CN114049921B - Shale brittleness quantitative evaluation method based on weighting of sensitive factors in whole loading process - Google Patents
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Abstract
The invention discloses a shale brittleness quantitative evaluation method based on weighting of sensitive factors in the whole loading process, comprehensively considers brittleness sensitive main control factors at each stage in the whole shale loading damage process, and provides a stable and reliable reservoir brittleness quantitative evaluation method based on weighting of a mathematical statistics method. The method provided by the invention combines the advantages of a subjective weighting method and an objective weighting method, considers the primary comprehensive influence factors and the brittleness evaluation of secondary master control sensitive parameters in each stage of the whole rock loading damage process, establishes a brittleness evaluation model combining the subjective weighting and the objective weighting, takes the optimal relation matrix and the brittleness sensitive factors as input data, and can make adjustment according to the conditions of different work areas and actual fields through the calculation of a fuzzy analytic hierarchy process, an entropy weighting method and a CRITIC method without deviating from the information provided by the original data, so that the brittleness prediction result is more accurate, reliable theoretical support is provided for the horizontal fracturing of the shale reservoir, and the brittleness can be accurately and reliably characterized.
Description
Technical Field
The invention relates to the technical field of unconventional clean and environment-friendly energy exploration and development, in particular to a shale brittleness quantitative evaluation method based on weighting of sensitive factors in the whole loading process.
Background
Natural gas is a strategic clean environment-friendly energy source which is in short supply in China. According to the plan of 'strategy for energy production and consumption revolution' (2016-. According to the current natural gas yield in China, the method is an extremely difficult task. Therefore, the exploration and development force of the natural gas in China is improved to the height of guaranteeing the national energy safety. The exploration and development of the shale gas have a vital role in the backup guarantee of the yield of the natural gas and have an important strategic significance in promoting the clean and environment-friendly energy in China. The exploration and development of shale oil and gas are gradually developed in China from 2005, and the shale oil and gas exploration and development process comprises three stages, namely a preliminary preparation stage including geological condition research, dessert region evaluation and the like, a marine shale gas exploration and development stage, and a continental shale gas exploration and development stage. The Sichuan basin is the most potential area for developing shale gas in China, and six large and medium-sized shale gas fields of Fuling, Weirong, Changning, Weiyuan, Zhaotong and Yongchuan have been established through exploration and development for many years, so that a good situation is opened for commercial development of shale gas in China.
The brittleness is used as the basic characteristic of the rock and has important significance for oil and gas exploration and development. Brittleness is not only a key parameter for reservoir evaluation, but also closely related to the wall stability of a vertical well, fracture initiation and extension of hydraulic fracturing, communication of final net blocking and the like in the process of oil and gas field exploitation. The brittleness evaluation of reservoir rock as an engineering dessert is an important precondition for realizing the yield increase of an oil and gas reservoir by adopting a hydraulic fracturing method. Therefore, the brittleness evaluation is one of the important contents of shale reservoir evaluation and is an important parameter for selecting a favorable zone and a strong interval. The researchers at home and abroad research the brittleness, and provide different characterization modes, the brittleness characterization methods are various, and the brittleness characterization of the rock has higher complexity and limitation. Therefore, a reasonable brittleness evaluation mode is explored, and the method is worthy of study.
Analytic Hierarchy Process (AHP) is a new multi-attribute decision-making method proposed by the professor of saath for the united states department of defense to solve the problem of power distribution. AHP is widely used because it can quantitatively analyze qualitative problems by constructing a hierarchical structure. Is one of the most commonly applied scientific decision-making methods in academic research in related fields. In 1982, the AHP theory is introduced into China by professor Liubao of Tianjin university, and the like, and the theory rapidly obtains wide attention and application in various fields of social economy. However, due to the limitation of data and the subjective experience of experts, the generally constructed judgment matrix may not pass consistency check at one time, and experts need to modify the judgment matrix multiple times according to their intuition or experience until the requirement of consistency is met. The repeated adjustment not only has large calculation amount and low precision, but also is contrary to the original opinion of experts.
The conventional brittleness evaluation method is an isolated brittleness prediction formula proposed by different scholars based on elastic parameters, mineral components, stress-strain characteristics and the like. It is generally believed that high young's modulus and low poisson's ratio mean high brittleness, but in the actual production process it was found that in the lower range of young's modulus, it is more influenced by the nanopores of the rock, and low young's modulus also has better brittleness. It is generally considered that the higher the content of brittle minerals, the greater the brittleness of the rock, but when there is a relationship between several minerals in the rock, the law that brittle minerals are proportional to brittleness is not met. In addition, the quantitative relation between the content of brittle minerals in different rocks and the brittleness of the rocks and the mechanism thereof still remain problems to be determined at present.
Regarding brittleness evaluation, no brittleness evaluation method considering the sensitive factors of the whole loading damage process exists at present, and existing brittleness prediction methods mostly relate to elastic parameters, mineral components and stress-strain curves. Therefore, the invention not only quantitatively evaluates the brittleness from a new angle, but also provides a new direction of the brittleness prediction method.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a quantitative evaluation method of shale brittleness based on weighting of sensitive factors in the whole loading process, combines the advantages of a subjective weighting method and an objective weighting method, considers brittleness evaluation of primary comprehensive influence factors and secondary main control sensitive parameters in each stage of the whole rock loading damage process, can make adjustment according to the conditions of different work areas and actual sites, and does not deviate from information provided by original data, so that the brittleness prediction result is more accurate, and the problems mentioned in the background technology are solved.
In order to achieve the purpose, the invention provides the following technical scheme: the shale brittleness quantitative evaluation method based on the weighting of the sensitive factors in the whole loading process comprises the following steps:
the shale brittleness quantitative evaluation method based on the weighting of the sensitive factors in the whole loading process comprises the following steps:
s1, establishing a hierarchical multi-level structure model which takes each typical stage of the whole process of rock loading damage as a standard layer, takes brittle comprehensive influence factors of each stage as a first-stage index layer and main control sensitive parameters of the comprehensive influence factors as a second-stage index layer based on a fuzzy analytic hierarchy process, constructing an optimal relation matrix, converting the optimal relation matrix into a fuzzy consistent matrix, and determining weights of the fuzzy analytic hierarchy process;
s2, carrying out normalization processing on input data through a hierarchical multi-layer structure model established by the fuzzy analytic hierarchy process in the step S1, and solving information entropy by adopting an entropy weight method to obtain weight of the entropy weight method;
s3, calculating the weight of the CRITIC method according to the variability of the evaluation indexes and the conflict among the indexes on the normalized data;
s4, combining the weight of the fuzzy analytic hierarchy process, the weight of the entropy weight method and the weight of the CRITIC method to obtain the comprehensive index weight of brittleness evaluation, establishing a shale brittleness evaluation model based on weighting of sensitive factors in the whole loading process to obtain a shale brittleness index BI, and finishing quantitative brittleness evaluation.
Preferably, the rock in step S1 includes different depositional environment characteristics, sea phases with different formation periods, sea-land transition phases and land phases.
Preferably, the whole loading damage process in step S1 includes: work hardening damage closing stage, linear elastic damage stage, softening elastic-plastic damage stage and cracking damage stage.
Preferably, the brittleness comprehensive influence factors of each stage in the step S1 include a brittle mineral parameter, an elasticity parameter, a crack initiation parameter and a fracture parameter; the main control sensitive parameters of the comprehensive influence factors comprise quartz mineral content, carbonate mineral content, Young modulus, Poisson ratio, compressive strength and internal friction angle.
Preferably, the input data in step S2 includes experimental data and well logging data; the experimental data comprise temperature, pressure, saturation and orientation under different conditions; the logging data comprises array acoustic logging data and ECS logging data.
Preferably, the numerical values of pairwise comparison between the parameters in the optimal relationship matrix adopt a 0, 0.5 and 1 scaling method; the method for converting the optimal relationship matrix into the fuzzy consistent matrix comprises the following steps:
wherein r isikThe value of the ith row and the k column in the optimal relation matrix is obtained; m is the number of evaluation indexes; r is a radical of hydrogeni、rjSumming the optimal relationship matrix according to rows to obtain a matrix;
the method for determining the weight of the fuzzy analytic hierarchy process comprises the following steps:
wherein f isijTo obscure the values in row i and column j in the uniform matrix.
Preferably, the normalization process in step S2 includes forward normalization Y1And negative normalization of Y2：
Wherein x isijThe j index value of the ith evaluation object; max (x)j) And min (x)j) Evaluation in index jThe maximum and minimum of the object.
Preferably, the CRITIC method weight is calculated in step S3 based on the variability of the evaluation index and the conflict between the indexes, and the standard deviation is used to represent the variability of the indexes:
wherein, yijThe j index value of the ith evaluation object after normalization processing; n is the number of evaluation objects;
the indexes are represented by correlation coefficients:
wherein R isijAnd the correlation coefficient of the j index of the ith evaluation object.
Preferably, the step S4 of combining the weight of the fuzzy analytic hierarchy process, the weight of the entropy weight process, and the weight of the CRITIC process specifically includes: firstly, performing arithmetic mean combination on the two objective weights of an entropy weight method and a CRITIC method, and then correcting subjective weights by the objective weights by adopting a weighted average method, wherein the weighted average method is expressed as follows:
wherein, WSjIs an entropy weight; wCjIs CRITIC weight; wfjIs the fuzzy analytic hierarchy process weight; wKjIs an objective weight.
Preferably, the shale brittleness index BI is expressed as:
wherein, WbjThe weight of the j index; y isijThe index value of the j item of the ith evaluation object after normalization processing.
The invention has the beneficial effects that:
1) the invention considers the common influence on brittleness under the coupling condition of sensitive factors in the whole loading process, reduces the error of the mutation point caused by the dependence of single factor on the brittleness prediction in the past, ensures that the brittleness evaluation result is more reliable, and provides a new research direction for the brittleness evaluation method.
2) The invention adopts a three-scale fuzzy analytic hierarchy process, overcomes the complex processes of multiple times of adjustment, test, readjustment and retest when the judgment matrix is not consistent, and the result of repeated adjustment violates the original intention of subjective empowerment.
3) The invention adopts the combination of the objective weight and the CRITIC method, fully considers the entropy value, variability and conflict information of the original data, and combines the subjective weight and the objective weight, thereby not only considering the actual information in the original data, but also fully respecting the subjective view and the field requirement, overcoming the defects of subjective uncertainty in the fuzzy analytic hierarchy process and objective weight which can not reflect the experience in the actual work, leading the brittleness evaluation method to be more practical, and leading the prediction result to be more accurate.
4) The shale brittleness quantitative evaluation method based on weighting of sensitive factors in the whole loading process can not only combine experience of site construction and original data information, but also comprehensively consider the defects of the traditional method, can greatly improve the brittleness prediction accuracy, and provides a new research direction for the brittleness evaluation method.
Drawings
FIG. 1 is a flow chart of a quantitative evaluation method for shale brittleness in an embodiment of the present invention;
FIG. 2 is a diagram of a fuzzy hierarchy model in an embodiment of the present invention;
FIG. 3 is a graph comparing the results of the brittleness evaluation method of the present invention and the conventional brittleness evaluation method.
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.
In order to avoid the problems in the traditional Analytic Hierarchy Process (AHP), a Fuzzy Analytic Hierarchy Process (FAHP) is provided by introducing the concept of fuzzy mathematics into the analytic hierarchy process, and some scholars build fuzzy consistent arrays by using fuzzy number scales and completely use the original scores of experts, so that the complicated step of checking and modifying the consistency of common judgment matrixes is omitted. The method has the advantages that the fuzzy analytic hierarchy process can fully embody the opinions of experts, but the fuzzy analytic hierarchy process lacks data information, so an entropy weight method capable of representing the amount of original data information and a CRITIC method capable of representing data volatility and data correlation are introduced to represent objective weights, the subjective opinions are fully embodied without deviating from data information, and brittleness can be accurately predicted.
The entropy weight method and the CRITIC method belong to objective weighting methods. The entropy weight method judges the discrete degree of each index by data entropy value, the smaller the entropy value is, the larger the discrete degree of the index is, and the larger the distributed weight is; the CRITIC method is mainly based on the contrast strength (variability) and conflict among data, so as to determine the weight magnitude. The entropy weight method and the CRITIC method can fully display the information embodied by the actual data.
Referring to fig. 1-3, the present invention provides a technical solution: the shale brittleness quantitative evaluation method based on weighting of sensitive factors in the whole loading process is shown in a flow chart 1 and comprises the following steps:
s1, establishing a hierarchical multi-level structure model which takes each typical stage of the whole process of rock loading damage as a criterion layer, takes brittle comprehensive influence factors of each stage as a first-stage index layer and main control sensitive parameters of the comprehensive influence factors as a second-stage index layer based on a fuzzy analytic hierarchy process, establishing an optimal relation matrix as shown in figure 2, converting the optimal relation matrix into a fuzzy consistent matrix, and determining weights of the fuzzy analytic hierarchy process.
Further, the rock comprises different sedimentary environment characteristics, sea phases with different formation periods, sea-land transition phases and land phases.
Further, the whole process of loading injury comprises: work hardening damage closing stage, linear elastic damage stage, softening elastic-plastic damage stage and cracking damage stage.
Furthermore, the brittleness comprehensive influence factors of each stage comprise a brittleness mineral parameter, an elasticity parameter, a crack initiation parameter and a fracture parameter; the main control sensitive parameters of the comprehensive influence factors comprise quartz mineral content, carbonate mineral content, Young modulus, Poisson ratio, compressive strength and internal friction angle.
Performing value assignment by adopting an empirical method, comparing every two brittle factors, determining the relative importance degree of the brittle factors according to evaluation standards, and establishing an optimal relationship matrix of a primary index layer of the comprehensive influence factors, wherein the optimal relationship matrix is shown in a table 1;
TABLE 1 criterion layer optimal relationship matrix
In the secondary index layer, quartz is taken as a sensitive parameter more important than carbonate rock, the Young modulus and the Poisson ratio are taken as equally important, and accordingly an optimal relation matrix of the secondary index layer is established, as shown in Table 2,
TABLE 2 index layer optimal relationship matrix
The values of pairwise comparisons between parameters in the optimal relationship matrix are scaled by 0, 0.5, 1, as shown in table 3.
TABLE 3 judge matrix Scale and its implications
Converting the optimal relation matrix into a fuzzy consistent matrix through conversion, and calculating an objective weight value W of a fuzzy analytic hierarchy processFAHP(0.1170、0.0676、0.1575、0.1575、0.2756、0.2085)。
The method for converting the optimal relationship matrix into the fuzzy consistent matrix comprises the following steps:
wherein r isikThe value of the ith row and the k column in the optimal relation matrix is obtained; m is the number of evaluation indexes; r isi、rjSumming the optimal relationship matrix according to rows to obtain a matrix;
further, determining the weight of the j index fuzzy analytic hierarchy process as follows:
wherein, fijThe value of the ith row and the j column in the fuzzy consistent matrix is obtained; m is the number of evaluation indexes.
S2, carrying out normalization processing on the input data through the hierarchical multi-layer structure model established by the fuzzy analytic hierarchy process in the step S1, and solving the information entropy by adopting an entropy weight method to obtain the weight of the entropy weight method.
Further, the input data comprises experimental data and logging data; the experimental data comprise temperature, pressure, saturation and orientation under different conditions; the logging data comprises array acoustic logging data and ECS logging data.
Inputting actual data X of nine groups of shale samples with depth of 3800-S(0.0787、0.3479、0.1157、0.1745、0.1111、0.1721)。
The normalization process includes forward normalizing Y1And negative normalization of Y2：
Wherein x isijThe j index value of the ith evaluation object; max (x)j) And min (x)j) The maximum value and the minimum value of the evaluation object in the index j are respectively.
Wherein, the entropy value of the j index is:
wherein, yijThe j index value of the ith evaluation object after normalization processing; p is a radical ofijThe proportion of the ith sample in the jth index is the proportion of the ith sample in the jth index; n is the number of evaluation targets.
The entropy weight objective weight of the jth index is:
wherein e isjEntropy value of j index; m is the number of evaluation indexes.
And S3, calculating the weight of the CRITIC method according to the variability of the evaluation indexes and the conflict among the indexes on the normalized data.
Specifically, the data information amount of the data Y subjected to normalization processing is obtained through index variability and index conflict, and objective weight W of the CRITIC method is obtainedC(0.1263、0.1245、0.1246、0.2454、0.1250、0.2543)。
The index variability is expressed as standard deviation:
the index conflict is expressed by a correlation coefficient:
the CRITIC method objective weight of the j index is as follows:
wherein, yijThe j index value of the ith evaluation object after normalization processing; r isijThe j item as the ith evaluation objectTarget correlation coefficient (negative correlation coefficient absolute value); n is the number of evaluation targets.
S4, combining the weight of the fuzzy analytic hierarchy process, the weight of the entropy weight method and the weight of the CRITIC method to obtain the comprehensive index weight of brittleness evaluation, establishing a shale brittleness evaluation model based on weighting of sensitive factors in the whole loading process to obtain a shale brittleness index BI, and finishing quantitative brittleness evaluation.
In order to fully embody subjective and objective weights, the two objective weight methods (an entropy weight method and a CRITIC method) are combined in an arithmetic average way, then a weighted average method is adopted to correct the subjective weight by the objective weight, and finally a brittleness evaluation comprehensive index weight W is obtainedj(0.0764、0.1017、0.1206、0.2107、0.2073、0.2832)。
Arithmetic mean combination:
weighted average method:
and obtaining the shale brittleness index BI based on weighting of sensitive factors in the whole loading process by the established comprehensive evaluation model.
Wherein, WSjIs an entropy weight; wCjIs CRITIC weight; w is a group offjIs the fuzzy analytic hierarchy process weight; wKjIs an objective weight; wbjThe weight of the j index; y isijThe j index value of the ith evaluation object after normalization processing; m is the number of evaluation indexes.
Based on the principle steps, the invention also establishes an operation system which takes the optimal relation matrix and the brittleness sensitive factors as input data and finally outputs the shale brittleness quantitative evaluation weight value through the calculation of a fuzzy analytic hierarchy process, an entropy weight method and a CRITIC method.
Application instance validation
The shale data used in this example are from petrophysical experimental tests, including rock mechanics experiments, X-ray diffraction quantitative detection of whole rock minerals, etc., and the measured data include young's modulus, poisson's ratio, brittle minerals, internal friction angle, compressive strength. And according to the specific implementation steps, obtaining a brittleness quantitative evaluation model based on weighting of sensitive factors in the whole loading process. The evaluation results were compared with those of the conventional brittleness evaluation method (mineral composition method, Rickman method) as shown in fig. 3. As can be seen from fig. 3, the rock with the sequence numbers 1, 2, 3 and 9 in the brittleness evaluation method provided by the present invention is consistent with the previous and subsequent changes of the Rickman prediction result, which indicates that the elastic parameter of the rock greatly contributes to the brittleness evaluation result; the rock brittleness evaluation results of the numbers 4, 5, 6, 7 and 8 are inconsistent with the results of the traditional evaluation method, and the brittleness evaluation results are greatly influenced by the compressive strength or the cohesive force. The feasibility of the shale brittleness evaluation method based on weighting of sensitive factors in the whole loading process is explained to a certain extent, and the prediction result of the method is more accurate and reliable compared with that of the traditional method from another aspect.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing embodiments, or equivalents may be substituted for elements thereof.
Claims (9)
1. The shale brittleness quantitative evaluation method based on weighting of sensitive factors in the whole loading process is characterized by comprising the following steps:
s1, establishing a hierarchical multi-level structure model which takes each typical stage of the whole process of rock loading damage as a standard layer, takes brittle comprehensive influence factors of each stage as a first-stage index layer and main control sensitive parameters of the comprehensive influence factors as a second-stage index layer based on a fuzzy analytic hierarchy process, constructing an optimal relation matrix, converting the optimal relation matrix into a fuzzy consistent matrix, and determining weights of the fuzzy analytic hierarchy process;
s2, carrying out normalization processing on input data through a hierarchical multi-layer structure model established by the fuzzy analytic hierarchy process in the step S1, and solving information entropy by adopting an entropy weight method to obtain weight of the entropy weight method;
s3, calculating the weight of the CRITIC method according to the variability of the evaluation indexes and the conflict among the indexes on the normalized data;
s4, combining the weight of the fuzzy analytic hierarchy process, the weight of the entropy weight method and the weight of the CRITIC method to obtain a brittleness evaluation comprehensive index weight, establishing a shale brittleness evaluation model based on weighting of sensitive factors in the whole loading process to obtain a shale brittleness index BI, and finishing quantitative brittleness evaluation;
the step S4 of combining the weight of the fuzzy analytic hierarchy process, the weight of the entropy weight process, and the weight of the CRITIC process is specifically: the entropy weight method and the CRITIC method are combined by arithmetic mean, and then the subjective weight is corrected by the objective weight by adopting a weighted average method, wherein the weighted average method is expressed as follows:
wherein, WSjIs an entropy weight; w is a group ofCjIs CRITIC weight; wfjIs the fuzzy analytic hierarchy process weight; wKjIs an objective weight.
2. The shale brittleness quantitative evaluation method based on the weighting of the sensitive factors in the whole loading process according to claim 1, characterized in that: the rock in step S1 includes different depositional environment characteristics, sea phases with different formation periods, sea-land transition phases and land phases.
3. The shale brittleness quantitative evaluation method based on the weighting of the sensitive factors in the whole loading process according to claim 1, characterized in that: the whole process of loading the damage in the step S1 includes: work hardening damage closing stage, linear elastic damage stage, softening elastic-plastic damage stage and cracking damage stage.
4. The shale brittleness quantitative evaluation method based on the weighting of the sensitive factors in the whole loading process according to claim 1, characterized in that: the brittleness comprehensive influence factors of each stage in the step S1 comprise a brittleness mineral parameter, an elasticity parameter, a crack initiation parameter and a fracture parameter; the main control sensitive parameters of the comprehensive influence factors comprise quartz mineral content, carbonate mineral content, Young modulus, Poisson ratio, compressive strength and internal friction angle.
5. The shale brittleness quantitative evaluation method based on the weighting of the sensitive factors in the whole loading process according to claim 1, characterized in that: the input data in the step S2 comprises experimental data and logging data; the experimental data comprise temperature, pressure, saturation and orientation under different conditions; the logging data comprises array acoustic logging data and ECS logging data.
6. The shale brittleness quantitative evaluation method based on whole-process sensitive factor weighting is characterized in that: the numerical values of pairwise comparison between the parameters in the optimal relationship matrix adopt 0, 0.5 and 1 scaling methods; the method for converting the optimal relationship matrix into the fuzzy consistent matrix comprises the following steps:
wherein r isikThe value of the ith row and the k column in the optimal relation matrix is obtained; m is the number of evaluation indexes; r isi、rjSumming the optimal relationship matrix according to rows to obtain a matrix;
the method for determining the weight of the fuzzy analytic hierarchy process comprises the following steps:
wherein f isijTo obscure the values in row i and column j in the uniform matrix.
7. The shale brittleness quantitative evaluation method based on the weighting of the sensitive factors in the whole loading process according to claim 1, characterized in that: the normalization processing in step S2 includes forward normalization of Y1And negative normalization of Y2：
Wherein x isijThe j index value of the ith evaluation object; max (x)j) And min (x)j) The maximum value and the minimum value of the evaluation object in the index j are respectively.
8. The shale brittleness quantitative evaluation method based on whole-process sensitive factor weighting is characterized in that: the CRITIC method weight is calculated in step S3 based on the variability of the evaluation index and the conflict between the indexes, and the standard deviation is used to represent the variability of the indexes:
wherein, yijThe j index value of the ith evaluation object after normalization processing; n is the number of the evaluation objects;
the indexes are represented by correlation coefficients:
wherein R isijAnd the correlation coefficient of the j index of the ith evaluation object.
9. The shale brittleness quantitative evaluation method based on the weighting of the sensitive factors in the whole loading process according to claim 1, characterized in that: the shale brittleness index BI is expressed as:
wherein, WbjThe weight of the j index; y isijThe index value of the j item of the ith evaluation object after normalization processing.
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