CN112330118B - Method for obtaining geological target evaluation parameter weight - Google Patents

Method for obtaining geological target evaluation parameter weight Download PDF

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CN112330118B
CN112330118B CN202011164988.7A CN202011164988A CN112330118B CN 112330118 B CN112330118 B CN 112330118B CN 202011164988 A CN202011164988 A CN 202011164988A CN 112330118 B CN112330118 B CN 112330118B
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李斌
赵星星
张鑫
梁宇
吉鑫
付修根
张欣玥
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Abstract

The invention discloses a new method for solving the weight of geological target evaluation parameters, which comprises the following steps: s1: determining a parameter characteristic value of geological target evaluation; s2: determining the expert weight of the evaluation parameter to obtain an expert experience scoring table; s3: constructing a parameter evaluation matrix; s4: constructing an expert constraint condition, and determining a mother sequence of the grey correlation matrix; s5: constructing a parameter evaluation matrix under expert constraint; s6: carrying out grey correlation analysis and evaluation index parameter normalization processing; s7: calculating a grey correlation coefficient; s8: calculating the grey correlation degree; s9: obtaining the weight of a target evaluation parameter; s10: and (4) evaluating the target. And (4) constructing constraint conditions of the evaluation parameters by using the expert experience values, further establishing a parameter matrix under the expert constraint, and solving the weight of the parameters. The method can objectively determine the parameter weight under the constraint of regional geological background, overcomes the direct human intervention of the conventional method, and further reasonably and effectively evaluates the favorable target to realize the optimization of the favorable exploration target.

Description

Method for solving geological target evaluation parameter weight
Technical Field
The invention relates to the technical field of oil and gas exploration, in particular to a method for solving the weight of geological target evaluation parameters.
Background
The quantitative evaluation of the beneficial targets is an important content of oil and gas exploration work, and is related to the optimization of geological targets and the smooth development of exploration and development work, so that the method has very important significance for effectively improving the result of the quantitative evaluation. At present, the main problem of the quantitative evaluation of the beneficial targets is that the weight of the evaluation parameters is determined without reasonable basis, which is mainly shown in the following steps: (1) when the expert scoring method is adopted, the method has the defects of strong subjectivity and uncertainty due to excessive dependence on experience and level of geologists; (2) when a pure mathematical method is adopted for calculation, the method adopts data algorithms such as correlation analysis or grey theory and the like to realize the quantification of weight calculation, but the method is greatly influenced by parameter values and the mathematical method, and the problem of overlarge parameter weight deviation under similar geological conditions often occurs.
Based on the above, the present invention designs a method for weight calculation of geological target evaluation parameters, so as to solve the above-mentioned problems.
Disclosure of Invention
The invention aims to provide a geological target evaluation parameter weight solving method, which constructs constraint conditions of evaluation parameters by utilizing geological target quantitative evaluation parameter scores, further establishes a parameter matrix under expert constraint and then solves the weight of the parameters. The method can objectively determine the parameter weight under the constraint of regional geological background, overcomes the direct human intervention of the conventional method, and further reasonably and effectively evaluates the favorable target to realize the optimization of the favorable exploration target.
In order to achieve the purpose, the invention provides the following technical scheme: a method for weight estimation of a geological target evaluation parameter, said method comprising the steps of:
s1: determining the parameter characteristic value of the geological target evaluation,
reading the characteristic value of each evaluation parameter by referring to the related geological map or test analysis data to obtain the characteristic value of the quantitative evaluation parameter of the geological target;
s2: determining expert weight of the evaluation parameters to obtain a geological target quantitative evaluation parameter score;
s3: constructing a parameter evaluation matrix and a parameter evaluation matrix,
determining the score of the geological target evaluation parameter by using a value assigning method;
s4: constructing expert constraint conditions, determining the mother sequence of the grey correlation matrix,
constructing a new expert constraint matrix, taking each evaluation parameter weight determined by the geological target quantitative evaluation parameter score as a mother sequence, and comparing the size of the average value of the mother sequence with the average value of each evaluation parameter, if the average value of the mother sequence is larger than the average value of the evaluation parameters, subtracting the difference value of the mother sequence and the subsequence by the geological target quantitative evaluation parameter score, thereby determining a new mother sequence; if the mother sequence average value is smaller than the evaluation parameter evaluation value, the mother sequence is not adjusted, and the like, the mother sequence average value is compared with the rest evaluation parameter average values one by one, and finally the mother sequence in the improved gray correlation matrix is determined;
s5: constructing a parameter evaluation matrix under the constraint of experts,
constructing a new parameter gray correlation matrix according to the mother sequence and the subsequence;
s6: carrying out grey correlation analysis and evaluation index parameter normalization processing;
s7: calculating a grey correlation coefficient;
s8: calculating the grey correlation degree;
s9: obtaining the weight of a target evaluation parameter;
s10: and (4) evaluating the target.
Preferably, the evaluation parameters include source rock thickness, hydrocarbon supply area coefficient, hydrocarbon supply conditions, reservoir type, distance to fracture, sedimentary facies, trap type, developmental background, cap lithology, cap thickness, distance to source rock, dredging conditions, and source fracture development.
Preferably, the specific step of S2 is:
and (3) constructing an evaluation parameter constraint weight according to an expert experience method, wherein the calculation formula is as follows:
Figure GDA0003684574020000031
wherein W is the quantitative evaluation parameter weight of the geological target, and WqFor the N-th geological target quantitative evaluation parameter score, S1, S2 and S3 are source rock conditions, R1, R2 and R3 are reservoir conditions, T1 and T2 are trap conditions, P1 and P2 are preservation conditions, and M1, M2 and M3 are migration conditions.
Preferably, in said S4,
in the expert constraint matrix, the geological target quantitative evaluation parameter score calculation formula is as follows:
Figure GDA0003684574020000032
constructing a new expert constraint matrix as follows:
Figure GDA0003684574020000033
the calculation formula of the mother sequence average value is as follows:
Wa＝(W1+W2+…+Wm)/m (4)；
the calculation formula of the average value of each evaluation parameter is as follows:
Figure GDA0003684574020000034
the calculation formula for comparing the sizes of the mother sequence average value and each evaluation parameter average value is as follows:
AV＝Wa-Xt (6)；
if the average value of the mother sequence is larger than the average value of the evaluation parameters, subtracting the difference value of the average value of the mother sequence and the average value of each evaluation parameter from the qualitative target quantitative evaluation parameter score, and determining a new mother sequence according to the following calculation formula:
If Wa＞Xt，Wz＝Wq-AV，
Wz＝{W1 W2 W3 … Wm} (7)；
if the average value of the mother sequence is smaller than the evaluation parameter evaluation value, the calculation formula of the mother sequence without adjustment is as follows:
If Wa＜Xt，Wz＝Wq，
Wz＝{W1 W2 W3 … Wm} (8)。
preferably, in S5, the subsequence is:
Figure GDA0003684574020000041
the new parameter gray correlation matrix is:
Figure GDA0003684574020000042
preferably, in S6, a min-max normalization method is selected to normalize the data, and the formula is as follows:
where max is the maximum value of the sample data, min is the minimum value of the sample data, and the value of the sequence is normalized to [0, 1 ].
Preferably, the specific step of S7 is:
calculating the normalized evaluation parameter data by a formula (12) to obtain a grey correlation coefficient of the trap evaluation parameter represented by each subsequence and the evaluation parameter weight obtained by a parent sequence expert scoring method,
Figure GDA0003684574020000043
wherein ξi(k) Expressing the correlation coefficient of the kth parameter of the ith subsequence and the kth parameter of the mother sequence, wherein rho is a resolution coefficient and the value range is [0, 1]]。
Preferably, in said S8,
the grey correlation is calculated by the formula:
Figure GDA0003684574020000051
wherein r isiAnd k represents the number of evaluation indexes, wherein the gray correlation degree between the subsequence and the parent sequence is shown.
Preferably, in S9, the calculation formula for obtaining the target evaluation parameter weight is as follows:
Figure GDA0003684574020000052
wherein k isiAnd weighting the evaluated parameters.
Preferably, in S10, the idea of calculating the target evaluation is to multiply the evaluation parameter by the weight of the parameter, and then add up the evaluation parameter to obtain the total score Z of the target evaluation, and the calculation formula is as follows:
Ks×(S1+S2+S3)+Kp×(P1+P2)+Kr×(R1+R2+R3)+Km×(M1+M2+M3)+Kt×(T1+T2)＝Z (15)。
compared with the prior art, the invention has the beneficial effects that: according to the method, the constraint conditions of the evaluation parameters are constructed by utilizing the geological target quantitative evaluation parameter scores, so that a parameter matrix under the constraint of experts is established, and then the weight of the parameters is obtained. The method can objectively determine the parameter weight under the constraint of regional geological background, overcomes the direct human intervention of the conventional method, and further reasonably and effectively evaluates the favorable target to realize the optimization of the favorable exploration target.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of determining geologic target evaluation parameter weights by combining gray correlation with expert scoring;
FIG. 2 is a flow chart of determining geological evaluation target weight through grey correlation under expert constraint conditions;
FIG. 3 is a diagram illustrating the evaluation and division of the Ordovician geological target in the central ridge of the Tarim basin according to the embodiment of the present invention;
FIG. 4 is a graph of a Tarim basin Jaltus group source rock distribution according to an embodiment of the present invention;
FIG. 5 is a diagram of an Ordovician hydrocarbon-containing system according to an embodiment of the present invention;
FIG. 6 is a graph showing the distribution of the Olympic Liangta groups in the central ridge of the Tarim basin according to the present invention;
FIG. 7 is a diagram of the fracture period of the central ridge of the Tarim pot according to an embodiment of the present invention;
FIG. 8 is a sectional view of a construction unit of a central raised zone of a Tarim pot according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a top surface structure trend of a cell group of a well-interior cell of a Tarim basin in accordance with an embodiment of the present invention;
FIG. 10 is a histogram of weights empirically determined by an expert in an embodiment of the present invention;
FIG. 11 is a gray correlation scattergram according to an embodiment of the present invention;
FIG. 12 is a weight chart of geological target evaluation parameters according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that: a method for weight estimation of a geological target evaluation parameter, said method comprising the steps of:
s1: determining the parameter characteristic value of the geological target evaluation,
and (3) reading the characteristic value of each evaluation parameter by referring to the related geological map or test analysis data, and obtaining the characteristic value of the geological target quantitative evaluation parameter (table 2) according to the reading value shown in table 1.
TABLE 1 geological target quantitative evaluation parameter eigenvalue reading basis
Figure GDA0003684574020000071
TABLE 2 statistic table of characteristic values of geological target evaluation parameters
Figure GDA0003684574020000081
Note that: s1-1, S1-2.., M3-x is a specific characteristic value of the geological target evaluation parameter.
S2: determining expert weights of the evaluation parameters to obtain a geological target quantitative evaluation parameter score (table 3);
and (3) constructing an evaluation parameter constraint weight according to an expert experience method, wherein the calculation formula is as follows:
Figure GDA0003684574020000082
wherein W is the quantitative evaluation parameter weight of the geological target, and WqFor the nth geological target quantitative evaluation parameter score, S1, S2 and S3 are source rock conditions, R1, R2 and R3 are reservoir conditions, T1, T2 and T3 are trap conditions, P1 and P2 are preservation conditions, and M1, M2 and M3 are migration conditions.
TABLE 3 geological target quantitative evaluation parameter scores
Figure GDA0003684574020000083
Figure GDA0003684574020000091
S3: constructing a parameter evaluation matrix and a parameter evaluation matrix,
determining the score of the geological target evaluation parameter (table 5) by using an assigning method (table 4);
TABLE 4 evaluation principle of quantitative target evaluation parameters
Probabilistic assignment Principle of assignment
1.0-0.75 Is likely to exist
0.75-0.5 May exist
0.5-0.25 Is not aware of whether or not there is
0.25-0 May not exist
TABLE 5 geological target evaluation parameter assignment score Table
Figure GDA0003684574020000101
And (3) noting that: [ S1-1], [ S1-2], [ P1-x ] and the like are scoring results of geological target quantitative evaluation parameters.
S4: constructing expert constraint conditions, determining the mother sequence of the grey correlation matrix,
in the traditional correlation analysis, the first row of indexes is automatically defaulted to be a grey correlated mother sequence, the evaluation indexes automatically calculate the correlation between a subsequence and the mother sequence, and the method can cause great system errors in calculation of the evaluation parameter weight. To eliminate this error, some scholars combine gray correlation with expert experience to determine the weights for evaluation goals. The flow is shown in FIG. 1. In fig. 1, k represents a certain evaluation parameter weight,
wqweight of evaluation parameter, w, determined on behalf of expert experiencejAnd (3) representing the evaluation parameter weight obtained by grey correlation, giving the geological meaning to gamma, assigning values according to actual requirements, and finally obtaining the evaluation parameter weight k determined by combining the grey correlation and expert experience. However, in actual work, the assignment of the parameter gamma is completely influenced by subjective factors of learners, and the obtained evaluation parameter weight has no objectivity in a strict sense. In order to improve the evaluation parameter weight calculation, the patent proposes the idea of constructing expert constraint conditions, the expert constraint conditions are blended into a parameter evaluation matrix, and then grey correlation analysis is carried out to calculate the evaluation parameter weight.
The process of the invention is as shown in figure 2, a new expert constraint matrix is constructed, each evaluation parameter weight determined by the geological target quantitative evaluation parameter score is used as a mother sequence, the size of the average value of the mother sequence and each evaluation parameter average value is compared, if the mother sequence average value is larger than the evaluation parameter average value, the difference value of the mother sequence and the subsequence is subtracted by the geological target quantitative evaluation parameter score, and thus a new mother sequence is determined; if the mother sequence average value is smaller than the evaluation parameter evaluation value, the mother sequence is not adjusted, and the like, the mother sequence average value is compared with the rest evaluation parameter average values one by one, and finally the mother sequence in the improved gray correlation matrix is determined;
in the expert constraint matrix, the geological target quantitative evaluation parameter score calculation formula is as follows:
Figure GDA0003684574020000111
constructing a new expert constraint matrix as follows:
Figure GDA0003684574020000112
the calculation formula of the mother sequence average value is as follows:
Wa＝(W1+W2+…+Wm)/m (4)；
the calculation formula of the average value of each evaluation parameter is as follows:
Figure GDA0003684574020000113
the calculation formula for comparing the sizes of the mother sequence average value and each evaluation parameter average value is as follows:
AV＝Wa-Xt (6)；
if the average value of the mother sequence is larger than the average value of the evaluation parameters, subtracting the difference value between the average value of the mother sequence and each average value of the evaluation parameters from the qualitative target quantitative evaluation parameter score, and determining a new mother sequence according to the following calculation formula:
If Wa＞Xt，Wz＝Wq-AV，
Wz＝{W1 W2 W3 … Wm} (7)；
if the average value of the mother sequence is smaller than the average value of the evaluation parameters, the calculation formula of the mother sequence without adjustment is as follows:
If Wa＜Xt，Wz＝Wq，
Wz＝{W1 W2 W3 … Wm} (8)。
s5: constructing a parameter evaluation matrix under the constraint of experts,
determination of mother sequences S4 already mentioned, in other words, the mother sequences in the gray correlation matrix under the expert constraint are the mother sequences finally determined by the expert experience of S4. The sub-sequences are ordered arrangements of data of sub-factors which influence the nature of the evaluated transactions to a certain extent. The subsequence is:
Figure GDA0003684574020000121
the new parameter gray correlation matrix is:
Figure GDA0003684574020000122
s6: carrying out grey correlation analysis and evaluation index parameter normalization processing;
since the elements analyzed by the gray correlation analysis are heterogeneous parameter indexes, and the data sizes are greatly different due to different dimensions, it is necessary to perform dimensionless processing or normalization processing on the data, so as to reduce the difference of the absolute values of the data. The invention selects a min-max standardization method to carry out normalization processing on data, and the formula is as follows:
Figure GDA0003684574020000123
wherein max is the maximum value of the sample data, min is the minimum value of the sample data, and the value of the sequence is normalized to [0, 1 ].
S7: calculating a grey correlation coefficient;
calculating the normalized evaluation parameter data by a formula (12) to obtain a grey correlation coefficient of the trap evaluation parameter represented by each subsequence calculation and the evaluation parameter weight obtained by a parent sequence expert scoring method,
Figure GDA0003684574020000131
wherein ξi(k) Representing the correlation coefficient of the kth parameter of the ith subsequence and the kth parameter of the mother sequence, wherein rho is a resolution coefficient and the value range is [0, 1%]The smaller the value of the weight value is, the more obvious the difference between the obtained correlation coefficients is, generally 0.5 is obtained, and the weight value is endowed with geological meaning, so that the weight value is finally obtained and changed to conform to geological rules.
S8: calculating the grey correlation degree;
the grey correlation is calculated by the formula:
Figure GDA0003684574020000132
wherein r isiAnd k represents the number of evaluation indexes, wherein the gray correlation degree between the subsequence and the parent sequence is shown.
S9: obtaining the weight of a target evaluation parameter;
the calculation formula for calculating the weight of the target evaluation parameter is as follows:
Figure GDA0003684574020000133
wherein k isiAnd (4) evaluating the parameter weight.
S10: the evaluation of the target is carried out,
the objective evaluation calculation idea is to multiply the evaluation parameters by the weight of the parameters, and then accumulate to obtain the total score Z value of the objective evaluation, and the calculation formula is as follows:
Ks×(S1+S2+S3)+Kp×(P1+P2)+Kr×(R1+R2+R3)+Km×(M1+M2+M3)+Kt×(T1+T2)＝Z (15)。
the score (Z value) for each target was found according to equation 15, see table 6.
TABLE 6 comprehensive scoring table for favorable target evaluation
Figure GDA0003684574020000141
And sorting according to the Z value, wherein the top-ranked target is a favorable preferred target.
Example 1
As shown in fig. 3, the evaluation of the beneficial objective of the ohto system was performed by taking a certain area of the central ridge of the Tarim basin as an example. And drawing up 11 evaluation targets, and preferably carrying out comprehensive evaluation on typical parameters of target units represented by serial numbers.
S1, reading the parameter characteristic value of the target evaluation unit
(1) Referring to fig. 4 and 5, parameter values such as the distribution thickness value of the hydrocarbon source rock, the hydrocarbon supply area coefficient, the hydrocarbon supply condition, and the like in each evaluation target region are read as evaluation bases of the hydrocarbon source rock conditions.
(2) Referring to fig. 6 and 7, the sedimentary facies types and distances from fractures of different evaluation targets are read as evaluation bases of reservoir conditions.
(3) Referring to fig. 7, the degree of fracture development and the number of fractures in each evaluation target region were read as the basis for evaluation of migration conditions.
(4) Referring to fig. 4 and 7, the distance of the source rock of the fracture data is read as the evaluation basis of the migration condition.
(5) Referring to fig. 8 and 9, the trap development background was read as the basis for evaluation of the trap condition.
(6) The thickness of a single-well direct cover layer (mudstone) in a reference area is read, and the average value of the cover layer is used as the basis for evaluating the storage condition; and establishing a geological target quantitative evaluation parameter characteristic value statistical table according to other data information, and referring to a table 7.
TABLE 7 statistical table of characteristic values of quantitative evaluation parameters of geological targets
Figure GDA0003684574020000151
Figure GDA0003684574020000161
Step 2: the expert experience method determines the weights of the quantitative evaluation parameters of the geological target, the scores are shown in the table 8, and the results are shown in the figure 10. Wherein the index 1 is (S1) source rock thickness, the index 2 is (S2) hydrocarbon supply area coefficient, the index 3 is (S3) hydrocarbon supply condition, the index 4 is (R1) reservoir type, the index 5 is (R2) distance to fracture, the index 6 is (R3) sedimentary facies belt, the index 7 is (T1) trap type, the index 8 is (T2) developmental background, the index 9 is (P1) cap lithology, the index 10 is (P2) cap thickness, the index 11 is (M1) distance to source rock, the index 12 is (M2) channeling condition, and the index 13 is (M3) through source fracture development.
TABLE 8 geological target quantitative evaluation parameter expert scoring sheet
Figure GDA0003684574020000171
And 3, step 3: and (4) determining the scores of the quantitative evaluation parameters of the geological target by a valuation method by referring to the evaluation criteria of the quantitative evaluation parameters of the geological target (Table 9) (Table 10).
TABLE 9 geological target evaluation criteria for Tarim basin carbonate rock
Figure GDA0003684574020000172
Figure GDA0003684574020000181
TABLE 10 geological target evaluation parameter scoring table
Figure GDA0003684574020000182
Step 4, constructing expert constraint conditions
Determining an expert constraint matrix according to equation (3),
Figure GDA0003684574020000191
the difference between the parent sequence and the average of the subsequence is compared according to the formula (4) and the formula (5), and the results are shown in the following table 11:
TABLE 11 statistical table of expert empirical mean values and various evaluation target parameters
Average value of expert experience 0.077
Object 1 0.923076923
Object 2 0.798461538
Target 3 0.673076923
Target 4 0.730769231
Target 5 0.663846154
Target 6 0.567692308
Target 7 0.519230769
Target 8 0.624615385
Target 9 0.634615385
Target 10 0.586923077
Target 11 0.576923077
The expert experience averages are found to be smaller than the respective evaluation averages by comparison, and S7 is performed, where the final parent sequence in the gray correlation matrix determined by the expert experience is
Wz＝{0.062 0.086 0.095 0.095 0.033 0.082 0.091 0.062 0.103 0.058 0.082 0.07 0.082}
Step 5, establishing a gray incidence matrix under geological constraint
And building a gray correlation matrix according to the determined weights scored by the experts.
Step 6, grey correlation analysis and evaluation index parameter normalization processing,
the data is normalized according to equation (11). The results are shown below.
00029 00053 00062 00062 0 00049 00058 00029 0007 00025 00049 00037
00717 00967 00967 00717 00717 00967 00967 00967 00967 00717 00967 00967
00217 00467 00967 00967 00467 00967 00967 00967 00967 00967 00717 00347
00217 00467 00717 00467 00217 00467 00467 00967 00967 00967 00967 00467
00717 00717 00467 00967 00217 00967 00967 00217 00967 00717 00717 00467
00217 00217 00467 00967 00217 00467 00967 00967 00967 00467 00967 00597
00217 00217 00717 00217 00217 00467 00967 00967 00967 00217 00717 00597
00467 00217 00717 00217 00467 00467 00217 00717 00467 00467 00467 00467
00467 00467 00717 00217 00467 00587 00717 00467 00967 00967 00217 00467
00217 00467 00467 00467 00217 00967 00967 00467 00967 00967 00717 00467
00217 00467 00217 00217 00967 00967 00717 00217 00967 00217 00717 00597
00217 00217 00217 00717 00217 00467 00967 00967 00967 00217 00967 00467
00029 00053 00062 00062 0 00049 00058 00029 0007 00025 00049 00037
Step 7, calculating a grey correlation coefficient
The grey correlation coefficient of the subsequence parameters to the mother sequence is calculated according to equation (12). The results are shown below:
Figure GDA0003684574020000201
Figure GDA0003684574020000211
step 8, calculating grey correlation degree
The gray correlation degree is calculated according to formula (13). The gray correlation calculation result is shown in fig. 11. The first column parameter has a grey correlation degree of 1.
Step 9, solving the weight of the geological target evaluation parameters determined by gray correlation under the constraint of expert experience
Using formula 14) to obtain the geological target evaluation parameter weight, and the value is shown in fig. 12 and table 12. In fig. 12, index 1 is (S1) source rock thickness, index 2 is (S2) hydrocarbon supply area coefficient, index 3 is (S3) hydrocarbon supply condition, index 4 is (R1) reservoir type, index 5 is (R2) distance to fracture, index 6 is (R3) sedimentary facies zone, index 7 is (T1) trap type, index 8 is (T2) developmental background, index 9 is (P1) cap lithology, index 10 is (P2) cap thickness, index 11 is (M1) distance to source rock, index 12 is (M2) dredging condition, and index 13 is (M3) through source fracture development.
TABLE 12 geological target evaluation parameter weights
Figure GDA0003684574020000212
And step 10, target evaluation.
The overall evaluation is determined using equation (15) for each target evaluation score, see table 13.
TABLE 13 comprehensive scoring table for geological target evaluation of central uplifted zone
Sorting Target sequence number Target evaluation
1 1 7.86015
2 2 7.036385
3 4 6.363
4 3 5.97185
5 5 5.947435
6 9 5.64715
7 8 5.467765
8 11 5.2332
9 6 5.12656
10 10 5.108435
11 7 4.451
From the analysis of table 13: targets 1, 2, 4 are favorable targets, targets 3, 5, 9 are moderately favorable targets, and traps 8, 11, 6, 10, 7 are unfavorable targets. The evaluation result is consistent with the field evaluation, and the method is considered to be more reasonable in geological target evaluation.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims (3)

1. A method for obtaining the weight of geological target evaluation parameters is characterized by comprising the following steps: the method comprises the following steps:
s1: determining the parameter characteristic value of the geological target evaluation,
reading the characteristic value of each evaluation parameter by referring to the related geological map or test analysis data to obtain the characteristic value of the quantitative evaluation parameter of the geological target;
s2: determining expert weight of the evaluation parameters to obtain a geological target quantitative evaluation parameter score;
and (3) constructing an evaluation parameter constraint weight according to an expert experience method, wherein the calculation formula is as follows:
Figure FDA0003698065080000011
wherein W is the quantitative evaluation parameter weight of the geological target, and WqFor the nth geological target quantitative evaluation parameter score, S1, S2 and S3 are source rock conditions, R1, R2 and R3 are reservoir conditions, T1 and T2 are trap conditions, P1 and P2 are preservation conditions, and M1, M2 and M3 are migration conditions;
s3: constructing a parameter evaluation matrix and a parameter evaluation matrix,
determining the score of the geological target evaluation parameter by using a value assigning method;
s4: constructing expert constraint conditions, determining the parent sequence of the grey incidence matrix,
constructing a new expert constraint matrix, taking each evaluation parameter weight determined by the geological target quantitative evaluation parameter score as a mother sequence, and comparing the size of the average value of the mother sequence with the average value of each evaluation parameter, if the average value of the mother sequence is larger than the average value of the evaluation parameters, subtracting the difference value of the mother sequence and a subsequence by the geological target quantitative evaluation parameter score, wherein the subsequence is the ordered arrangement of each sub-factor data influencing the properties of the evaluated transactions to a certain extent, and thus determining a new mother sequence; if the mother sequence average value is smaller than the evaluation parameter evaluation value, the mother sequence is not adjusted, and the like, the mother sequence average value is compared with the rest evaluation parameter average values one by one, and finally the mother sequence in the improved gray correlation matrix is determined;
s5: constructing a parameter evaluation matrix under the constraint of experts,
constructing a new parameter gray correlation matrix according to the mother sequence and the subsequence;
the subsequence is:
Figure FDA0003698065080000021
the new parameter gray correlation matrix is:
Figure FDA0003698065080000022
s6: carrying out grey correlation analysis and evaluation index parameter normalization processing;
a min-max standardization method is selected to carry out normalization processing on the data, and the formula is as follows:
Figure FDA0003698065080000023
wherein max is the maximum value of the sample data, min is the minimum value of the sample data, and the value of the sequence is normalized to [0, 1 ];
s7: calculating a grey correlation coefficient;
calculating the normalized evaluation parameter data by a formula (12) to obtain a grey correlation coefficient of the trap evaluation parameter represented by each subsequence and the evaluation parameter weight obtained by a parent sequence expert scoring method,
Figure FDA0003698065080000024
wherein xi isi(k) Representing the correlation coefficient of the kth parameter of the ith subsequence and the kth parameter of the mother sequence, wherein rho is a resolution coefficient and the value range is [0, 1%]；
S8: calculating the grey correlation degree;
the grey correlation is calculated by the formula:
Figure FDA0003698065080000031
wherein r isiRepresenting the grey correlation degree between the subsequence and the parent sequence, wherein k represents the number of evaluation indexes;
s9: obtaining the weight of a target evaluation parameter;
the calculation formula for calculating the weight of the target evaluation parameter is as follows:
Figure FDA0003698065080000032
wherein k isiWeighting the evaluated parameters;
s10: the evaluation of the target is carried out,
the target evaluation calculation idea is to multiply the evaluation parameters by the weight of the parameters, and then accumulate to obtain the total score Z value of the target evaluation, and the calculation formula is as follows:
Ks×(S1+S2+S3)+Kp×(P1+P2)+Kr×(R1+R2+R3)+Km×(M1+M2+M3)+Kt×(T1+T2)＝Z (15)。
2. the method of claim 1, wherein the geological target evaluation parameter weight is calculated by: the evaluation parameters comprise source rock thickness, hydrocarbon supply area coefficient, hydrocarbon supply conditions, reservoir type, distance to fracture, sedimentary facies belt, trap type, developmental background, cap lithology, cap thickness, distance to source rock, dredging conditions and source fracture development.
3. The method of claim 1, wherein the geological target evaluation parameter weight is calculated by: in the case of the above-mentioned S4,
in the expert constraint matrix, the geological target quantitative evaluation parameter score calculation formula is as follows:
Figure FDA0003698065080000033
constructing a new expert constraint matrix as follows:
Figure FDA0003698065080000041
the calculation formula of the mother sequence average value is as follows:
Wa＝(W1+W2+…+Wm)/m (4)；
the calculation formula of the average value of each evaluation parameter is as follows:
Figure FDA0003698065080000042
the calculation formula for comparing the sizes of the mother sequence average value and each evaluation parameter average value is as follows:
AV＝Wa-Xt (6)；
if the average value of the mother sequence is larger than the average value of the evaluation parameters, subtracting the difference value of the average value of the mother sequence and the average value of each evaluation parameter from the qualitative target quantitative evaluation parameter score, and determining a new mother sequence according to the following calculation formula:
If Wa＞Xt，Wz＝Wq-AV，
Wz＝{W1 W2 W3 … Wm} (7)；
if the average value of the mother sequence is smaller than the evaluation parameter evaluation value, the calculation formula of the mother sequence without adjustment is as follows:
If Wa＜Xt，Wz＝Wq，
Wz＝{W1 W2 W3 … Wm} (8)。
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