CN111914209A - Drainage gas production effect fuzzy comprehensive evaluation method based on entropy method - Google Patents

Drainage gas production effect fuzzy comprehensive evaluation method based on entropy method Download PDF

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CN111914209A
CN111914209A CN202010399587.3A CN202010399587A CN111914209A CN 111914209 A CN111914209 A CN 111914209A CN 202010399587 A CN202010399587 A CN 202010399587A CN 111914209 A CN111914209 A CN 111914209A
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杜敬国
李泽坤
李嘉林
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Zhuojia Technology Tangshan Co ltd
North China University of Science and Technology
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Abstract

The invention discloses a fuzzy comprehensive evaluation method for drainage gas production effect based on an entropy method, which comprises the steps of selecting indexes for evaluating the drainage gas production effect of foam aiming at the characteristics of a foam drainage gas production well, constructing a foam drainage gas production effect evaluation index system according to the principle of combining qualitative and quantitative methods, considering the mutual relation among the drainage gas production effect evaluation indexes of the gas well, establishing a membership matrix by adopting a linear analysis method, determining index weight by adopting the entropy method, and calculating the comprehensive evaluation index for the drainage gas production effect of the gas well by combining the fuzzy comprehensive evaluation method.

Description

Drainage gas production effect fuzzy comprehensive evaluation method based on entropy method
Technical Field
The invention relates to the technical field of drainage gas production effect evaluation, in particular to a drainage gas production effect fuzzy comprehensive evaluation method based on an entropy method.
Background
The drainage gas production is an important measure for solving the problem of excessive accumulated liquid or water production of a gas well shaft and a stratum near the bottom of the gas well and recovering the normal production of the gas well, and is used for improving the production efficiency in the production process of the gas well. The foam drainage gas production has the advantages of simple equipment, easy construction, quick response, low cost and no influence on gas well production, and is widely applied to the global drainage gas production operation production, so that the systematic evaluation of the foam drainage gas production effect is very important.
However, the analysis of factors influencing the effect of the foam drainage gas production process is more, so that a complete evaluation system which is generally accepted by the industry does not exist in the field of foam drainage gas production, and the foam drainage effect of effluent gas wells of different gas fields cannot be contrastingly evaluated, so that a drainage gas production comprehensive evaluation method based on an entropy method and fuzzy comprehensive evaluation is provided based on the production technology management needs in the field of gas well gas production.
Disclosure of Invention
The invention aims to provide a drainage gas production effect fuzzy comprehensive evaluation method based on an entropy method aiming at the technical defects in the prior art.
The technical scheme adopted for realizing the purpose of the invention is as follows:
a drainage gas production effect fuzzy comprehensive evaluation method based on an entropy method comprises the following steps:
step 1, selecting n evaluation indexes delta u influencing the drainage and gas production effectsi，i＝1，2，…，n；
Step 2, corresponding to the relation between the comprehensive evaluation index D of the drainage gas production effect and the evaluation level, setting the comprehensive evaluation index D as m value intervals, vjThe median of the interval, j is 1, 2, …, m, from which the evaluation set V is determined1,v2, v3,…vm}；
Step 3, establishing a membership matrix R by adopting a linear analysis method:
first, each evaluation index is divided into m evaluation sections, and a level limit value a corresponding to the evaluation section of each evaluation index is setj，j＝1，2，…，m；
Then, an evaluation interval for each evaluation index is calculatedDegree of membership to a panel
Figure BDA0002488920730000041
Further, a matrix formed by membership degrees, namely a fuzzy relation matrix R can be solved;
step 4, determining the weight W of each evaluation index by adopting an entropy method:
first, the membership degree calculated in step 3 is calculated
Figure BDA0002488920730000021
Is converted into a relative value, such that
Figure BDA0002488920730000022
Then normalization processing is carried out to obtain tij；
Then, the ith influence factor t under the jth evaluation interval is calculatedijThe proportion of the evaluation interval and the entropy value of the jth evaluation interval; further calculating the weight of each evaluation index, and then obtaining the weight W of each evaluation index;
and 5, carrying out fuzzy synthesis on each evaluation index weight W obtained in the step 4 and the fuzzy relation matrix R obtained in the step 3 by using (+,) operators to obtain a final evaluation vector S, and calculating a comprehensive evaluation index D of the gas recovery effect of the gas well foam drainage by using the final evaluation vector S.
In the above-described embodiment, in the step 1, n is 4, and the evaluation indexes are daily gas generation rate Δ qgRate of change of daily output water Δ qwThe oil jacket differential pressure change rate delta P and the daily foam drainage gas production operation cost delta C.
In the above technical solution, in the step 2, m is 5, and the evaluation set V is { V ═ V1,v2,v3,v4,v5V, rating scale of good, better, general and poor1＝90,v2＝70,v3＝50,v4＝30,v5＝10。
In the above technical solution, in the step 3,
Figure BDA0002488920730000023
wherein, find
Figure BDA0002488920730000024
The constructed matrix yields a fuzzy relation matrix R.
In the above technical solution, in step 3, the normalization processing formula is as follows:
the forward direction index is as follows:
Figure BDA0002488920730000025
negative direction index:
Figure BDA0002488920730000031
then r'ijOr r ″)ijThe value of the j evaluation interval of the ith influencing factor (i is 1, 2, …, n; j is 1, 2, …, m), and the normalized data is recorded as tij。
In the above technical solution, in the step 4, t isijThe specific gravity of the evaluation interval
Figure BDA0002488920730000032
Entropy of j-th evaluation interval
Figure BDA0002488920730000033
In the above technical solution, in the step 4, the information entropy redundancy is calculated: dj＝l-ejThen calculating the weight of each index
Figure BDA0002488920730000034
Calculating to obtain a weight, determining the weight W: w ═ W1，w2，…，wn) Wherein the sum of the weights is 1.
In the above technical solution, in the step 5, S ═ isW·R＝(s1，s2，...，sm)。
In the above technical solution, in the step 5, D ═ S ═ V ═ S1*v1+s2*v2+…+sm*vm。
Compared with the prior art, the invention has the beneficial effects that:
1. when the weight is calculated, the defect that the weight of the principal component is calculated by using the variance contribution rate can be overcome by using an entropy method, the actually existing grade boundary ambiguity is solved, and the interference of artificial subjective factors is avoided. In addition, the method takes the discrete degree of the data into consideration, and the index weight can be adjusted according to the influence degree of the index on the comprehensive evaluation.
2. From the perspective that the evaluation result reflects the number of problems, the model combining the two methods can obtain a new index with practical significance, can reflect the main index of each principal component, and can sequence the comprehensive condition of each index, so that the complex problem is simplified, and more scientific, accurate and objective evaluation information is obtained.
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FIG. 1 is a flow chart of a fuzzy comprehensive evaluation method for drainage and gas production effects based on an entropy method, which is related by the invention;
FIG. 2 is a graph comparing gas production before and after the foam drainage gas production operation according to the present invention;
FIG. 3 is a comparison graph of water production before and after the foam drainage gas production operation according to the present invention;
FIG. 4 is a comparison graph of the differential pressure values of the oil jacket before and after the foam water drainage gas recovery operation according to the present invention;
FIG. 5 is a comprehensive evaluation index and an oil jacket differential pressure change rate curve chart of the foam drainage gas production operation based on the entropy method.
Detailed Description
The present invention will be described in further detail with reference to specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in the attached figure 1, the drainage gas production effect fuzzy comprehensive evaluation method based on the entropy method comprises the following steps:
step 1, selecting an evaluation index for evaluating the foam drainage gas production effect according to the characteristics of the foam drainage gas production well;
step 2, constructing a foam drainage gas production effect evaluation index system according to the principle of combining qualitative and quantitative methods:
setting m value intervals v corresponding to the relation between the comprehensive evaluation index and the evaluation leveljJ is 1, 2, …, m; from this, the evaluation set V ═ { V ═ V is determined1,v2,v3,…vm}；
And 3, considering the correlation among evaluation indexes of gas well drainage and gas production effects, and establishing a membership matrix by adopting a linear analysis method to obtain a fuzzy relation matrix R:
3.1, dividing each evaluation index into m evaluation sections, and setting a grade limit value a corresponding to the evaluation section of each evaluation indexj，j＝1，2，…，m；
3.2, calculating the membership degree of the evaluation interval of each evaluation index relative to the evaluation set
Figure BDA0002488920730000041
Further, a matrix formed by membership degrees, namely a fuzzy relation matrix R can be solved;
step 4, determining the weight of each evaluation index by adopting an entropy method:
4.1, calculating the membership degree obtained in the step 3
Figure BDA0002488920730000042
Is converted into a relative value, such that
Figure BDA0002488920730000043
Then normalization processing is carried out to obtain tij；
4.2, calculating the ith influence factor t in the jth evaluation intervalijThe proportion of the evaluation interval and the entropy value of the jth evaluation interval; further calculating the weight of each evaluation index to obtainEach evaluation index weight W;
and 5, calculating a comprehensive evaluation index of the gas well foam drainage gas production effect by combining a fuzzy comprehensive evaluation method.
Example 2
Referring to a method flow chart shown in fig. 1, the drainage gas production effect fuzzy comprehensive evaluation method based on the entropy method comprises the following steps:
step 1, determining drainage gas production evaluation indexes, and selecting four important indexes influencing drainage gas production effects by analyzing various factors influencing foam drainage gas production effects and benefits: rate of change of solar gas production (Δ q)g) Rate of change of daily output water (Δ q)w) The oil jacket differential pressure change rate (Δ P) and the daily foam drainage gas production operation cost (Δ C) were used as evaluation indexes. And forming a fuzzy set U by the four evaluation indexes, and using the fuzzy set as a drainage and gas production effect evaluation target. The expression for U is as follows:
U＝{u1,u2,u3,u4}＝{Δqg,Δqw,ΔP,ΔC}.
Figure BDA0002488920730000051
Figure BDA0002488920730000052
Figure BDA0002488920730000053
Figure BDA0002488920730000054
step 2, determining an evaluation set V, setting the comprehensive evaluation index D as m value intervals in order to correspond to the relation between the comprehensive evaluation index and the evaluation level, and setting the comprehensive evaluation index D as VjIs the median of the interval, in this embodiment
V＝{v1,v2,v3,v4,v5Good, general, poor
That is, j is 1, 2, 3, 4, 5.
In order to correspond to the relationship between the comprehensive evaluation index and the evaluation effect, the finally calculated comprehensive evaluation index is classified into the evaluation set, and the value range of the comprehensive evaluation index set in this embodiment is shown in table 1 below:
TABLE 1 value intervals
Figure BDA0002488920730000055
vjThe median of the value interval of the corresponding comprehensive evaluation index is obtained.
That is, the evaluation grade is good when D is 80 or more and less than 100, good when D is 60 or more and less than 80, good when D is 40 or more and less than 60, good when D is 20 or more and less than 40, and poor when D is 0 or more and less than 20.
And 3, in order to consider the correlation among the evaluation indexes of the gas well drainage and gas production effect evaluation, a linear analysis method is adopted to establish a membership matrix, in order to divide the membership of the evaluation indexes relative to the evaluation set, different levels of each evaluation index need to be divided into regions, and the specific division form is shown in table 2.
Table 2 evaluation intervals defined by evaluation indexes
Figure BDA0002488920730000061
For example, in table 2, the evaluation level is good when the daily gas production change rate is 0.5 or more, good when the daily gas production change rate is 0.2 or more and less than 0.5, good when the daily gas production change rate is 0.1 or more and less than 0.2, good when the daily gas production change rate is 0.05 or more and less than 0.1, general when the daily gas production change rate is 0.05 or more and less than 0.05, and poor when the daily gas production change rate is 0 or more and less than 0.05.
Then dividing the regions according to the evaluation indexes to calculate the membership degree, wherein the membership degree is calculated as follows:
if the evaluation index delta u is setiDividing m levels, setting aj(j ═ 1, 2.. once, m) is a level limit value corresponding to the evaluation index, and a calculation formula (i.e., a calculation formula of membership degree) of the evaluation index corresponding to the evaluation level is as follows:
Figure BDA0002488920730000062
according to Δ uiAnd aiSolving for the relation of (1) and the corresponding relation
Figure BDA0002488920730000063
The membership degree of each evaluation index to each evaluation interval is obtained, and further the membership degree of each evaluation index to each evaluation interval can be obtained
Figure BDA0002488920730000064
The constructed matrix is the fuzzy relation matrix R.
In this embodiment, if i is 1, 2, 3, 4, that is, n is 4, and each evaluation index is divided into 5 levels, that is, m is 5, then for u, u is divided into n and n is divided into n1(rate of change of daily gas production) of a1＝0.5，a2＝0.2，a3＝0.1，a4＝0.05，a5Calculating to obtain the result according to the calculation formula of the membership degree
Figure BDA0002488920730000065
Calculating u by the same principle2(rate of change of daily Water production), u3(rate of change of differential pressure in oil jacket), u4Obtaining a fuzzy relation matrix R according to the membership degree in the daily injection cost change rate as follows:
Figure BDA0002488920730000071
step 4, determining the weight of the influencing factors, and selecting n influencing factors (the book)In the example, four factors, i.e., n is 4) and m evaluation intervals (m is 5 in the present example) are selected, then
Figure BDA0002488920730000072
The value of the j-th evaluation interval for the i-th influencing factor (i 1, 2.., n; j 1, 2.., m).
Normalization processing of indexes: heterogeneous indexes are homogeneous, and because the measurement units of all indexes are not uniform, the standardization treatment is firstly carried out, namely, the absolute value of the indexes is converted into a relative value, namely, the order is carried out
Figure BDA0002488920730000073
Thereby solving the homogenization problem of various heterogeneous index values. The index normalization is as follows:
the forward direction index is as follows:
Figure BDA0002488920730000074
negative direction index:
Figure BDA0002488920730000075
then r'ijOr r ″)ijThe value of the j evaluation interval of the ith influencing factor (i 1, 2.. multidot.n; j 1, 2.. multidot.m) is normalized, and the normalized data is denoted as tij。
Calculating the ith influence factor (t) in the jth evaluation intervalij) Specific gravity in this evaluation interval:
Figure BDA0002488920730000076
where i 1., n, j 1.., m.
Calculating the entropy value of the j evaluation interval:
Figure BDA0002488920730000077
wherein k is 1/ln (n) and satisfies ej≥0。
Calculating the information entropy redundancy:
dj＝1-ej
calculating the weight of each index;
Figure BDA0002488920730000081
calculating to obtain a weight, determining the weight W:
W＝(w1，w2，…，wn)
wherein the sum of the weights is 1.
In this example
Figure BDA0002488920730000082
And 5, calculating a comprehensive evaluation index, and obtaining a final evaluation vector S by adopting (+,) operator fuzzy synthesis weight W and the matrix R:
S＝W·R＝(s1，s2，...，sm)
in the present embodiment, the first and second electrodes are,
Figure BDA0002488920730000083
s1＝0*0.30403643+0*0.53612817+0.073*0.15329247+0*0.00654293＝0.01119035031
s2＝0.14*0.30403643+0.14*0.53612817+0.927*0.15329247+0*0.00654293＝0.25972516369
s3＝0.86*0.30403643+0.86*0.53612817+0*0.15329247+0*0.00654293＝0.722541556
s4＝0*0.30403643+0*0.1378+0*0.53612817+0*0.15329247+0.47*0.00654293＝0.0030751771
s5＝0*0.30403643+0*0.1378+0*0.53612817+0*0.15329247+0.53*0.00654293＝0.0034677529
S＝(s1,s2,s3,s4,s5)
the comprehensive evaluation index calculation formula is as follows: d ═ S × V
From Table 2, D ═ S × V was calculated
D＝S*V＝s1*90+s2*70+s3*50+s4*30+s5*10＝55.4419036282
Example 3
The foam drainage effect evaluation is carried out on the gas well in a certain area of the oil and gas field in southwest of China by using the method in the embodiment 2, the foam drainage gas production process is carried out on the well from 4 to 18 days in 2012, the process implementation days are 126 days from 8 to 22 days in 2012, so that approximately equal amount of foam drainage free days are selected and compared with the days in 2011, 12 and 1 days in 2011 and 4 and 17 days in 2012. The dosage and period of the foam discharging agent are continuously adjusted according to field data in the process of field process implementation.
Data before and after foam drainage gas production operation are processed through Python, data visualization is achieved through a matplotlib tool library, and a gas production quantity comparison graph before and after foam drainage gas production operation in the attached figure 2, a water production quantity comparison graph before and after foam drainage gas production operation in the attached figure 3 and an oil jacket pressure difference comparison graph before and after foam drainage gas production operation in the attached figure 3 are made.
The data are integrated and combined with a calculation formula of the method, a comprehensive evaluation index based on an entropy method is calculated, the data of the differential pressure change rate of the oil jacket are integrated, data visualization is realized through a Matplotlib tool library based on Python, as shown in an attached figure 5, the relation between the comprehensive evaluation index and the differential pressure change rate of the oil jacket is analyzed, and therefore the drainage and gas production effects are better analyzed and evaluated.
As shown in fig. 2-4, with the continuous addition of the foam discharging agent, the oil jacket pressure difference is continuously reduced, the water yield and the gas yield start to gradually increase in 5, 16 and 16 days in 2012, and then relatively stable production is maintained, the oil jacket pressure difference is reduced to a minimum value interval of 14.56 and 14.78MPa in 6, 25 and 7, 10 days, and the accumulated liquid in the shaft is basically discharged, wherein the oil jacket pressure difference value is stable in an interval, the produced water in the formation is relatively stable, which indicates that at this time, the foam discharging water and gas production operation system reasonably achieves the purpose of discharging accumulated liquid at the bottom of the shaft to realize stable production.
From the production effect of a gas well, the gas yield and the water yield of the foam drainage gas production operation start to increase at 5, 16 and 2012, so that the construction operation purpose is achieved, but the comprehensive evaluation index of the foam drainage gas production operation based on the entropy method is only 40, and the method belongs to a stage with a common effect, because the cost of the daily drainage gas production operation is considered, good economic benefit is not achieved. When the change rate of the differential pressure of the oil jacket reaches 30%, the comprehensive evaluation index of the foam drainage gas production operation reaches more than 60, the construction operation effect reaches a good stage, and the method has relatively good economic benefit.
Compared with the traditional production parameter curve, the evaluation method provided by the invention has the advantages that the evaluation parameters of the foam drainage effect are more comprehensive and richer in performance on the basis of combining the drainage gas production operation cost, the performance of different operation effect stages is more obvious, and the design of foam drainage gas production and the optimization of a foaming agent are more facilitated.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A drainage gas production effect fuzzy comprehensive evaluation method based on an entropy method is characterized by comprising the following steps:
step 1, selecting n evaluation indexes delta u influencing the drainage and gas production effectsi，i＝1，2，…，n；
Step 2, corresponding to the relation between the comprehensive evaluation index D of the drainage gas production effect and the evaluation level, setting the comprehensive evaluation index D as m value intervals, vjThe median of the interval, j is 1, 2, …, m, from which the evaluation set V is determined1,v2,v3,…vm}；
Step 3, establishing a membership matrix R by adopting a linear analysis method:
first, each evaluation index is divided into m evaluation sections, and a level limit value a corresponding to the evaluation section of each evaluation index is setj，j＝1，2，…，m；
Then, the membership degree of the evaluation interval of each evaluation index relative to the evaluation set is calculated
Figure FDA0002488920720000013
Further, a matrix formed by membership degrees, namely a fuzzy relation matrix R can be solved;
step 4, determining the weight W of each evaluation index by adopting an entropy method:
first, the membership degree calculated in step 3 is calculated
Figure FDA0002488920720000012
Is converted into a relative value, such that
Figure FDA0002488920720000011
Then normalization processing is carried out to obtain tij；
Then, the ith influence factor t under the jth evaluation interval is calculatedijThe proportion of the evaluation interval and the entropy value of the jth evaluation interval; further calculating the weight of each evaluation index, and then obtaining the weight W of each evaluation index;
and 5, carrying out fuzzy synthesis on each evaluation index weight W obtained in the step 4 and the fuzzy relation matrix R obtained in the step 3 by using (+,) operators to obtain a final evaluation vector S, and calculating a comprehensive evaluation index D of the gas recovery effect of the gas well foam drainage by using the final evaluation vector S.
2. The drainage gas production effect fuzzy comprehensive evaluation method based on the entropy method is characterized in that n is 4 in the step 1, and m is 5 in the step 2.
3. The entropy method-based drainage gas production effect fuzzy comprehensive evaluation method, according to claim 2, wherein the evaluation indexes are daily gas production change rates Δ qg. Rate of change of daily output water Δ qwThe oil jacket differential pressure change rate delta P and the daily foam drainage gas production operation cost delta C.
4. The drainage gas production effect fuzzy comprehensive evaluation method based on the entropy method as claimed in claim 2, wherein the evaluation set V ═ { V ═ V1,v2,v3,v4,v5The evaluation grade is good, general and poor; v. of1＝90,v2＝70,v3＝50,v4＝30,v5＝10。
5. The drainage gas production effect fuzzy comprehensive evaluation method based on the entropy method as claimed in claim 1, wherein in the step 3,
Figure FDA0002488920720000021
wherein, find
Figure FDA0002488920720000022
The constructed matrix yields a fuzzy relation matrix R.
6. The drainage gas production effect fuzzy comprehensive evaluation method based on the entropy method as claimed in claim 1, wherein in the step 3, the normalization processing formula is as follows:
the forward direction index is as follows:
Figure FDA0002488920720000023
negative direction index:
Figure FDA0002488920720000024
then r'ijOr r ″)ijIs the ith influence factorThe value of the j-th evaluation interval of the element (i: 1, 2, …, n; j: 1, 2, …, m) is normalized and the normalized data is denoted as tij。
7. The drainage gas production effect fuzzy comprehensive evaluation method based on the entropy method as claimed in claim 6, wherein in the step 4, t isijThe specific gravity of the evaluation interval
Figure FDA0002488920720000025
Entropy of j-th evaluation interval
Figure FDA0002488920720000026
8. The drainage gas production effect fuzzy comprehensive evaluation method based on the entropy method as claimed in claim 7, wherein in the step 4, the information entropy redundancy is calculated: dj＝1-ejThen calculating the weight of each index
Figure FDA0002488920720000031
Calculating to obtain a weight, determining the weight W: w ═ W1，w2，…，wn) Wherein the sum of the weights is 1.
9. The method for fuzzy comprehensive evaluation of drainage gas production effect based on entropy method according to claim 8, wherein in the step 5, S-W-R (S)1，s2，...，sm)。
10. The entropy-method-based fuzzy comprehensive evaluation method for drainage gas production effects, according to claim 9, wherein in the step 5, D ═ S ═ V ═ S1*v1+s2*v2+…+sm*vm。
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CN115859223A (en) * 2023-02-27 2023-03-28 四川省计算机研究院 Multi-source data industry fusion analysis method and system
CN117391551A (en) * 2023-12-12 2024-01-12 天津朔程科技有限公司 Gas well drainage gas production evaluation method
CN117391551B (en) * 2023-12-12 2024-03-22 天津朔程科技有限公司 Gas well drainage gas production evaluation method

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