CN107944749B - Shale gas block development potential evaluation method based on relative preference index - Google Patents
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Abstract
The invention discloses a shale gas block development potential evaluation method based on a relative preference index, which comprises the steps of collecting accumulation and development elements of a shale gas research block, and collecting basic data; establishing a control factor relation flow chart; obtaining the influence distribution of the control factors; establishing an evaluation factor vector; calculating the specific gravity distribution of the evaluation factors; respectively calculating the quality distribution of the comparison blocks under each evaluation factor; respectively calculating the reliability distribution of the quality of the comparison blocks under each evaluation factor; calculating a relative preference index COI; the invention establishes a relative evaluation theoretical system of dynamic calculation, calculates the COI (total cost index) of relative preference by using the collective difference of multiple control factors, comprehensively evaluates the selected area according to the value of the COI, provides a uniform method for comparison of different geological backgrounds, different blocks and different exploration degrees, gives consideration to the accumulation and sequestration possibility and developability of shale gas and provides a more reliable solution for comprehensive evaluation of the shale gas preference area.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration and development, in particular to a shale gas block development potential evaluation method based on a relative preference index. The method is suitable for evaluating the exploration and development potential of the shale gas block, and is particularly suitable for comprehensively evaluating the favorable area of the shale gas mature exploration area.
Background
In recent years, shale gas development is considered as a revolution in the global energy field, which not only can greatly improve the self-sufficient rate of national energy, but also can change the national energy consumption structure. In view of the increasingly important position of shale gas, in order to achieve more accurate evaluation of the potential of shale gas blocks, new technical methods in multiple professional fields emerge in related subjects such as geology, geophysical prospecting and the like in recent years: for example, a shale resource comprehensive evaluation method based on regional original data and combined with a hydrocarbon-containing system simulation technology, a GeoSphere reservoir while-drilling mapping technology, a nuclear magnetic resonance factor analysis technology, a seismic data pre-stack seismic channel processing technology, a logging data function principal component analysis technology and the like.
The potential evaluation work of shale gas exploration and development at the present stage is based on technical research for obtaining important parameters, supports deep research by means of related professional technical progress or application of new technology, and is less complete in an evaluation system. In the past, the following problems have not been fully appreciated:
1. shale gas exploration and development in china is still in an early stage compared to the united states, and development of much work requires reference to the mature experience of the united states. However, the geological conditions of shale gas in two countries are greatly different, and how to scientifically compare shale gas systems with different geological backgrounds and obtain referenceable information from the shale gas systems is of great importance;
2. under the condition of lacking complete geological rule disclosure, a block absolute evaluation system is established, and the method has guiding significance on shale gas exploration and development potential evaluation;
3. in the comparative analysis work of related factors, the individual difference of isolated factors is used for representing the block comprehensive effect, and the obtained result is reasonable.
Under the premise that the shale gas reservoir theoretical system is not changed, the twig technology is deeply promoted, but a perfect evaluation system research is lacked, and the comprehensive evaluation work of the selected area is carried out under the thought framework, so that the following difficulties are encountered:
1. the work of selecting the main control factors is too dependent on experience and has strong subjectivity.
2. After a plurality of master control factors are selected, the relation among the master control factors is quite complex, the master control factors are mutually influenced and cannot be independently eliminated.
3. The influence between most factors is not described by a clear functional relationship.
4. The dominant factors that can be quantitatively compared account for only a small percentage of all factors.
5. In the comparative analysis work of the main control factors in the historical data, the comparative analysis is easy to be carried out aiming at the factors with larger differences, and the systematic overall research is not carried out.
6. The factors of measurement, calculation and quantification are that the source of data and the calculation result have uncertainty, such as data obtained by logging, well logging, seismic exploration, laboratory measurement and the like, to characterize the relevant factors, and the difference of reliability caused by different blocks or different obtaining means is ignored.
In view of the above problems and difficulties, it is necessary to improve the comprehensive evaluation method for the favorable area in the aspects of shale gas enrichment and reservoir and exploitability, so that the logic of the evaluation system is tighter and the method is more reasonable in the framework of the shale gas reservoir theory, and a reliable basis is provided for shale gas exploration and development potential evaluation and comprehensive area selection evaluation.
Disclosure of Invention
In order to overcome the defects of the background art, the invention aims to provide a shale gas block development potential evaluation method based on a relative preference index on the basis of analyzing the problems of the existing comprehensive evaluation method. The method establishes a method for rating and evaluating the selected area by calculating the relative preference index, and provides a unified comprehensive evaluation method system for comparing different geological backgrounds, different blocks and different exploration degrees.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a shale gas block development potential evaluation method based on a relative preference index comprises the following specific steps:
the method comprises the following steps: gathering enrichment and reclamation elements of shale gas research blocks, sources include but are not limited to: hydrocarbon production capacity, reservoir storage performance, gas reservoir cap condition and easy exploitation. Collecting the underlying data includes, but is not limited to: geophysical prospecting, drilling, logging and geological data.
Step two: and establishing a control factor relation flow chart.
Step three: and solving the influence distribution of the control factors in the relational flow graph. Wherein, step three includes 3 substeps:
3.1 establishing an adjacency matrix M1 corresponding to the relational flow graph;
3.2 using the element with the value of 0 in the decimal correction matrix M1 between 0 and 1 and approaching to 0, and establishing a frequency distribution matrix M2 on the basis of the element;
and 3.3, calculating and solving the influence distribution of the control factors by using a power iteration method.
Description of step three 1: the matrix and the elements are concepts which are defined mathematically; the adjacency matrix building method, the power iteration method, is a mathematically known method.
Step three, explanation 2: the frequency distribution matrix M2 establishing method comprises the following steps: the matrix M1 with the modified 0 value is obtained by dividing each element by the sum of all elements in the row where the element is located, and the obtained new value is used as the element with the same position in the frequency distribution matrix M2.
Step four: an evaluation factor vector V1 is established.
Step four description 1: vectors are concepts that have been defined mathematically.
Step four, explanation 2: the evaluation factor vector V1 establishing method comprises the following steps: and extracting factors participating in comparison from the control factors to serve as evaluation factors, wherein the vector elements are sequentially composed of corresponding influence distribution values.
Step five: calculating the specific gravity distribution W of the evaluation factorf. Wherein, step five comprises 3 substeps:
5.1 construction of the comparison matrix M3: taking the evaluation factor vector V1 as a column vector, taking a vector V2 formed by reciprocals of each element of the V1 vector as a row vector, and multiplying the row vector by the column vector to obtain a comparison matrix M3;
5.2 replace the elements of the comparison matrix M3 greater than 9 by 9, the elements between 1 and 9 being rounded; modifying elements of the matrix whose elements are less than 1: let aijAre elements of the matrix M3, where the indices i, j are the row and column, if aij<1, then modify its value to 1/aji。。
5.3 solving the eigenvector W1 corresponding to the maximum eigenvalue of the matrix M3, dividing each element in the eigenvector W1 by the sum of all elements W1 to obtain a new value as an evaluation factor proportion distribution vector WfThe element values of the same position in the vector are obtained, thereby obtaining an evaluation factor weight distribution vector Wf。
The fifth step is explained as follows: the eigenvalue and eigenvector are concepts that have been defined mathematically.
Step six: and respectively calculating the quality degree distribution D of the comparison blocks under each evaluation factor. Wherein, step six includes 4 substeps:
6.1 if the kth evaluation factor can only be measured qualitatively, then through comparing the blocks pairwise, a block comparison matrix of the kth evaluation factor is established, which is marked as M4kThe subscript k means the number corresponding to the evaluation factor; solving for M4kThe eigenvector corresponding to the largest eigenvalue is marked as w2kThe feature vector w2 is applied in the same way as in step 5.3kCalculating to obtain block goodness distribution D under the k evaluation factork。
6.2 if the k-th evaluation factor can be measured quantitatively and is characterized by a frequency, DkIs the current frequency count divided by the sum of all frequency counts.
6.3 if the kth evaluation factor can be measured quantitatively and is characterized by the size of the attribute value, Min-Max standardization is performed first, and then the matrix M4 is constructedk: the normalized data set is defined as vector V3, column vector V3, vector V4 formed by the reciprocal of each element of vector V3 is defined as row vector, and column vector is multiplied by row vector to obtain matrix M4k(ii) a Solving for M4kThe eigenvector corresponding to the largest eigenvalue is marked as w2kThe feature vector w2 is applied in the same way as in step 5.3kCalculating to obtain block goodness distribution D under the k evaluation factork。
6.4, establishing a quality distribution matrix D. Matrix D is composed of elements DklThe subscript k means an evaluation factor-corresponding number, and the subscript l means a block-corresponding number. The k column of the matrix is the block goodness distribution D under the k evaluation factorkAnd so on.
Step 6.1 description 1: block goodness distribution D under k-th evaluation factorkIs a vector, and the value of the l-th element in the vector corresponds to the goodness of the l-th block.
Step 6.1 Explanation 2: the k th evaluation factorSubdivision block contrast matrix M4kEach matrix element is obtained by comparing two blocks. Comparing two blocks each time, comparing the quality, wherein the precision of the degree is divided into two stages, the first stage precision is an upper, middle and lower three stages, and corresponds to numerical values 9, 6 and 3; if the precision needs to be improved to the second level, the two levels are divided into nine levels, namely upper, middle and middle, middle and lower, upper, middle and lower, corresponding to values of 9 to 1. If block a is better quality than block B by 9 to 1, then block B is worse quality than block a by 1 to 9, and so on.
Step 6.1 Explanation 3: and comparing the qualitative measurement, wherein the obtained comparison matrix needs to be subjected to matrix logic consistency check, and the matrix is continuously corrected until the check is passed, so that the matrix is consistent with the logic consistency. And (3) logical consistency checking and judging process: let the maximum eigenvalue of the contrast matrix be λmaxThe contrast matrix is an n-order matrix when λmax-n<Epsilon (threshold epsilon given by person, value 0)<ε<<1) The logical consistency check passes.
Step 6.2 illustrates: frequency is a concept that has been defined mathematically.
Step 6.3 illustrates: the Min-Max normalization is a mathematically known method and it should be noted that the minimum should be slightly smaller than the minimum of the real data set to prevent the occurrence of 0.
Step seven: and respectively calculating the quality reliability distribution T of the comparison blocks under each evaluation factor. Wherein, step seven comprises 3 substeps:
7.1 establishing a block quality reliability comparison matrix M5 of the k-th evaluation factor by comparing the two blockskThe subscript k means the number corresponding to the evaluation factor;
7.2 solving the matrix M5kThe eigenvector corresponding to the largest eigenvalue is marked as w3kThe feature vector w3 is applied in the same way as in step 5.3kCalculating to obtain the block goodness reliability distribution T under the k evaluation factork。
7.3, establishing a quality reliability distribution matrix T. The matrix T is formed by elements TklThe subscript number k meansThe evaluation factor corresponds to a serial number, and the subscript l means a block corresponding serial number. The k column of the matrix is the block goodness reliability distribution T under the k evaluation factorkAnd so on.
Step 7.1 Explanation 1: the reliability of the quality degree refers to the degree of certainty in the qualitative comparison process, or the degree of certainty in the source of the quantitative data and the calculation accuracy. Block goodness reliability distribution T under k-th evaluation factorkIs a vector, and the value of the l-th element in the vector is the reliability of the goodness corresponding to the l-th block.
Step 7.1 Explanation 2: m5 was constructed in the same manner as described in Explanation 2 of step 6.1k。
Step 7.1 description 3: matrix M5 was aligned in the same way as in Explanation 3 of step 6.1kAnd carrying out matrix logic consistency check, and continuously correcting the matrix until the check is passed.
Step eight: and calculating a relative preference index COI, multiplying the block goodness distribution of each factor by the block goodness reliability distribution according to the block correspondence, multiplying the obtained result by the proportion of each evaluation factor, and accumulating the results of all the evaluation factors according to the block correspondence to obtain the COI.
Description of step eight 1: the relative preference index (COI) is a data set, consisting of the relative preference index of each block. The relative preference index corresponding to the ith block is marked as COIl. The relative preference index for each block may be a numerical value or a distribution of numerical values related to geographic location, depending on whether each evaluation factor for the comparison block is represented by a single datum or a set of data. COIlIs as in formula (1):
COI is expressed as formula (2):
wherein m is the total number of evaluation factors; z is the total number of comparison blocks.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, research is carried out on the basis of analyzing the problems of the existing comprehensive evaluation method, the relative preference index is calculated by using the collective difference of multiple control factors, the block comprehensive evaluation is carried out through the numerical value of the index, a set of relative evaluation theoretical system of dynamic calculation is established, the shale gas enrichment and accumulation possibility and the exploitability are considered, and the result has guiding significance.
2. Different from the traditional comprehensive evaluation method, different methods and standards are adopted for rating and evaluating quantifiable factors and factors difficult to quantify respectively, and the quantitative control factors and the qualitative control factors are calculated under a unified evaluation system, so that block evaluation results under the same standard are more reasonable.
3. The method considers the reliability of the data source and the uncertainty of the calculation result, and the difference caused by the reliability and the uncertainty is included in the relative preference index for calculation, so that the method system is more rigorous.
Drawings
FIG. 1 is a technical flow chart of the present invention;
FIG. 2 is a flow chart of control factor relationships according to an embodiment of the present invention;
FIG. 3 is a diagram of a data structure of the adjacency matrix M1 according to an embodiment of the invention.
Detailed Description
In order to make the technical solution and advantages of the present invention more clearly understood, the following detailed description of the preferred embodiments of the present invention is made with reference to fig. 1, fig. 2 and fig. 3, and it should be noted that the specific embodiments described herein are only used for explaining the present invention and are not used for limiting the present invention.
Taking the shale gas block in south of the east of Chuan as an example, 3 wells of W1, W2 and W3 are arranged in the pre-selected well positions in W block, 3 wells of N pre-selected well positions in N block N are N1, N2 and N3, 2 wells of P pre-selected well positions in P block P are P1 and P2, the geographical positions are different, and the 3 pre-selected well positions in 8 blocks are comprehensively evaluated, and the steps are as shown in figure 1.
Considering the related characterization factors from the aspects of micro, macro, raw storage and mining, the enumeration is as follows: mineral composition, organic carbon content, porosity, maturity, permeability, gas production, fracture development, formation pressure, brittleness, shale thickness, planar spread (area), burial depth, construction.
And 102, establishing a control factor relation flow chart.
The control factor relationship flows to figure 2.
And 103, solving the influence distribution of the control factors in the relational flow graph.
Set up adjacency matrix M1. The adjacency matrix M1 is shown in fig. 3.
Secondly, correcting the matrix M1 by 0.01 to establish a frequency distribution matrix: each element of the matrix M1 is divided by the sum of all the elements in the row in which it is located, and the new value is taken as the element of the frequency distribution matrix M2 at that position, with the following results:
M2＝
[0.004739336,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336,0.473933649,0.004739336,0.473933649,0.004739336,0.004739336,0.004739336,0.004739336；0.004739336,0.004739336,0.004739336,0.473933649,0.004739336,0.473933649,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336；0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077；0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.892857143,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571；0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077,0.076923077；0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.892857143,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571；0.004739336,0.004739336,0.473933649,0.004739336,0.473933649,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336；0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.892857143,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571；0.004739336,0.004739336,0.004739336,0.004739336,0.473933649,0.004739336,0.473933649,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336；0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.892857143,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571；0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.892857143,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571,0.008928571；0.004739336,0.004739336,0.004739336,0.473933649,0.004739336,0.004739336,0.004739336,0.473933649,0.004739336,0.004739336,0.004739336,0.004739336,0.004739336；0.001968504,0.001968504,0.001968504,0.001968504,0.001968504,0.001968504,0.196850394,0.196850394,0.001968504,0.196850394,0.196850394,0.196850394,0.001968504]
thirdly, a feature vector V0 corresponding to the maximum feature value 1 is obtained by using a power iteration method, so that the feature vector V0 meets the condition: the sum of all vector elements of V0 equals 1. The results are as follows:
V0＝
[0.0127,0.0127,0.0267,0.0258,0.0355,0.4022,0.0299,0.3778,0.0187,0.0152,0.0152,0.0152,0.0127]
obtaining the influence distribution of the control factors: mineral components: 0.0127; organic carbon content: 0.0127; porosity: 0.0267; maturity: 0.0258; permeability: 0.0355; gas generation amount: 0.4022, respectively; crack development: 0.0299; formation pressure: 0.3778, respectively; brittleness: 0.0187; shale thickness: 0.0152; area: 0.0152; burying deeply: 0.0152; the construction method comprises the following steps: 0.0127.
and 104, extracting the factors participating in comparison from the control factors, and establishing an evaluation factor vector V1 according to the corresponding influence distribution numerical value.
According to the obtained basic data information of the block W, N, P, the organic carbon content, maturity, formation pressure, shale thickness and burial depth are selected as evaluation factors, and the results are as follows:
V1＝[0.0127,0.0258,0.3778,0.0152,0.0152]
Firstly, constructing a matrix M3, and obtaining the following results:
M3＝
[1,0.492248062,0.03361567,0.835526316,0.835526316；2.031496063,1,0.068290101,1.697368421,1.697368421；29.7480315,14.64341085,1,24.85526316,24.85526316；1.196850394,0.589147287,0.040232927,1,1；1.196850394,0.589147287,0.040232927,1,1]
② modify the matrix M3 as follows:
M3＝[1,0.5,0.11111,1,1；2,1,0.11111,2,2；9,9,1,9,9；1,0.5,0.11111,1,1；1,0.5,0.11111,1,1]
thirdly, the proportion distribution W of the evaluation factors is obtainedf：
And solving the eigenvector w1 corresponding to the maximum eigenvalue of the matrix M3. Calculation of the evaluation factor specific gravity distribution W by W1f. The results are as follows:
Wf＝[0.0652,0.1152,0.6892,0.0652,0.0652]
obtaining an evaluation factor specific gravity distribution: organic carbon content 0.0652, maturity 0.1152, formation pressure 0.6892, shale thickness 0.0652, burial depth 0.0652.
And 106, respectively calculating the quality degree distribution D of the comparison blocks under each evaluation factor.
The organic carbon content, maturity, formation pressure, shale thickness, buried depth all have quantitative data, wherein:
the organic carbon contents of W1, W2, W3, N1, N2, N3, P1 and P2 are as follows in sequence: 3.2, 3.1, 3.4, 3.3, 3.5, 3.1, 3.2;
the maturity of W1, W2, W3, N1, N2, N3, P1 and P2 is as follows: 2.3, 2.2, 2.3, 2.8, 2.7, 2.1, 2.0;
the formation pressures of W1, W2, W3, N1, N2, N3, P1 and P2 are sequentially as follows: 1.1, 1.09, 1.1, 1.32, 1.31, 1.30, 0.98, 0.97;
the shale thicknesses of W1, W2, W3, N1, N2, N3, P1 and P2 are as follows: 28. 27, 28, 30, 31, 27, 28;
the buried depths of W1, W2, W3, N1, N2, N3, P1 and P2 are as follows: 2652. 2822, 3429, 2690, 2811, 3530, 2538, 2679.
Before comparison, Min-Max standardization is carried out, and then a matrix M4 is constructed, wherein all buried depth data take negative values.
Organic carbon content contrast matrix M41The results are as follows:
M41＝
[1,1,11,0.355,0.524,0.268,11,1；1,1,11,0.355,0.524,0.268,11,1；0.091,0.091,1,0.032,0.048,0.024,1,0.091；2.818,2.818,31,1,1.476,0.756,31,2.818；1.909,1.909,21,0.677,1,0.512,21,1.909；3.727,3.727,41,1.323,1.952,1,41,3.727；0.091,0.091,1,0.032,0.048,0.024,1,0.091；1,1,11,0.355,0.524,0.268,11,1]
maturity contrast matrix M42The results are as follows:
M42＝
[1,1.09,1,0.708,0.708,0.752,1.198,1.33；0.917,1,0.917,0.649,0.649,0.689,1.099,1.22；1,1.09,1,0.708,0.708,0.752,1.198,1.33；1.413,1.541,1.413,1,1,1.062,1.693,1.879；1.413,1.541,1.413,1,1,1.062,1.693,1.879；1.331,1.45,1.331,0.942,0.942,1,1.594,1.769；0.835,0.91,0.835,0.591,0.591,0.627,1,1.11；0.752,0.82,0.752,0.532,0.532,0.565,0.901,1]
formation pressure comparison matrix M43The results are as follows:
M43＝
[1,1.083264,1,0.371608,0.382535,0.394123,12.881188,1301；0.923136,1,0.923136,0.343045,0.353131,0.363829,11.891089,1201；1,1.083264,1,0.371608,0.382535,0.394123,12.881188,1301；2.691007,2.915071,2.691007,1,1.029403,1.060588,34.663366,3501；2.614143,2.831807,2.614143,0.971437,1,1.030294,33.673267,3401；2.537279,2.748543,2.537279,0.942873,0.970597,1,32.683168,3301；0.077633,0.084097,0.077633,0.028849,0.029697,0.030597,1,101；0.000769,0.000833,0.000769,0.000286,0.000294,0.000303,0.009901,1]
shale thickness contrast matrix M44The results are as follows:
M44＝
[1,11,1,0.355,0.268,0.268,11,1；0.091,1,0.091,0.032,0.024,0.024,1,0.091；1,11,1,0.355,0.268,0.268,11,1；2.818,31,2.818,1,0.756,0.756,31,2.818；3.727,41,3.727,1.323,1,1,41,3.727；3.727,41,3.727,1.323,1,1,41,3.727；0.091,1,0.091,0.032,0.024,0.024,1,0.091；1,11,1,0.355,0.268,0.268,11,1]
buried depth contrast matrix M45The results are as follows:
M45＝
[1,1.23981,8.62512,1.04519,1.22086,976.55556,0.88518,1.03169；0.80658,1,6.95682,0.84303,0.98472,787.66667,0.71397,0.83214；0.11594,0.14374,1,0.12118,0.14155,113.22222,0.10263,0.11961；0.95676,1.1862,8.25221,1,1.16808,934.33333,0.84691,0.98709；0.81909,1.01552,7.06477,0.85611,1,799.88889,0.72505,0.84505；0.00102,0.00127,0.00883,0.00107,0.00125,1,0.00091,0.00106；1.12971,1.40062,9.74387,1.18076,1.37922,1103.22222,1,1.16551；0.96928,1.20172,8.36016,1.01308,1.18336,946.55556,0.85799,1]
through calculation, the organic carbon content quality distribution D1The results are as follows:
D1＝[0.086,0.086,0.008,0.242,0.164,0.320,0.008,0.086]
maturity and inferiority distribution D2The results are as follows:
D2＝[0.115,0.106,0.115,0.163,0.163,0.154,0.096,0.087]
distribution D of superiority and inferiority of formation pressure3The results are as follows:
D3＝[0.09221719,0.08512899,0.09221719,0.24815707,0.24106890,0.23398069,
0.00715908,0.00007091]
shale thickness goodness distribution D4The results are as follows:
D4＝[0.074,0.007,0.074,0.209,0.277,0.277,0.007,0.074]
buried depth quality distribution D5The results are as follows:
D5＝[0.1725,0.1391,0.0200,0.1650,0.1413,0.0002,0.1948,0.1672]
establishing a quality distribution matrix D, and obtaining the following results:
D＝
[0.086,0.115,0.09221719,0.074,0.1725；0.086,0.106,0.08512899,0.007,0.1391；0.008,0.115,0.09221719,0.074,0.02；0.242,0.163,0.24815707,0.209,0.165；0.164,0.163,0.2410689,0.277,0.1413；0.32,0.154,0.23398069,0.277,0.0002；0.008,0.096,0.00715908,0.007,0.1948；0.086,0.087,0.00007091,0.074,0.1672]
step 107: and respectively calculating the quality reliability distribution T of the comparison blocks under each evaluation factor.
The organic carbon content and maturity of block W, N, P are all experimental results, and the reliability is basically consistent, and the formation pressure, shale thickness and burial depth of the three blocks are all established on the basis of geophysical data processing and interpretation, and the maximum difference between the reliability comes from that block W, P is two-dimensional seismic exploration data, and block N is three-dimensional seismic exploration data, so the reliability of block N is higher. The division of the comparison precision adopts three steps of upper, middle and lower, which is the first-level precision. The reliability contrast matrix is established as follows:
organic carbon content reliability comparison matrix M51：
M51＝
[1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1]
Maturity reliability contrast matrix M52：
M52＝
[1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1；1,1,1,1,1,1,1,1]
Formation pressure reliability comparison matrix M53：
M53＝
[1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1；3,3,3,1,1,1,3,3；3,3,3,1,1,1,3,3；3,3,3,1,1,1,3,3；1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1]
Shale thickness reliability contrast matrix M54：
M54＝
[1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1；3,3,3,1,1,1,3,3；3,3,3,1,1,1,3,3；3,3,3,1,1,1,3,3；1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1]
Buried depth reliability contrast matrix M55：
M55＝
[1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1；3,3,3,1,1,1,3,3；3,3,3,1,1,1,3,3；3,3,3,1,1,1,3,3；1,1,1,0.333,0.333,0.333,1,1；1,1,1,0.333,0.333,0.333,1,1]
The threshold epsilon is set to 0.1 and all of the above 5 reliability comparison matrices pass the logical consistency check. Through calculation, the reliability distribution T of the organic carbon content1：
T1＝[0.125,0.125,0.125,0.125,0.125,0.125,0.125,0.125]
Maturity reliability distribution T2：
T2＝[0.125,0.125,0.125,0.125,0.125,0.125,0.125,0.125]
Formation pressure reliability distribution T3：
T3＝[0.0714,0.0714,0.0714,0.2143,0.2143,0.2143,0.0714,0.0714]
Shale thickness reliability distribution T4：
T4＝[0.0714,0.0714,0.0714,0.2143,0.2143,0.2143,0.0714,0.0714]
Buried depth reliability distribution T5：
T5＝[0.0714,0.0714,0.0714,0.2143,0.2143,0.2143,0.0714,0.0714]
Establishing a reliability distribution matrix T:
T＝
[0.125,0.125,0.0714,0.0714,0.0714；0.125,0.125,0.0714,0.0714,0.0714；0.125,0.125,0.0714,0.0714,0.0714；0.125,0.125,0.2143,0.2143,0.2143；0.125,0.125,0.2143,0.2143,0.2143；0.125,0.125,0.2143,0.2143,0.2143；0.125,0.125,0.0714,0.0714,0.0714；0.125,0.125,0.0714,0.0714,0.0714]
step 108: calculating the relative preference index COI according to equations (1) and (2):
wherein, the relative preference indexes of W1, W2, W3, N1, N2, N3, P1 and P2 are as follows: 0.0080, 0.0071, 0.0067, 0.0462, 0.0451, 0.0433, 0.0027 and 0.0031.
The relative preference index size is arranged in sequence, and the sequence is as follows: n1, N2, N3, W1, W2, W3, P2 and P1. In general, block N is a relatively more optimal vantage point and the geographic location of N1 is a relatively optimal preselected well location.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and scope of the present invention is also included in the present invention.
Not described in detail is prior art.
Claims (7)
1. A shale gas block development potential evaluation method based on a relative preference index is characterized by comprising the following specific steps:
collecting enrichment accumulation and development elements of a shale gas research block, and collecting basic data;
step two, establishing a control factor relation flow diagram, wherein the control factors are the elements and data collected in the step one, and specifically include mineral components, organic carbon content, porosity, maturity, permeability, gas generation amount, crack development, formation pressure, brittleness, shale thickness and plane distribution, namely area, burial depth and structure;
thirdly, solving the influence distribution of the control factors;
step four, establishing an evaluation factor vector;
calculating the proportion distribution of the evaluation factors;
respectively calculating the quality distribution of the comparison blocks under each evaluation factor;
step seven, respectively calculating the reliability distribution of the quality of the comparison blocks under each evaluation factor;
step eight, calculating a relative preference index COI;
in the first step, the gathering, reservoir-forming and development elements of the shale gas research block are collected and established on the basis of early geological analysis work, and the sources include: hydrocarbon production capacity, reservoir performance, gas reservoir cap condition, and/or easy-to-recover; collecting the base data includes: geophysical, drilling, logging, and/or geological data;
in the second step, a control factor relation flow chart is established;
in the third step, firstly, an adjacent matrix corresponding to the relational flow graph is established, then, the 0 value in the matrix is corrected by using a decimal between 0 and 1 and approaching to 0, a frequency distribution matrix is established, and the influence distribution of the control factors is calculated on the frequency distribution matrix through a power iteration method;
the fourth step, the evaluation factor vector establishing method is as follows: extracting factors participating in comparison from the control factors as evaluation factors, wherein the vector elements are sequentially composed of corresponding influence distribution values;
in the fifth step, firstly, a comparison matrix is established, then the matrix is modified, then a matrix characteristic vector is solved, and the characteristic vector is converted to obtain an evaluation factor proportion distribution vector;
the frequency distribution matrix establishing method comprises the following steps: dividing each element of the matrix subjected to the 0 value correction by the sum of all elements in the row where the element is located to obtain a new value as an element at the same position in the frequency distribution matrix;
the method for establishing the comparison matrix comprises the following steps: taking the evaluation factor vector as a column vector, taking a vector formed by the reciprocal of each element of the vector as a row vector, and multiplying the column vector by the row vector to obtain a matrix;
the method for modifying the comparison matrix comprises the following steps: replacing elements in the matrix larger than 9 by 9, and rounding the elements between 1 and 9; modifying elements of the matrix whose elements are less than 1: let aijAre elements of the matrix, where the indices i, j are the row and column, if aij<1, then modify its value to 1/aji；
The conversion of the feature vector means: and dividing each element of the feature vector by the sum of all the elements to obtain a new value as an element numerical value of the evaluation factor at the same position in the proportion distribution vector.
2. The shale gas block development potential evaluation method based on the relative preference index as claimed in claim 1, wherein: in the sixth step, the block goodness distribution under each evaluation factor is a vector; in the solving process, whether the current evaluation factor is qualitative or quantitative needs to be judged; if the evaluation factor can only be measured qualitatively, establishing a block comparison matrix of the factor by comparing every two blocks, solving a feature vector corresponding to the maximum feature value of the matrix, and converting the feature vector to obtain the block goodness and badness distribution under the factor; if the evaluation factor can be measured quantitatively and is characterized by frequency, the value of each element of the goodness distribution vector is the sum of the current frequency divided by all the frequency; if the evaluation factor can be measured quantitatively and is characterized by the size of the attribute value, Min-Max standardization is carried out, then a block comparison matrix is constructed, the eigenvector corresponding to the maximum eigenvalue of the matrix is solved, the eigenvector is converted to obtain block goodness and inferiority distribution under the factor, and the conversion of the eigenvector refers to dividing each element of the eigenvector by the sum of all elements, and the obtained new value is used as the element numerical value of the same position in the evaluation factor proportion distribution vector.
3. The shale gas block development potential evaluation method based on the relative preference index as claimed in claim 2, wherein: under qualitative measurement, the blocks are compared pairwise, and the method for establishing the contrast matrix comprises the following steps: comparing two blocks each time, comparing the quality, wherein the precision of the degree is divided into two stages, the first stage precision is an upper, middle and lower three stages, and corresponds to numerical values 9, 6 and 3; if the precision needs to be improved to the second level, the two levels are divided into nine levels, namely upper level, middle level, lower level, upper level, middle level, lower level, corresponding to a value of 9 to 1, if the block A is better than the block B in quality, and the degree is 9 to 1, the block B is worse than the block A in quality, namely the degree is 1 to 9, and so on.
4. The shale gas block development potential evaluation method based on the relative preference index as claimed in claim 3, wherein: comparing the qualitative measurement, wherein the obtained comparison matrix needs to be subjected to matrix logic consistency check, and the matrix is continuously corrected until the check is passed, so that the matrix is consistent with the logic consistency, and the logic consistency check judgment process comprises the following steps: let the maximum eigenvalue of the contrast matrix be λmaxThe contrast matrix is an n-order matrix when λmaxThe difference from n is less than a given minimum value, the logical consistency check passes.
5. The shale gas block development potential evaluation method based on the relative preference index as claimed in claim 1, wherein: in the seventh step, firstly, a block quality reliability comparison matrix of each evaluation factor is established through pairwise comparison of blocks; solving the eigenvector corresponding to the maximum eigenvalue of each matrix, and converting the eigenvector to obtain a result; the quality reliability refers to the degree of confidence in the qualitative comparison process, or the degree of confidence in the source and calculation accuracy of quantitative data; the seventh step comprises 3 substeps:
7.1 establishing a block quality reliability comparison matrix M5 of the k-th evaluation factor by comparing the two blockskThe subscript k means the number corresponding to the evaluation factor,
7.2 solving the matrix M5kThe eigenvector corresponding to the largest eigenvalue is marked as w3kAccording to the stepsFive same methods for feature vector w3kCalculating to obtain the block goodness reliability distribution T under the k evaluation factork，
7.3 establishing a reliability distribution matrix T with good and bad degree, wherein the matrix T consists of elements TklThe lower index k means the number corresponding to the evaluation factor, the lower index l means the number corresponding to the block, and the kth column of the matrix is the reliability distribution T of the block goodness under the kth evaluation factorkAnd so on.
6. The shale gas block development potential evaluation method based on the relative preference index as claimed in claim 5, wherein: the block goodness and badness reliability comparison matrix establishing method of each evaluation factor is to compare the goodness and badness of the quality by taking two blocks each time, the accuracy of the degree is divided into two stages, the first stage accuracy is an upper, a middle and a lower three stages, and corresponds to numerical values 9, 6 and 3; if the precision needs to be improved to the second level, the precision is divided into nine levels, namely upper, middle, upper and lower levels, middle and middle, middle and lower levels, upper, lower, middle and lower levels, corresponding to a value of 9 to 1, if the block A is better than the block B in quality, and the degree is 9 to 1, the block B is worse than the block A in quality, namely the degree is 1 to 9, and the like; the logic consistency checking and judging process is to set the maximum characteristic value of the comparison matrix as lambdamaxThe comparison matrix is an n-order matrix when lambdamaxIf the difference from n is less than a given minimum value, the logical consistency check is passed; the conversion of the feature vector means that each element of the feature vector is divided by the sum of all elements, and the obtained new value is used as an evaluation factor and is compared with the element value at the same position in the weight distribution vector.
7. The shale gas block development potential evaluation method based on the relative preference index as claimed in claim 5, wherein: in the step eight, the method for calculating the relative preference index COI comprises the following steps: and multiplying the block goodness distribution of each factor by the block goodness reliability distribution according to the block correspondence, multiplying the obtained result by each evaluation factor proportion distribution, and then correspondingly accumulating the results of all the evaluation factors according to the blocks to obtain the COI.
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| 页岩气选区评价指标筛选及其权重确定方法——以四川盆地海相页岩为例;郭秀英 等;《地质勘探》;20151031;第35卷(第10期);57-64 * |
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