Method and device for determining comprehensive index of oil and gas reservoir area selection evaluation

Abstract

The invention relates to the technical field of oil and gas reservoir favorable area optimization and resource evaluation, in particular to an oil and gas reservoir area selection evaluation comprehensive index determination method and device. The method comprises the steps of obtaining a standardized reference value corresponding to each index related to oil and gas reservoir selection area evaluation in a research area; according to the standardized reference value corresponding to each index, performing standardization processing on each index to obtain a standardized value corresponding to each index; selecting a weight assignment method, and determining a final weight value corresponding to each index according to the selected weight assignment method; and according to the standardized value and the final weight value, determining an evaluation comprehensive index corresponding to each sampling point in the research area. The problems that an existing oil and gas reservoir favorable area optimization method is weak in multi-index comprehensive evaluation capacity, a multi-factor superposition method can cause reduction of the identification degree under the condition of excessive indexes, evaluation integrity can be weakened by reducing the number of the indexes through dimension reduction, and reliability of an evaluation result can be reduced due to lack of systematic quantitative evaluation standards are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of the optimization of favorable areas of oil and gas reservoirs and resource evaluation, and particularly relates to a method and device for determining a comprehensive index for the evaluation of oil and gas reservoir selection areas. Background Art

[0002] The optimization of favorable areas is a necessary research content and working step in resource potential evaluation. It mainly focuses on the geological information and indicators included in aspects such as the gas generation capacity of source rocks, the effectiveness of reservoirs, the quality of preservation conditions, and recoverability. Combining the results and current situations of experimental analysis, production capacity testing, engineering conditions, etc., it explores the main controlling factors for oil and gas formation, and ultimately aims to clarify and predict the favorable enrichment locations and resource abundances of oil and gas under different geological backgrounds. In this process, not only a reasonable geological understanding is required as a theoretical basis, but also a large amount of different types of data is needed as support. Therefore, how to scientifically and effectively integrate relevant geological parameters objectively in a systematic manner and, based on this, conduct favorable area selection to point out the direction for subsequent exploration deployment becomes particularly important.

[0003] Currently, it has reached a consensus that the optimization of favorable areas of oil and gas reservoirs basically adopts the multi-factor superposition method. The difference lies in that there are significant differences among different units and institutions in determining the key parameters and thresholds for area selection and choosing the mathematical methods for data processing. At present, the research of Chinese scholars on the favorable area selection method mainly focuses on aspects such as the classification and integration of complex geological parameters, the constraint relationship and correlation degree, the control effect and level division of the hydrocarbon content of key indicators. For example, China University of Geosciences (Beijing) first proposed the parameter index system for shale gas area selection in China in 2010; Li Yanjun et al. established the correlation between 13 geological parameters and gas generation capacity, gas storage capacity, and easy exploitability, and focused on the core content of area selection evaluation through dimensionality reduction analysis; Li Wuguang (2011) comprehensively sorted and classified the indicators related to area selection after collecting the oil and gas field data in some areas of the Upper Yangtze region; and Liu Chaoying (2013) based on the "dual model" of resource value and enrichment probability, and the confidence level of its results is affected by the degree of data and information mastery, etc.

[0004] However, overall, there are still the following problems and deficiencies in the existing evaluation and interpretation means for oil and gas reservoir area selection parameters:

[0005] 1. At present, there are many published results on the evaluation parameter system and index hierarchy division method for area selection, while the research on the standardized values of key parameters and the comprehensive evaluation ability of multiple indicators is relatively weak.

[0006] 2. The favorable area selected by conventional multi-factor superposition method is the superposition area of ​​the selected evaluation index contour lines in the target area plane map. If the parameters are not integrated and comprehensively interpreted, it is bound to cause a high degree of clutter in the plane map when there are too many indicators, resulting in a decrease in the recognition of the results.

[0007] 3. Existing statistical methods such as grey correlation, clustering, hierarchical and factor analysis focus on the identification of geological parameter types and hierarchical division. They aim to reduce the evaluation dimension by establishing relationships with data such as oil and gas production, or by extracting and screening key indicators in different fields (such as sedimentation, geochemistry or structure) through similarity. This method can certainly reduce a certain amount of workload, but it cannot guarantee whether the geological information related to the selected area evaluation will be lost, which weakens the integrity of the evaluation.

[0008] 4. The types and levels of data used by different scholars or institutions vary greatly. Due to differences in parameter scale, size and accuracy, the final district selection results are difficult to compare horizontally. At the same time, the lack of systematic quantitative evaluation criteria will also reduce the reliability of the results to a certain extent.

[0009] The evaluation of the scale and quantity of oil and gas is a huge challenge. The ultimate goal is to carry out the optimization of favorable areas and the calculation of resource volume on the basis of geological knowledge, and to form numerical results of quantitative characterization. The content of oil and gas selection evaluation is rich, the data is diverse, and the information is complicated. Whether accurate, reasonable and scientific evaluation results can be submitted in the end is the most concerned issue for many scholars and institutions. Taking into account the actual needs and current status of oil and gas reserves and production increase and exploration and development, with systematicity, objectivity, reliability and guidance as the main purposes, and starting from the amount of information that the evaluation indicators can cover, it is an urgent research point and work task to propose a comprehensive evaluation index for selection. Summary of the invention

[0010] The present invention proposes a method and device for determining a comprehensive index for oil and gas reservoir selection evaluation, in order to solve the problems that the existing method for selecting favorable oil and gas reservoir areas has a weak ability to comprehensively evaluate multiple indicators, the multi-factor superposition method will lead to a decrease in recognition when there are too many indicators, and reducing the number of indicators by dimensionality reduction will weaken the integrity of the evaluation, and the lack of systematic quantitative evaluation criteria will reduce the reliability of the evaluation results.

[0011] According to one aspect of the present invention, a method for determining a comprehensive index for evaluating an oil and gas reservoir selection area is provided, comprising:

[0012] Obtain standardized reference values ​​corresponding to each indicator related to the evaluation of oil and gas reservoir selection in the study area;

[0013] Standardize each of the indicators according to the standardized reference value corresponding to each indicator to obtain the standardized value corresponding to each indicator;

[0014] Select a weight assignment method and determine the final weight value corresponding to each indicator according to the selected weight assignment method;

[0015] Determine the evaluation comprehensive index corresponding to each sampling point in the study area according to the standardized value and the final weight value.

[0016] Preferably, before obtaining the standardized reference value corresponding to each indicator related to the evaluation of hydrocarbon reservoir selection areas in the study area, determine the standardized reference value corresponding to each indicator in the study area. The method includes:

[0017] Divide each type of indicator into several levels and set the corresponding standardized reference value according to each level;

[0018] Determine the level where the value of each indicator is located, and the standardized reference value corresponding to this level is the standardized reference value corresponding to this indicator.

[0019] Preferably, the method for standardizing each indicator according to the standardized reference value corresponding to each indicator to obtain the standardized value corresponding to each indicator includes:

[0020] Judge whether the degree of superiority and inferiority of the interval values within the level where the indicator is located corresponds to a decreasing rule from large to small;

[0021] If so, determine the standardized value corresponding to the indicator according to the standardized reference value using the log2 function standardized value calculation formula under the decreasing rule. If not, determine the standardized value corresponding to the indicator according to the standardized reference value using the log2 function standardized value calculation formula under the increasing rule.

[0022] Preferably, the log2 function standardized value calculation formula under the decreasing rule is:

[0023]

[0024] In the formula: I d Is the standardized value corresponding to the indicator under the decreasing rule; N x Is the specific value of the indicator; N min Is the lower limit value of the node within the level interval where the specific value of the indicator is located; N max Is the upper limit value of the node within the level interval where the specific value of the indicator is located; W j Is the standardized reference value corresponding to the indicator in different level intervals; j is the level item number;

[0025] The standardized value calculation formula of the log2 function under the increasing rule is as follows:

[0026]

[0027] In the formula: I g is the standardized value corresponding to the index under the increasing rule; N x is the specific value of the index; N min is the lower limit value of the node within the level interval where the specific value of the index is located; N max is the upper limit value of the node within the level interval where the specific value of the index is located; W j is the standardized reference value corresponding to the index in different level intervals; j is the level item number.

[0028] Preferably, it is determined whether the interval values within the level corresponding to a certain index include both increasing and decreasing rule intervals from excellent to poor;

[0029] If so, any two values are selected from the two rule intervals and substituted into the corresponding formula (1) or (2) respectively to verify whether the magnitudes of the corresponding standardized values obtained conform to the excellent and poor rules, and the formula corresponding to the standardized value that conforms to the excellent and poor rules is selected to calculate the standardized value of the index.

[0030] Preferably, the weight assignment method at least includes:

[0031] One or several of multiple linear regression, grey relational analysis, fuzzy comprehensive evaluation, analytic hierarchy process, principal component analysis, factor analysis, and entropy weight method.

[0032] Preferably, the method for determining the evaluation comprehensive index corresponding to each sampling point in the study area according to the standardized value and the final weight value includes:

[0033] According to the standardized value and the final weight value corresponding to each index, the evaluation comprehensive index corresponding to the sampling point is calculated using formula (3);

[0034]

[0035] In the formula: I c is the evaluation comprehensive index; I ij is the standardized value at the j-th level of the i-th index; K i is the final weight value of the i-th index; m is the number of indices; i is the index item number; j is the level item number, and n is the number of levels.

[0036] Preferably, it further includes: obtaining the geological parameters of the study area, generating the geological map data of the study area according to the geological parameters and the evaluation comprehensive index, and performing grid processing on the geological map data;

[0037] Interpolate the gridded geological map data using the Kriging interpolation method, and re-evaluate and assign the comprehensive index to the sampled points after interpolation, and finally obtain a comprehensive plan view with the distribution characteristics of the comprehensive evaluation index information.

[0038] Preferably, it further includes: obtaining the gas content corresponding to each sampled point in the study area;

[0039] Establish the relationship between the comprehensive evaluation index corresponding to each sampled point in the study area and the gas content, and determine whether the correlation between the two reaches the predetermined requirement. If so, it indicates that the determined comprehensive evaluation index meets the requirements.

[0040] According to one aspect of the present invention, there is provided an apparatus for determining a comprehensive evaluation index for hydrocarbon reservoir selection area, including:

[0041] An acquisition unit for acquiring the standardized reference value corresponding to each index related to hydrocarbon reservoir selection area evaluation in the study area;

[0042] A standardization processing unit for standardizing each of the indexes according to the standardized reference value corresponding to each of the indexes to obtain the standardized value corresponding to each of the indexes;

[0043] A weight assignment unit for selecting a weight assignment method and determining the final weight value corresponding to each of the indexes according to the selected weight assignment method;

[0044] A comprehensive evaluation index determination unit for determining the comprehensive evaluation index corresponding to each sampled point in the study area according to the standardized value and the final weight value.

[0045] The present invention has at least the following beneficial effects:

[0046] The present invention proposes a method and apparatus for determining a comprehensive evaluation index for hydrocarbon reservoir selection area. By obtaining the standardized reference values of various indexes and determining the weight values, the comprehensive evaluation index corresponding to each index is obtained, and the favorable area selection work is carried out mainly based on the comprehensive evaluation index; it has the characteristics of simple, convenient, comprehensive, systematic, quantitative and reliable, and provides a reliable basis for subsequent resource potential calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings here are incorporated into the specification and form a part of this specification. These drawings show embodiments consistent with the present invention and are used together with the specification to explain the technical solutions of the present invention.

[0048] Figure 1 A flowchart showing a method for determining a comprehensive evaluation index for hydrocarbon reservoir selection area according to an embodiment of the present invention;

[0049] Figure 2 Shows the relationship diagram between the evaluation comprehensive index and the gas content according to an embodiment of the present invention;

[0050] Figure 3 Shows the optimal selection result map of the favorable areas of marine shale gas around the Sichuan Basin according to an embodiment of the present invention. Detailed implementation manners

[0051] Various exemplary embodiments, features and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings do not have to be drawn to scale unless otherwise specified.

[0052] The term "exemplary" used herein means "serving as an example, embodiment or illustration". Any embodiment described as "exemplary" herein does not have to be construed as superior or better than other embodiments.

[0053] The term "and / or" herein is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set composed of A, B, and C.

[0054] In addition, in order to better illustrate the present invention, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present invention can be implemented without some specific details. In some instances, methods, means, elements and circuits well known to those skilled in the art are not described in detail so as to highlight the gist of the present invention.

[0055] Figure 1 Shows the flowchart of the method for determining the evaluation comprehensive index of oil and gas reservoir selection area according to an embodiment of the present invention; Figure 2 Shows the relationship diagram between the evaluation comprehensive index and the gas content according to an embodiment of the present invention; Figure 3 Shows the optimal selection result map of the favorable areas of marine shale gas around the Sichuan Basin according to an embodiment of the present invention. As Figures 1-3As shown in the figure, a method for determining a comprehensive index for evaluating hydrocarbon reservoir selection areas includes the following steps: Step S01: Obtain the standardized reference values corresponding to each index related to hydrocarbon reservoir selection area evaluation in the study area; Step S02: Standardize each of the indexes according to the standardized reference values corresponding to each index to obtain the standardized values corresponding to each index; Step S03: Select a weight assignment method and determine the final weight values corresponding to each index according to the selected weight assignment method; Step S04: Determine the comprehensive evaluation index corresponding to each sampling point in the study area according to the standardized values and the final weight values.

[0056] The method for determining the comprehensive index for evaluating hydrocarbon reservoir selection areas provided by the embodiments of the present invention specifically includes the following steps:

[0057] Step S01: Obtain the standardized reference values corresponding to each index related to hydrocarbon reservoir selection area evaluation in the study area.

[0058] In the present invention, before obtaining the standardized reference values corresponding to each index related to hydrocarbon reservoir selection area evaluation in the study area, to determine the standardized reference values corresponding to each index in the study area, the method includes: Divide each type of index into several levels, and set the corresponding standardized reference values according to each level; According to the value corresponding to each index, determine the level it belongs to, and the standardized reference value corresponding to this level is the standardized reference value corresponding to this index.

[0059] In the embodiments of the present invention, on the basis of selecting the relevant evaluation parameter system (indexes), combined with the actual situation, existing work experience and understanding in the study area, divide the numerical range of each index into several levels, and give the standardized reference values W corresponding to each level interval according to the specific situation of the study area j , (j = 1, 2, 3, 4), that is, the weight values corresponding to each level interval; The levels divided in the embodiments of the present invention are 4 levels of "excellent, good, medium, poor", and the standardized reference values in different intervals corresponding to each level are shown in Table 1 below. In Table 1, "excellent" represents extremely good hydrocarbon accumulation conditions, and theoretically there are no flaws; "good" represents acceptable conditions, and generally does not affect hydrocarbon accumulation; "medium" represents general conditions, most likely to be poor, with risks; "poor" represents worrying conditions, and there is basically no possibility of hydrocarbon accumulation.

[0060] It should be noted that due to the particularity of large data dimensions such as burial depth, any change less than 100m is regarded as 100m for calculation. In addition, the standardized reference values of the optimal and worst levels differ by at least 10 times or more, so as to ensure that the comprehensive information index for evaluating the selection area (evaluation comprehensive index) obtained by the final addition and subtraction calculations is consistent with the conventional understanding and empirical phenomena.

[0061] Table 1: Division of standardized reference values for different level intervals

[0062]

[0063] If the specific value of a certain parameter falls within a certain level interval, the information quantity weight value corresponding to this parameter is the information quantity weight value of the level interval corresponding to this parameter.

[0064] Step S02: According to the standardized reference value corresponding to each of the indicators, perform standardization processing on each of the indicators to obtain the standardized value corresponding to each of the indicators.

[0065] In the present invention, the method for performing standardization processing on each indicator according to the standardized reference value corresponding to each indicator to obtain the standardized value corresponding to each indicator includes: determining whether the degree of superiority and inferiority of the interval values within the level where the indicator is located corresponds to a decreasing rule from large to small; if so, according to the standardized reference value, using the log2 function standardized value calculation formula under the decreasing rule, determine the standardized value corresponding to this indicator, if not, according to the standardized reference value, using the log2 function standardized value calculation formula under the increasing rule, determine the standardized value corresponding to this indicator.

[0066] In the present invention, the log2 function standardized value calculation formula under the decreasing rule is:

[0067]

[0068] In the formula: I d is the standardized value corresponding to the indicator under the decreasing rule; N x is the specific value of the indicator; N min is the lower limit value of the node within the level interval where the specific value of the indicator is located; N max is the upper limit value of the node within the level interval where the specific value of the indicator is located; W j is the standardized reference value corresponding to the indicator within different level intervals; i is the item number of the indicator; j is the item number of the level;

[0069] The log2 function standardized value calculation formula under the increasing rule is:

[0070]

[0071] In the formula: I g is the standardized value corresponding to the indicator under the increasing rule; N x is the specific value of the indicator; N min is the lower limit value of the node within the level interval where the specific value of the indicator is located; N max is the upper limit value of the node within the level interval where the specific value of the indicator is located; W jThe standardized reference values corresponding to the indicators within different level intervals; i is the item number of the indicator; j is the item number of the level.

[0072] In the embodiments of the present invention, the method does not subjectively screen, classify, or grade the geological parameters or data that have been mastered; instead, all parameters, indicators, or data considered available by practitioners are standardized and then the comprehensive information index for the evaluation of the selected area is calculated. In this process, to avoid error transmission caused by different representation methods, dimensions, scales, and precisions among parameters, based on the principle of information entropy, the log2 function is used to perform standardized processing (bit value conversion) on each numerical point of each type of parameter. The processed value can be understood as the amount of information carried by the geological parameter in participating in the work of favorable area selection. The larger this value, the greater the amount of information carried and the greater the contribution to the favorable area selection.

[0073] When the specific value of a certain indicator falls into a certain level interval corresponding to the indicator, if it is judged that the quality degree of the data corresponding to the decreasing trend of the values within this level interval from large to small is from excellent to poor, then the standardized processing is carried out using formula (1); if the quality degree of the data corresponding to the increasing trend of the values within this level interval from small to large is from excellent to poor, then the standardized processing is carried out using formula (2).

[0074] For example, for a certain indicator, the numerical intervals corresponding to its four levels are: level "excellent" corresponds to the interval 1.5 - 2.5, level "good" corresponds to the interval 2.5 - 3, level "medium" corresponds to the interval 3 - 4.5, level "poor" corresponds to the interval 4.5 - 5. The specific value of this indicator is 2.4, and the corresponding level is excellent. The values within this level interval are from large to small, that is, 2.5 - 1.5 corresponds to the quality degree from excellent to poor, so the corresponding values of the quality degree show a decreasing pattern. Then, when calculating the standardized value, formula (1) is used for calculation. Substitute the specific value N x = 2.4 and the standardized reference value W j = 15 of its corresponding "excellent" level into formula (1) to calculate the corresponding standardized value I g .

[0075] In the present invention, it is judged whether the interval values within the level corresponding to a certain indicator contain two regular intervals of increasing and decreasing from excellent to poor. If so, any two values are selected from these two regular intervals and substituted into the corresponding formula (1) or (2) respectively to verify whether the corresponding standardized values obtained conform to the excellent - poor rule, and the formula corresponding to the standardized value that conforms to the excellent - poor rule is selected to calculate the standardized value of the indicator.

[0076] In the embodiments of the present invention, among the actual various indicators, there are often cases where the numerical values of the level intervals of some indicators do not show their advantages and disadvantages in a monotonically increasing or decreasing manner. For shale gas accumulation, for example, the organic matter thermal evolution maturity R o If it is too small, the gas generation threshold has not been reached; if this value is too large, the gas generation potential will be insufficient due to the carbonization of organic matter. Taking the shale in the Niutitang Formation of the Lower Cambrian in the Upper Yangtze region as an example, theoretical calculations and measured data show that the range of the interval of the advantages and disadvantages of its R o value is shown in Table 2 below.

[0077] Table 2: Division of standardized reference values for different level intervals of R o

[0078]

[0079] For such a situation, the interval can be divided more finely. The two laws of increasing or decreasing included are divided into two intervals, and then according to the standardized reference values, test calculations are carried out according to formulas (1) and (2) to ensure that the standardized values calculated by formula (1) or (2) are consistent with the size law of the original data values.

[0080] The specific method is as follows: Judge whether the interval values within the level corresponding to an indicator include two law intervals of increasing and decreasing from good to bad. If so, randomly select two numerical values from these two law intervals and substitute them into the corresponding formula (1) or (2) respectively; that is, first, randomly select two numerical values from the interval with a decreasing law corresponding to the numerical values from good to bad in the two law intervals and substitute them into formula (1) to calculate two corresponding standardized values, and randomly select two numerical values from the interval with an increasing law corresponding to the numerical values from good to bad in the two law intervals and substitute them into formula (2) to calculate two corresponding standardized values;

[0081] Judge whether the degree of superiority or inferiority of the indicator value corresponding to the relatively larger standardized value among the two standardized values randomly selected from the decreasing law interval is relatively higher than that of another indicator value randomly selected from this decreasing law interval, and judge whether the degree of superiority or inferiority of the indicator value corresponding to the relatively larger standardized value among the two standardized values randomly selected from the increasing law interval is relatively higher than that of another indicator value randomly selected from this increasing law interval;

[0082] If the judgment result corresponding to the decreasing law interval is yes, when the value of the index is within the level that includes both increasing and decreasing law intervals from better to worse in terms of interval values, select formula (1) to calculate the corresponding standardized value; if the judgment result corresponding to the increasing law interval is yes, when the value of the index is within the level that includes both increasing and decreasing law intervals from better to worse in terms of interval values, select formula (2) to calculate the corresponding standardized value.

[0083] For example, in the difference level of R in Table 2 o , from better to worse, it includes two law intervals of increasing (>4.5) and decreasing (<1.5). Then, randomly select two values in the two law intervals. For example, select 4.6 and 4.7 in the increasing law interval, and select 1.4 and 1.3 in the decreasing law interval. Then substitute 4.6 and 4.7 into formula (2) to calculate the corresponding two standardized values I g1 and I g2 , substitute 1.4 and 1.3 into formula (1) to calculate the corresponding two standardized values I d1 and I d2 ;

[0084] Judge if I g1 >I g2 , and the superiority and inferiority degree of the value 4.6 corresponding to I g1 is relatively higher among the two values, that is, 4.6 is better than 4.7. Then when the specific value of R o falls into the difference level, use formula (2) to calculate its corresponding standardized value; judge if I d1 >I d2 , and the superiority and inferiority degree of the value 1.4 corresponding to I d1 is relatively higher among the two values, that is, 1.4 is better than 1.3. Then when the specific value of R o falls into the difference level, use formula (1) to calculate its corresponding standardized value.

[0085] When originally performing standardized calculation through a single log2 function, the calculation result may cause the final result to not conform to the actual law. For example, in this index, the values should correspond to the level from better to worse in the order of from large to small, but the calculated I value is the opposite. The present invention introduces the W j value, thereby ensuring that the final result conforms to the actual law of change of the superiority and inferiority level.

[0086] Step S03: Select a weight assignment method, and determine the final weight value corresponding to each said index according to the selected weight assignment method.

[0087] In the present invention, the weight assignment method at least includes one or more of multiple linear regression, grey relational analysis, fuzzy comprehensive evaluation, analytic hierarchy process (AHP), principal component analysis (PCA), factor analysis, and entropy weight method.

[0088] In the embodiments of the present invention, the principles and arithmetic expressions behind different mathematical methods for obtaining weight values largely limit their usage conditions and applicable ranges. Accordingly, the present invention sorts out common weight assignment methods and gives their related advantages and disadvantages, as shown in Table 3, providing a scientific theoretical basis for subsequent method selection. If you want to highlight the level and scale of parameters, methods such as AHP or PCA are preferred; if all are quantitative indicators at the same level, the entropy weight method is recommended so as to be coupled with the above-mentioned standardized assignment methods that tend to express information content.

[0089] Table 3: Principles, advantages and disadvantages of different methods for obtaining weights

[0090]

[0091]

[0092] The specific method to be selected is as follows:

[0093] The usage condition of multiple linear regression requires that the data all conform to the normal distribution and the sample size is at least 5 times the number of independent variables. Therefore, if the index data all conform to the normal distribution and the sample size is at least 5 times the number of independent variables, then multiple linear regression is selected;

[0094] The usage condition of grey relational analysis requires a linear relationship between the data and different parameter weights need to be given based on subjective judgment before calculating the correlation coefficient. Therefore, if there is a linear relationship between the index data and different parameter weights can be given before calculating the correlation coefficient, then grey relational analysis is selected;

[0095] The usage condition of fuzzy comprehensive evaluation is that when the data set is large, the membership degree weight coefficient will be reduced under the constraint that the weight vector sum is 1, blurring the final result. Therefore, if the data set is not larger than a predetermined size, then fuzzy comprehensive evaluation is selected;

[0096] The usage condition of the analytic hierarchy process (AHP) is that when there are too many indicators, it will affect the accuracy of eigenvalues and eigenvectors. Therefore, if the number of indicators is not larger than a predetermined number, then the analytic hierarchy process (AHP) is selected;

[0097] The usage condition of principal component analysis (PCA) is that it is difficult to guarantee the contribution rate of the principal components after dimensionality reduction; the number of principal components to be extracted needs to be defined artificially. Therefore, if the number of principal components to be extracted can be given and the influence of dimensionality reduction is not considered, then principal component analysis (PCA) is selected;

[0098] The usage conditions of factor analysis method are applicable to the conditions where the initial factors are all independent of each other and linearly combined. Therefore, if the initial factors are all independent of each other and linearly combined, the factor analysis method is selected;

[0099] The usage conditions of entropy weight method require that the data are independent of each other and there should be no mutual correlation, and the data dimensions or units should be consistent. Therefore, if the data are independent of each other, there is no mutual correlation, and the data dimensions or units are consistent, the entropy weight method is selected.

[0100] Select one or several of the above weight assignment methods according to specific needs, and determine the weight values corresponding to each index according to each weight assignment method respectively; then calculate the average value of the weight values corresponding to all weight assignment methods of each index, and this average value is the final weight value corresponding to this index. Among them, if among the several weight assignment methods selected, the weight value corresponding to a certain method is quite different from the weight values of other methods, the weight value result of this method can be excluded.

[0101] Step S04: Determine the evaluation comprehensive index corresponding to each sampling point in the study area according to the standardized value and the final weight value.

[0102] In the present invention, the method for determining the evaluation comprehensive index corresponding to each sampling point in the study area according to the standardized value and the final weight value includes: calculating the evaluation comprehensive index corresponding to the sampling point by using formula (3) according to the standardized value and the final weight value corresponding to each index;

[0103]

[0104] In the formula: I c is the evaluation comprehensive index (comprehensive evaluation information index); I ij is the standardized value of the i-th index of the sampling point at the j-th level; K i is the weight value (final weight value) of the i-th index of the sampling point obtained by calculation; m is the number of indexes of the sampling points participating in the evaluation of the selected area; i is the index item number; j is the level item number, and n is the number of levels.

[0105] In the embodiment of the present invention, substitute the standardized value and the final weight value calculated in steps S02 and S03 into formula (3) for comprehensive calculation, and finally obtain the comprehensive evaluation information index I c .

[0106] In the present invention, it further includes: obtaining geological parameters of the study area, generating geological map data of the study area according to the geological parameters and the comprehensive evaluation index, and performing grid processing on the geological map data; performing interpolation on the grid-processed geological map data by using the Kriging interpolation method, and re-assigning the comprehensive evaluation index to the sampling points after interpolation, and finally obtaining a comprehensive plan view with the distribution characteristics of the comprehensive evaluation index information.

[0107] In an embodiment of the present invention, geological parameter data required for generating a geological map of the study area is obtained, and according to the geological parameter data and the comprehensive evaluation index corresponding to each sampling point calculated in the above steps, geological map data with I c values is generated by using DoubleFox software.

[0108] Since the number of sampling points on the generated geological map data is small, it is necessary to perform interpolation on it by using the Kriging interpolation method according to the geological parameters. For the newly added sampling points after interpolation, it is necessary to re-assign values by the method of the above steps S01 - S04, that is, to recalculate the corresponding I c values.

[0109] Among them, for the ancient land or areas not within the value range, the whitening method is adopted, and standardized vector maps of different indicators are generated by using the Kriging difference method, and surface operations are performed in sequence, and then a comprehensive plan view with the distribution characteristics of the evaluation index information index I c can be obtained.

[0110] It should be noted that in the above process, the contour lines of the surface operation can be encrypted or redundant ones can be deleted as appropriate to avoid too much or too little data, thereby increasing the subsequent calculation workload or reducing the operation accuracy.

[0111] In the present invention, it further includes: obtaining the gas content corresponding to each sampling point in the study area; establishing the relationship between the comprehensive evaluation index corresponding to each sampling point in the study area and the gas content, and judging whether the correlation between the two reaches a predetermined requirement. If so, it means that the determined comprehensive evaluation index meets the requirements.

[0112] In an embodiment of the present invention, in order to verify the feasibility of the present invention, here, the optimization of favorable areas for marine shale gas in the periphery of the Sichuan Basin is taken as an example. First, find the standardized reference values corresponding to each index, and then perform standardization processing on all the indexes participating in the selection area evaluation by using formulas (1) and (2) to obtain the standardized values corresponding to each index.

[0113] Then, by selecting and focusing on the control effects of different geological parameters on gas content, three weight assignment methods, namely multiple linear regression, grey correlation, and information entropy weight method (entropy weight method), which mainly consider the correlation between parameters and the variability of the data itself, are used to calculate the weight values corresponding to each index. Since the calculation results of multiple linear regression are quite different from those of the other methods and do not meet its application conditions (the total number of samples is 5 times or more than the number of independent variables), it is excluded. The results of the remaining weight assignment methods are averaged to obtain the final weight values corresponding to each index based on geological understanding analysis. The specific results are shown in Table 4.

[0114] Table 4: Weight Distribution of Key Parameters in the Selected Area

[0115]

[0116] Finally, according to the final weight values and the standardized values, the comprehensive information index I at each sampling point position is calculated using formula (3). c . The specific calculation results are shown in Table 5 below. The values in parentheses in Table 5 are the standardized values corresponding to the index:

[0117] Table 5: Data Statistics and Standardized Assignment of Evaluation Indexes for Some Wells in the Study Area

[0118]

[0119]

[0120] The calculated comprehensive information index I c is fitted with the measured total gas content data of each sampling point. As Figure 2 shown, it can be found that there is a significant correlation between the two (sig≈0 << 0.05); secondly, the model also has a high determination coefficient (R 2 > 0.7), meeting the predetermined requirements, indicating that the method of the present invention has strong rationality and scientificity.

[0121] In this process, since the parameters of each geodetic coordinate point in the block to be evaluated are not complete, the Kriging interpolation module of DoubleFox software can be used to numerically predict and calculate the parameters participating in the selected area evaluation in the unexplored block without data, that is, interpolation. In this way, the calculation of I c can be carried out under a large amount of data capacity, and finally the I c plane distribution map of the block to be evaluated is obtained, as Figure 3 shown; in Figure 3 , the areas circled by thick lines correspond to relatively high I c values, which can be used as the final preferred result of the favorable area in the study area.

[0122] The favorable blocks selected based on the present invention are highly consistent with the current exploration and development understanding. Accordingly, it can be shown that the above-mentioned system and method have strong rationality, scientificity, accuracy and popularizability.

[0123] It can be understood that, without violating the principle logic, the above-mentioned method embodiments mentioned in the present invention can be combined with each other to form combined embodiments. Due to space limitations, the present invention will not elaborate further.

[0124] The execution subject of the method for determining the comprehensive evaluation index for hydrocarbon reservoir selection can be a device for determining the comprehensive evaluation index for hydrocarbon reservoir selection. For example, the method for determining the comprehensive evaluation index for hydrocarbon reservoir selection can be executed by a terminal device, a server or other processing devices. Among them, the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the method for determining the comprehensive evaluation index for hydrocarbon reservoir selection can be implemented by a processor invoking computer-readable instructions stored in a memory.

[0125] Those skilled in the art can understand that in the above method of the specific implementation manner, the writing order of each step does not mean a strict execution order and does not constitute any limitation to the implementation process. The specific execution order of each step should be determined according to its function and possible internal logic.

[0126] The present invention also provides a device for determining the comprehensive evaluation index for hydrocarbon reservoir selection, including: an acquisition unit, configured to acquire a standardized reference value corresponding to each index related to hydrocarbon reservoir selection evaluation in a study area; a standardization processing unit, configured to perform standardization processing on each of the indexes according to the standardized reference value corresponding to each index to obtain a standardized value corresponding to each index; a weight assignment unit, configured to select a weight assignment method and determine a final weight value corresponding to each index according to the selected weight assignment method; and an evaluation comprehensive index determination unit, configured to determine an evaluation comprehensive index corresponding to each sampling point in the study area according to the standardized value and the final weight value.

[0127] In some embodiments, the functions or modules and units included in the device provided in the embodiments of the present invention can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be elaborated here.

[0128] The present invention provides a calculation formula for the standardization processing of geological parameters based on the principle of information entropy, as well as an optimal selection suggestion for different weight assignment methods. On this basis, a comprehensive evaluation information index I for the evaluation of oil and gas reservoir selection areas is further proposed. c And based on this, the work of optimizing favorable areas is carried out. Through this invention, a comprehensive evaluation information index for optimizing favorable areas that is simple, convenient, comprehensive, systematic and quantitatively reliable can be formed, providing guiding suggestions for subsequent resource potential calculation.

[0129] The present invention has at least the following advantages: (1) The smaller the entropy value, the less information it carries. Based on this theory, the present invention starts from the information carried by the evaluation index and gives a calculation formula for data standardization processing based on the principle of information entropy. This can, on the basis of a certain geological understanding, weaken the influence of unfavorable factors on the final selection result through a quantitative method, ensuring the objectivity of the work of optimizing favorable areas for oil and gas reservoirs; (2) The comprehensive evaluation information index I proposed by the present invention c does not require hierarchical division, deletion or screening of geological parameters before calculation. This not only ensures the integrity and systematicness of the geological information participating in the selection area evaluation, enhances the relevance between data statistics and geological understanding, but also considers the influence of the relevance and restriction of multiple indicators in the time and space range on the final oil and gas enrichment. It improves the scientificity and rationality of the favorable area optimization result; (3) The present invention adopts the Kriging interpolation principle to depict, predict and extrapolate the I c index within the whole region and form an intuitive planar map. This method not only fills the blank area lacking data, but also can adjust the result accuracy and range according to needs, improving the integrity and operability of the evaluation process.

[0130] The above has described the embodiments of the present invention. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications or improvements to the technology in the market, or to enable other ordinary skill in the art in the technical field to understand the disclosed embodiments.

Claims

1. A method for determining a comprehensive index for evaluating and selecting oil and gas reservoirs, characterized in that Including: Obtaining the standardized reference value corresponding to each index related to the evaluation of hydrocarbon reservoir selection area in the study area; According to the standardized reference value corresponding to each index, performing standardized processing on each index to obtain the standardized value corresponding to each index; Selecting a weight assignment method, and determining the final weight value corresponding to each index according to the selected weight assignment method; According to the standardized value and the final weight value, determining the evaluation comprehensive index corresponding to each sampling point in the study area.

2. The method for determining the comprehensive index for evaluating hydrocarbon reservoir selection areas according to claim 1, wherein, Before obtaining the standardized reference value corresponding to each index related to the evaluation of hydrocarbon reservoir selection area in the study area, determining the standardized reference value corresponding to each index in the study area, and the method includes: Dividing each type of index into several levels, and setting the corresponding standardized reference value according to each level; According to the value corresponding to each index, determining the level it belongs to, and the standardized reference value corresponding to this level is the standardized reference value corresponding to this index.

3. The method for determining the comprehensive index for evaluating hydrocarbon reservoir selection areas according to claim 2, characterized in that, The method for performing standardized processing on each index according to the standardized reference value corresponding to each index to obtain the standardized value corresponding to each index includes: Judging whether the degree of superiority and inferiority of the interval values within the level where the index is located corresponds to a decreasing law from large to small; If so, according to the standardized reference value, using the log2 function standardized value calculation formula under the decreasing law, determining the standardized value corresponding to this index; if not, according to the standardized reference value, using the log2 function standardized value calculation formula under the increasing law, determining the standardized value corresponding to this index.

4. The method for determining the comprehensive evaluation index for hydrocarbon reservoir selection area according to claim 3, characterized in that: The log2 function standardized value calculation formula under the decreasing law is: Where: I d is the standardized value corresponding to the index under the decreasing law; N x is the specific value of the index; N min is the lower limit value of the node within the level interval where the specific value of the index is located; N max is the upper limit value of the node within the level interval where the specific value of the index is located; W j is the standardized reference value corresponding to the index in different level intervals; j is the level item number; The log2 function standardized value calculation formula under the increasing law is: Where: I g is the standardized value corresponding to the indicator under the increasing rule; N x is the specific value of the indicator; N min is the lower limit value of the node within the level interval where the specific value of the indicator is located; N max is the upper limit value of the node within the level interval where the specific value of the indicator is located; W j is the standardized reference value corresponding to the indicator in different level intervals; j is the level item number.

5. The method for determining the comprehensive evaluation index for hydrocarbon reservoir selection area according to claim 4, characterized in that: Judging whether the interval values within the level corresponding to a certain index include two regular intervals of increasing and decreasing from excellent to inferior; If so, randomly select two values from these two regular intervals and substitute them into the corresponding formula (1) or (2) respectively to verify whether the corresponding standardized values obtained conform to the superiority and inferiority rules, and select the formula corresponding to the standardized value that conforms to the superiority and inferiority rules to calculate the standardized value of this index.

6. The method for determining the comprehensive index for evaluating hydrocarbon reservoir selection areas according to claim 1, characterized in that The weight assignment method at least includes: One or several of multiple linear regression, grey relational analysis, fuzzy comprehensive evaluation, analytic hierarchy process, principal component analysis, factor analysis, and entropy weight method.

7. The method for determining the comprehensive index for evaluating hydrocarbon reservoir selection areas according to claim 1, wherein, The method for determining the evaluation comprehensive index corresponding to each sampling point in the study area according to the standardized value and the final weight value includes: According to the standardized value and the final weight value corresponding to each index, using formula (3) to calculate the evaluation comprehensive index corresponding to the sampling point; Where: I c is the evaluation comprehensive index; I ij is the standardized value at the j-th level of the i-th indicator; K i is the final weight value of the i-th indicator; m is the number of indicators; i is the item number of the indicator; j is the item number of the level, and n is the number of levels.

8. The method for determining the comprehensive index for evaluating hydrocarbon reservoir selection areas according to any one of claims 1-7, characterized in that It also includes: Obtaining the geological parameters of the study area, generating the geological map data of the study area according to the geological parameters and the evaluation comprehensive index, and performing grid processing on the geological map data; Interpolate the geologic map data after grid processing using the Kriging interpolation method, and re-evaluate and assign the comprehensive index to the sampling points after interpolation to finally obtain a comprehensive plan view with the distribution characteristics of the comprehensive evaluation index information.

9. The method for determining the comprehensive index for evaluating oil and gas reservoir selection areas according to any one of claims 1 to 7, characterized in that It also includes: Obtain the gas content corresponding to each sampling point in the study area; Establish the relationship between the comprehensive evaluation index corresponding to each sampling point in the study area and the gas content, and determine whether the correlation between the two meets the predetermined requirements. If so, it indicates that the determined comprehensive evaluation index meets the requirements.

10. An apparatus for determining a comprehensive index for evaluating and selecting oil and gas reservoirs, characterized in that It includes: An acquisition unit for obtaining the standardized reference value corresponding to each index related to the evaluation of hydrocarbon reservoir selection areas in the study area; A standardization processing unit for standardizing each of the indicators according to the standardized reference value corresponding to each of the indicators to obtain the standardized value corresponding to each of the indicators; A weight assignment unit for selecting a weight assignment method and determining the final weight value corresponding to each of the indicators according to the selected weight assignment method; A comprehensive evaluation index determination unit for determining the comprehensive evaluation index corresponding to each sampling point in the study area according to the standardized value and the final weight value.

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