2000 | 1.0000 | 0.0833 | 0.6563 |
8 | 0.8000 | 1.0000 | 0.0754 | 0.0727 | 0.3452 | 0.0000 | 0.7368 | 0.2222 | 1.0000 | 0.5096 | 0.7083 | 0.4313 |
9 | 0.7000 | 0.5714 | 0.5908 | 0.4691 | 0.7261 | 0.7895 | 0.1509 | 1.0000 | 0.3571 | 0.8654 | 0.0000 | 0.3375 |
10 | 0.7000 | 0.1071 | 0.3389 | 0.2864 | 0.7853 | 0.6316 | 0.6561 | 1.0000 | 0.0429 | 0.8365 | 0.7667 | 0.2125 |
11 | 0.5000 | 0.3214 | 0.4898 | 0.3991 | 0.5913 | 0.5338 | 1.0000 | 0.6667 | 0.2000 | 0.9327 | 0.4750 | 0.0000 |
12 | 0.3000 | 0.2500 | 1.0000 | 1.0000 | 0.9187 | 0.3233 | 0.0000 | 0.6667 | 0.0000 | 0.0000 | 0.8250 | 0.0250 |
Get the judgment matrixes of all the hierarchies through expert scoring Table
Therefore, we can calculate the weights for the evaluation indicators shown in Table
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0.5 | 0.7 | 0.7 |
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0.3 | 0.5 | 0.5 |
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0.3 | 0.5 | 0.5 |
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0.5 | 0.5 |
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0.5 | 0.5 |
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0.5 | 0.5 | 0.5 | 0.6 |
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0.5 | 0.5 | 0.5 | 0.6 |
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0.5 | 0.5 | 0.5 | 0.6 |
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0.4 | 0.4 | 0.4 | 0.5 |
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0.5 | 0.5 | 0.6 | 0.7 | 0.9 | 0.5 |
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0.5 | 0.5 | 0.6 | 0.7 | 0.9 | 0.5 |
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0.4 | 0.4 | 0.5 | 0.6 | 0.8 | 0.4 |
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0.3 | 0.3 | 0.4 | 0.5 | 0.7 | 0.3 |
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0.1 | 0.1 | 0.2 | 0.3 | 0.5 | 0.1 |
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0.5 | 0.5 | 0.6 | 0.7 | 0.9 | 0.5 |
The weights of all the indicators.
Indicator | Weights of indicators | Subindicators | Weights of subindicators | Combination weights |
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0.4222 |
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0.5 | 0.2111 |
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0.5 | 0.2111 | ||
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0.2889 |
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0.2625 | 0.0758 |
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0.2625 | 0.0758 | ||
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0.2625 | 0.0758 | ||
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0.2125 | 0.0614 | ||
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0.2889 |
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0.2056 | 0.0594 |
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0.2056 | 0.0594 | ||
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0.1722 | 0.0498 | ||
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0.1388 | 0.0401 | ||
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0.0722 | 0.0209 | ||
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0.2056 | 0.0594 |
Calculate the relative closeness of every RMU (see Table
The relative closeness of 12 RMUs.
RMU |
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1 | 0.2762 | 0.1366 | 0.3309 |
2 | 0.1224 | 0.2559 | 0.6765 |
3 | 0.2495 | 0.1454 | 0.3683 |
4 | 0.0606 | 0.3325 | 0.8459 |
5 | 0.2223 | 0.1715 | 0.4355 |
6 | 0.2723 | 0.1264 | 0.3170 |
7 | 0.2054 | 0.1919 | 0.4829 |
8 | 0.1477 | 0.2823 | 0.6565 |
9 | 0.1448 | 0.2249 | 0.6083 |
10 | 0.2252 | 0.1874 | 0.4542 |
11 | 0.2065 | 0.1668 | 0.4468 |
12 | 0.2450 | 0.1594 | 0.3941 |
From Table
Determine the best classification rating. First of all, by (
The distance between any two RMUs.
RMU | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
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1 | 0.0000 | 0.0258 | 0.0097 | 0.0465 | 0.0117 | 0.0012 | 0.0154 | 0.0214 | 0.0221 | 0.0126 | 0.0122 | 0.0073 |
2 | 0.0258 | 0.0000 | 0.0161 | 0.0207 | 0.0141 | 0.0245 | 0.0104 | 0.0044 | 0.0037 | 0.0132 | 0.0136 | 0.0185 |
3 | 0.0097 | 0.0161 | 0.0000 | 0.0368 | 0.0021 | 0.0084 | 0.0058 | 0.0118 | 0.0125 | 0.0029 | 0.0026 | 0.0023 |
4 | 0.0465 | 0.0207 | 0.0368 | 0.0000 | 0.0347 | 0.0452 | 0.0311 | 0.0250 | 0.0244 | 0.0339 | 0.0342 | 0.0392 |
5 | 0.0117 | 0.0141 | 0.0021 | 0.0347 | 0.0000 | 0.0105 | 0.0037 | 0.0097 | 0.0104 | 0.0008 | 0.0005 | 0.0044 |
6 | 0.0012 | 0.0245 | 0.0084 | 0.0452 | 0.0105 | 0.0000 | 0.0142 | 0.0202 | 0.0209 | 0.0113 | 0.0110 | 0.0061 |
7 | 0.0154 | 0.0104 | 0.0058 | 0.0311 | 0.0037 | 0.0142 | 0.0000 | 0.0060 | 0.0067 | 0.0028 | 0.0032 | 0.0081 |
8 | 0.0214 | 0.0044 | 0.0118 | 0.0250 | 0.0097 | 0.0202 | 0.0060 | 0.0000 | 0.0007 | 0.0089 | 0.0092 | 0.0141 |
9 | 0.0221 | 0.0037 | 0.0125 | 0.0244 | 0.0104 | 0.0209 | 0.0067 | 0.0007 | 0.0000 | 0.0095 | 0.0099 | 0.0148 |
10 | 0.0126 | 0.0132 | 0.0029 | 0.0339 | 0.0008 | 0.0113 | 0.0028 | 0.0089 | 0.0095 | 0.0000 | 0.0003 | 0.0053 |
11 | 0.0122 | 0.0136 | 0.0026 | 0.0342 | 0.0005 | 0.0110 | 0.0032 | 0.0092 | 0.0099 | 0.0003 | 0.0000 | 0.0049 |
12 | 0.0073 | 0.0185 | 0.0023 | 0.0392 | 0.0044 | 0.0061 | 0.0081 | 0.0141 | 0.0148 | 0.0053 | 0.0049 | 0.0000 |
Next, we can draw the dynamic fuzzy clustering figure (see Figure
The dynamic clustering figure.
Lastly, calculate the values of
The values of
Classification Number | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
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0.77 | 1.79 | 1.63 | 15.06 | 28.50 | 1.81 | 1.42 | 2.32 | 0.24 | 0.79 |
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4.96 | 4.26 | 4.07 | 4.12 | 4.39 | 4.95 | 6.09 | 8.85 | 19.39 | 241.88 |
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−4.19 | −2.47 | −2.44 | 10.94 | 24.12 | −3.14 | −4.67 | −6.52 | −19.14 | −241.09 |
From Table
Through analyzing the actual situation in the process of oilfield development, we first present some practically feasible evaluation indicators and their computing method in the second section. In order to reasonably decide the weight of each indicator, we introduce a kind of fuzzy AHP in Section
In order to make the development department of oilfield companies accurately and timely grasp the current situation of oilfield development and management of RMUs, it needs to establish a relatively perfect evaluation method to really respond to the management level, efficiency, and development effect of RMUs, promoting the delicacy management of oilfield development. As we know, by means of the relatively effective evaluation method to ascertain the appraisal rating of the RMUs, we cannot only know their production performance, but also it is helpful to motivate the enthusiasm of practical production for all the RMUs. By using the evaluation method proposed in this paper, the management level could be constantly improved, and the delicacy management of oilfield development can be deepened. And the oilfield companies continuously strengthen the digital construction, so it will support the accuracy and objectivity of the evaluation method.