Method, device and equipment for determining shale oil dessert

Abstract

The invention discloses a shale oil dessert determination method, device and equipment. The method comprises the following steps: acquiring a reservoir identification model pre-constructed for a target reservoir; based on the membership relationship of the elements of each level in the reservoir identification model and a preset importance degree discrimination index, determining the correlation degree between the elements of each level in the reservoir identification model; calculating the importance coefficient of each reservoir parameter relative to the sweet spot index based on the correlation degree; obtaining numerical values corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir; and determining a sweet spot index of the target reservoir based on the numerical value corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter so as to perform shale oil sweet spot discrimination on the target reservoir. Based on the pre-constructed reservoir identification model, the correlation degree between the hierarchical elements is calculated to determine the importance coefficient of the reservoir parameters, and the sweet spot index is determined based on the importance coefficient, so that a more accurate and reliable result is obtained.

Description

Technical Field

[0001] The present application relates to the field of petroleum exploration technology, and in particular to a method, device and equipment for determining shale oil sweet spots. Background Art

[0002] Shale oil is a type of petroleum stored in shale formations rich in organic matter and mainly with nano-scale pores. It generally exists in adsorbed and free forms. Shale oil accumulation has certain particularities. It is mainly found in continental strata and has characteristics such as multi-cyclic structural evolution and large lithofacies changes, as well as evaluation difficulties. Currently, the commonly used methods for determining sweet spots in shale oil reservoirs mainly include the following methods:

[0003] (1) Based on production data and evaluation parameters, the principal component analysis method is used to select evaluation indicators, and a comprehensive evaluation model and chart for shale oil reservoir quality are constructed based on classification and discriminant analysis;

[0004] (2) Based on the field experimental data of the oil field and comprehensive reference to various parameters, a set of multi-parameter "sweet spot" distribution prediction technology covering geology, well logging, geophysical exploration and engineering has been formed;

[0005] (3) The shale reservoir quality (RQ) and completion quality (CQ) are evaluated by setting cutoff values ​​for parameters such as porosity, saturation, permeability, and formation stress. The evaluation results of reservoir quality and completion quality are divided into two levels. Then, a new evaluation parameter RCQ is formed by combining the results of the two to perform a graded evaluation of the reservoir. The final evaluation results are divided into four levels.

[0006] It can be seen that the current research on the sweet spot determination method of shale oil reservoirs is more focused on the calculation of parameters such as physical properties and brittleness, lithology identification, etc., while in the grading and evaluation of shale oil reservoir sweet spots, most of them are directly using geochemical and physical parameters or a single logging curve to classify sweet spot reservoirs by setting cutoff values. However, the above methods refer to less information and data, and the reliability of the results is low, and they cannot accurately and reliably determine the shale oil sweet spots. Summary of the invention

[0007] The purpose of the embodiments of the present application is to provide a method, device and equipment for determining shale oil sweet spots, so as to solve the problem that shale oil sweet spots cannot be accurately and reliably determined.

[0008] In order to solve the above technical problems, the first aspect of this specification provides a method for determining a shale oil sweet spot, comprising:

[0009] Acquire a reservoir identification model pre-constructed for a target reservoir, wherein the reservoir identification model includes a plurality of reservoir parameters related to determining a sweet spot index of the target reservoir, and a multi-level structural relationship between the plurality of reservoir parameters and the sweet spot index of the target reservoir, wherein each level includes at least one element;

[0010] Determining the degree of association between the elements at each level in the reservoir identification model based on the affiliation relationship between the elements at each level in the reservoir identification model and a preset importance discrimination index;

[0011] Based on the correlation degree, calculating the importance coefficient of each reservoir parameter relative to the sweet spot index;

[0012] Acquire numerical values ​​corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir;

[0013] Based on the numerical values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, the sweet spot index of the target reservoir is determined to identify the shale oil sweet spot of the target reservoir.

[0014] In some embodiments, the reservoir identification model is constructed by:

[0015] Acquire multiple reservoir attribute parameters and well logging information of the target reservoir;

[0016] Based on a plurality of preset evaluation indicators and the well logging information, some reservoir attribute parameters are selected from the plurality of reservoir attribute parameters as reservoir parameters in a reservoir identification model, and a subordinate relationship between the reservoir parameters and the plurality of evaluation indicators is determined, wherein the plurality of evaluation indicators are determined based on the sweet spot indicators;

[0017] Based on the affiliation and the reservoir parameters, the reservoir identification model is constructed.

[0018] In some embodiments, based on a plurality of preset evaluation indicators and the well logging information, some reservoir attribute parameters are selected from the plurality of reservoir attribute parameters as reservoir parameters in the reservoir identification model, and the affiliation between the reservoir parameters and the plurality of evaluation indicators is determined, including:

[0019] Based on the well logging information, determining a whitening weight function of each evaluation index;

[0020] Based on the whitening weight function, determining whitening number information of the plurality of reservoir attribute parameters under each evaluation index;

[0021] Based on the whitening number information, the reservoir parameter is selected from the plurality of reservoir attribute parameters, and the affiliation between the reservoir parameter and the evaluation index is determined.

[0022] In some embodiments, the reservoir identification model has a three-layer structure, which includes from top to bottom: a target layer composed of sweet spot indicators, a quasi-measurement layer composed of multiple evaluation indicators, and a solution layer composed of multiple reservoir parameters. Any one of the reservoir parameters is subordinate to at least one evaluation indicator, and the multiple evaluation indicators are subordinate to the sweet spot indicators.

[0023] In some embodiments, the reservoir parameters include porosity, oil saturation, reservoir recoverability index, organic carbon content and brittleness index, and the preset multiple evaluation indicators include reservoir physical properties, source rock characteristics and engineering brittleness;

[0024] The porosity, the saturation content and the reservoir recoverability index belong to the reservoir physical properties, the organic carbon content belongs to the source rock characteristics, and the brittleness index belongs to the engineering brittleness.

[0025] In some embodiments, based on the affiliation of the elements at each level in the reservoir identification model and a preset importance discrimination index, determining the degree of association between the elements at each level in the reservoir identification model includes:

[0026] Based on the preset importance discrimination index and the affiliation, a judgment matrix of the reservoir identification model is constructed, wherein any one element in the judgment matrix is ​​used to characterize the relative importance of the influence of two elements in the current level that belong to the same target element in the adjacent level on the target element;

[0027] A consistency check is performed on the judgment matrix, and when it is determined that the judgment matrix passes the consistency check, the judgment matrix is ​​used as the degree of association.

[0028] In some embodiments, based on the correlation degree, calculating the importance coefficient of each reservoir parameter relative to the sweet spot index includes:

[0029] Based on the hierarchical structure in the reservoir identification model and the degree of association, the weight coefficient corresponding to each reservoir parameter is determined, and the weight coefficient corresponding to each reservoir parameter is used as the importance coefficient.

[0030] In some embodiments, based on the values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, the sweet spot index of the target reservoir is determined to identify the shale oil sweet spot of the target reservoir, including:

[0031] Based on the importance coefficient of each reservoir parameter, weighted summation is performed on the values ​​of the plurality of reservoir parameters to obtain the sweet spot index;

[0032] The sweet spot level of the target reservoir is determined based on the sweet spot index and the corresponding relationship between the preset sweet spot level and the sweet spot index interval.

[0033] In some embodiments, obtaining values ​​corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir includes:

[0034] The well logging information of the target reservoir is obtained, and based on the well logging information and a preset calculation method for each reservoir parameter, a numerical value corresponding to each reservoir parameter is determined.

[0035] A second aspect of the present specification provides a device for determining a shale oil sweet spot, comprising:

[0036] A model acquisition module, used to acquire a reservoir identification model pre-constructed for a target reservoir, wherein the reservoir identification model includes a plurality of reservoir parameters related to determining a sweet spot index of the target reservoir, and a multi-level structural relationship between the plurality of reservoir parameters and the sweet spot index of the target reservoir, wherein each level includes at least one element;

[0037] A qualitative analysis module, used to determine the degree of association between the elements at each level in the reservoir identification model based on the affiliation of the elements at each level in the reservoir identification model and a preset importance discrimination index;

[0038] A quantitative calculation module, used for calculating the importance coefficient of each reservoir parameter relative to the sweet spot index based on the correlation degree;

[0039] A data acquisition module, used to acquire numerical values ​​corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir;

[0040] The sweet spot determination module is used to determine the sweet spot index of the target reservoir based on the numerical value corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, so as to identify the shale oil sweet spot of the target reservoir.

[0041] The third aspect of this specification provides an electronic device, comprising: a memory and a processor, the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor implements the steps of any one of the methods described in the first aspect by executing the computer instructions.

[0042] A fourth aspect of the present specification provides a computer storage medium, wherein the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the steps of any one of the methods described in the first aspect are implemented.

[0043] The method for determining shale oil sweet spots provided in the embodiments of the present specification obtains a reservoir identification model pre-constructed for a target reservoir, the reservoir identification model includes multiple reservoir parameters related to determining the sweet spot index of the target reservoir, and a multi-level structural relationship between the multiple reservoir parameters and the sweet spot index of the target reservoir, and any level includes at least one element; based on the affiliation of the elements of each level in the reservoir identification model and a preset importance discrimination index, the degree of association between the elements of each level in the reservoir identification model is determined; based on the degree of association, the importance coefficient of each reservoir parameter relative to the sweet spot index is calculated; the numerical values ​​corresponding to the multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir are obtained; based on the numerical values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, the sweet spot index of the target reservoir is determined to discriminate the shale oil sweet spot of the target reservoir. In the present application, the sweet spot indicators are calculated based on a pre-established reservoir identification model that characterizes the hierarchical structural relationship between the reservoir parameters and the sweet spot indicators of the target reservoir, so that a correspondence between the reservoir parameters and the sweet spot indicators with higher practicality, systematicity and authenticity can be obtained, providing a basis for the accurate calculation of the sweet spot indicators; and based on the affiliation of the elements of each level in the reservoir identification model and the preset importance discrimination index, the present application can realize the qualitative analysis of the elements of each level for the sweet spot indicators, and obtain the degree of correlation between the elements of each level in the reservoir identification model, and then based on the degree of correlation, it can realize the quantitative evaluation of the reservoir parameters of the target reservoir, and obtain the importance coefficient of each reservoir parameter relative to the sweet spot indicator, and then combine the importance coefficient and numerical value of each reservoir parameter to determine the sweet spot indicator of the target reservoir, which can more accurately and reliably realize the discrimination of shale oil sweet spots. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some implementation methods recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 Shown is a schematic diagram of a method for determining a shale oil sweet spot provided in an embodiment of the present application;

[0046] Figure 2 Shown is a schematic diagram of a reservoir identification model provided in an embodiment of the present application;

[0047] Figure 3 Shown is a schematic diagram of a device for determining a shale oil sweet spot provided in an embodiment of the present application;

[0048] Figure 4Shown is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0050] As mentioned above, current research on the sweet spot determination method of shale oil reservoirs is more focused on the calculation of parameters such as physical properties and brittleness, lithology identification, etc., while in the grading and evaluation of shale oil reservoir sweet spots, the sweet spot reservoir categories are classified by setting cutoff values ​​for parameters. However, this method has less reference information and data, and the reliability of the results is low, making it impossible to accurately and reliably determine the shale oil sweet spots.

[0051] In order to solve the above problems, an embodiment of the present application provides a method for determining a shale oil sweet spot, which specifically includes: obtaining a reservoir identification model pre-constructed for a target reservoir, the reservoir identification model including multiple reservoir parameters related to determining the sweet spot index of the target reservoir, and a multi-level structural relationship between the multiple reservoir parameters and the sweet spot index of the target reservoir, and any level includes at least one element; based on the affiliation of the elements of each level in the reservoir identification model and a preset importance judgment index, determining the degree of association between the elements of each level in the reservoir identification model; based on the degree of association, calculating the importance coefficient of each reservoir parameter relative to the sweet spot index; obtaining the numerical values ​​corresponding to the multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir; based on the numerical values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, determining the sweet spot index of the target reservoir, so as to perform shale oil sweet spot judgment on the target reservoir.

[0052] In the present application, the sweet spot indicators are calculated based on a pre-established reservoir identification model that characterizes the hierarchical structural relationship between the reservoir parameters and the sweet spot indicators of the target reservoir, so that a correspondence between the reservoir parameters and the sweet spot indicators with higher practicality, systematicity and authenticity can be obtained, providing a basis for the accurate calculation of the sweet spot indicators; and based on the affiliation of the elements of each level in the reservoir identification model and the preset importance discrimination index, the present application can realize the qualitative analysis of the elements of each level for the sweet spot indicators, and obtain the degree of correlation between the elements of each level in the reservoir identification model, and then based on the degree of correlation, it can realize the quantitative evaluation of the reservoir parameters of the target reservoir, and obtain the importance coefficient of each reservoir parameter relative to the sweet spot indicator, and then combine the importance coefficient and numerical value of each reservoir parameter to determine the sweet spot indicator of the target reservoir, which can more accurately and reliably realize the discrimination of shale oil sweet spots.

[0053] It can be understood that the above method provided in the embodiment of the present application can be applied to electronic devices, and the electronic device can refer to an electronic device with data calculation, processing and storage capabilities. The electronic device can be a terminal such as a PC (Personal Computer), a tablet computer, a smart phone, a wearable device, an intelligent robot, etc.; it can also be a server. Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. This application does not limit this.

[0054] The following is an introduction to the method for determining the shale oil sweet spot provided in the embodiments of the present application in conjunction with the accompanying drawings.

[0055] Figure 1 FIG. 1 is a schematic diagram of a method for determining a shale oil sweet spot provided in an embodiment of the present application. Figure 1 As shown, the method may include:

[0056] S101: Acquire a reservoir identification model pre-constructed for a target reservoir.

[0057] The reservoir identification model includes a plurality of reservoir parameters related to determining the sweet spot index of the target reservoir, and a multi-level structural relationship between the plurality of reservoir parameters and the sweet spot index of the target reservoir, and any level includes at least one element.

[0058] It can be understood that the reservoir identification model is aimed at calculating the sweet spot index, and multiple reservoir parameters of the target reservoir are used as the basic elements involved in calculating the sweet spot index. There can be a multi-layer structure between the basic elements and the target, and there is a subordinate relationship between the lower-layer elements and the corresponding upper-layer elements, that is, the lower-layer elements belong to at least any one of the elements in the corresponding upper layer. The embodiment of the present application takes into account that directly judging the relationship between the reservoir parameters and the sweet spot index may lead to greater complexity and uncertainty. By judging the effect of the lower-layer elements on the upper-layer elements through the hierarchical structure, a more accurate and reliable hierarchical structure relationship between the reservoir parameters and the sweet spot index can be obtained, which provides a basis for the accurate calculation of the subsequent sweet spot index.

[0059] It can be understood that the reservoir identification model can be constructed by analyzing the correlation between the reservoir attribute parameters of the target reservoir and combining them with the evaluation index of the preset sweet spot index. Among them, the reservoir attribute parameters can be understood as basic attribute parameters related to the target reservoir, such as porosity, saturation, reservoir recoverability index, organic carbon content, brittleness index, rigidity coefficient and other parameters, and this application does not impose such restrictions on this.

[0060] In some embodiments, the reservoir attribute parameters of the target reservoir may be clustered based on the maximum membership principle, and preset evaluation indicators may be used as clustering categories to constrain the clustering results of the reservoir attribute parameters and determine the reservoir identification model of the target reservoir.

[0061] In some embodiments, the reservoir identification model may include a target layer, a criterion layer, and a solution layer, wherein the target layer may correspond to the reservoir evaluation target of the sweet spot index calculation, the criterion layer may correspond to the rules, methods, calculation logic, etc. of the sweet spot index calculation, and the solution layer may correspond to the basic elements involved in the sweet spot index calculation, that is, the reservoir attribute parameters of the target reservoir. It can be understood that there may be a certain correlation between the elements corresponding to the criterion layer and / or the solution layer. Further, the criterion layer and / or the solution layer may be multi-layered, that is, it may include multiple criterion layers and / or solution layers; there may also be no correlation between the elements corresponding to the criterion layer and / or the solution layer. Further, the criterion layer and / or the solution layer may be one layer, which can be specifically set and generated based on the target layer and the actual application scenario, and the present application does not limit this.

[0062] In some embodiments, the reservoir identification model can be constructed in the following manner:

[0063] Acquire multiple reservoir attribute parameters and well logging information of the target reservoir;

[0064] Based on a plurality of preset evaluation indicators and the well logging information, some reservoir attribute parameters are selected from the plurality of reservoir attribute parameters as reservoir parameters in a reservoir identification model, and a subordinate relationship between the reservoir parameters and the plurality of evaluation indicators is determined, wherein the plurality of evaluation indicators are determined based on the sweet spot indicators;

[0065] Based on the affiliation and the reservoir parameters, the reservoir identification model is constructed.

[0066] It can be understood that for the reservoir identification model, the reservoir parameters related to the determination of the sweet spot index in the reservoir attribute parameters can be determined based on the analysis of the reservoir parameters and logging information of the target reservoir, and the reservoir identification model can be constructed by combining the relationship between the reservoir parameters and the preset evaluation indexes, the correlation between the reservoir parameters, etc. The structure of the constructed reservoir identification model will be introduced below in conjunction with the attached drawings and will not be described in detail here.

[0067] In some embodiments, selecting a reservoir parameter from a plurality of reservoir attribute parameters and determining the affiliation between the reservoir parameter and a plurality of evaluation indicators may include: determining at least one evaluation indicator for evaluating the sweet spot as a preset plurality of evaluation indicators based on the evaluation criteria of the sweet spot level, and then clustering the reservoir attribute parameters based on the correlation between each reservoir attribute parameter and the logging information and the maximum affiliation principle, and determining the evaluation indicator to which each reservoir attribute parameter belongs, so as to achieve the purpose of selecting the reservoir parameter and determining the affiliation between the reservoir parameter and the evaluation indicator. It is understandable that some reservoir attribute parameters may not belong to any of the evaluation indicators, and thus, when selecting the reservoir parameters, the reservoir attribute parameters belonging to any one of the evaluation indicators may be used as the selected reservoir parameters.

[0068] In some embodiments, some reservoir parameters may be correlated with each other, and the reservoir parameters and the evaluation indicators may be indirectly subordinated to each other. For example, for reservoir parameters A and reservoir parameters B, if the two are correlated, reservoir parameters A and reservoir parameters B may be subordinated to parameter C, and parameter C is subordinated to evaluation indicator D. Reservoir parameters A and reservoir parameters B are directly subordinated to parameter C and indirectly subordinated to evaluation indicator D, that is, the scheme layer is multi-layered. In other embodiments, there is no correlation between reservoir parameters, and the reservoir parameters and the evaluation indicators may be directly subordinated to each other, that is, the scheme layer is single-layered, and this application does not limit this.

[0069] In some embodiments, the reservoir parameters may be subordinate to at least two evaluation indicators, that is, the determination of at least two evaluation indicators is related to the reservoir parameters. In other embodiments, the reservoir parameters may be subordinate to one evaluation indicator, which is not limited in the present application.

[0070] It can be understood that, through the different affiliations between the above-mentioned reservoir parameters and multiple evaluation indicators, a more accurate, reliable and practical reservoir identification model can be constructed, and more precise results can be obtained when determining the sweet spot indicators.

[0071] In some embodiments, based on a plurality of preset evaluation indicators and the well logging information, selecting some reservoir attribute parameters from the plurality of reservoir attribute parameters as reservoir parameters in the reservoir identification model, and determining the affiliation between the reservoir parameters and the plurality of evaluation indicators may include:

[0072] Based on the well logging information, determining a whitening weight function of each evaluation index;

[0073] Based on the whitening weight function, determining whitening number information of the plurality of reservoir attribute parameters under each evaluation index;

[0074] Based on the whitening number information, the reservoir parameter is selected from the plurality of reservoir attribute parameters, and the affiliation between the reservoir parameter and the evaluation index is determined.

[0075] It can be understood that the whitening number can be used to characterize the degree of membership between each reservoir attribute parameter and the evaluation index, that is, the correlation between the determination of the evaluation index and each reservoir attribute parameter.

[0076] In some embodiments, according to the calculation requirements of the sweet spot index and based on the maximum membership principle, the target reservoir containing shale oil can be graded and evaluated by applying the gray clustering analysis method. Among them, the gray clustering analysis method is mainly based on the construction of the whitening weight function. By analyzing the whitening number of reservoir attribute parameters under different gray conditions (i.e., different evaluation indicators), the evaluation indicators to which the analysis object belongs can be distinguished, and it can be determined which category of evaluation indicators it should be classified into. The gray clustering analysis method can determine the number and standard of evaluation indicators in advance, which can avoid the situation that a certain category does not actually exist and other methods will definitely divide this category. It is more in line with the actual needs of determining the sweet spot index, and can consider the role of each reservoir attribute parameter in the graded evaluation. Through the fuzzy and overlapping graded boundaries between each level, the evaluation indicators are divided into categories, and the reservoir identification model obtained can contain more abundant information, which is more in line with the actual situation, and the clustering results are more comprehensive, objective, credible, and practical.

[0077] S102: Determine the degree of association between the elements at each level in the reservoir identification model based on the affiliation relationship between the elements at each level in the reservoir identification model and a preset importance determination index.

[0078] It can be understood that the degree of association may include the relative importance of at least two elements belonging to the same adjacent level element in the reservoir identification model to the adjacent level element. For example, if both element A and element B belong to element C of the adjacent upper level, the degree of association between element A and element B to element C can be obtained based on the importance of element A and element B to element C respectively. Specifically, the ratio of the importance of element A and element B to element C can be used as the degree of association between element A and element B to element C.

[0079] In some embodiments, the preset importance determination index may include numbers 1 to 9, and different numbers may represent different primary scales, such as consistent importance, slightly important, obviously important, strongly important, extremely important, etc.

[0080] In some embodiments, the correlation degree may be the relative importance of two elements belonging to the same adjacent level element to the adjacent level element. Accordingly, the preset importance determination index may be represented by the following table:

[0081] Table 1

[0082] Importance Scale Importance Scale Description 1 The two elements have the same importance 3 The former is slightly more important than the latter 5 The former is obviously more important than the latter 7 The former is more important than the latter 9 The former is extremely more important than the latter 2、4、6、8 Intermediate scale 1 / k, k=1, 2, 3, ..., 9 The order of elements is swapped, and the importance ratio is the reciprocal of each other

[0083] In some embodiments, based on the affiliation of the elements at each level in the reservoir identification model and a preset importance discrimination index, determining the degree of association between the elements at each level in the reservoir identification model may include:

[0084] Based on the preset importance discrimination index and the affiliation, a judgment matrix of the reservoir identification model is constructed, wherein any one element in the judgment matrix is ​​used to characterize the relative importance of the influence of two elements in the current level that belong to the same target element in the adjacent level on the target element;

[0085] A consistency check is performed on the judgment matrix, and when it is determined that the judgment matrix passes the consistency check, the judgment matrix is ​​used as the degree of association.

[0086] It can be understood that the judgment matrix can be the importance of each element in any layer attribute relative to the elements of the adjacent layer to which it belongs. The construction method of the judgment matrix will be introduced below and will not be described in detail here.

[0087] It can be understood that the consistency check is used to determine whether the degree of association of elements at each level is contradictory. For example, element A is more important than element B, element B is more important than element C, but element C is more important than element A. The rationality of the construction of the judgment matrix can be ensured through the judgment matrix.

[0088] In some embodiments, the judgment matrix can be generated based on the affiliation relationship and the information input by the user. Specifically, the user can input the importance of each element to the elements of the adjacent level to which it belongs based on the preset importance judgment index, for example, it can be expressed by a numerical value, and then the judgment matrix can be constructed based on the information input by the user and the affiliation relationship in the reservoir identification model.

[0089] In some embodiments, the importance of each element in the reservoir identification model to the elements of the adjacent layers to which it belongs can be analyzed based on the well logging information of the target reservoir combined with the experimental simulation analysis data, and then a judgment matrix can be constructed based on the analysis results and the affiliation relationship in the reservoir identification model. Specifically, based on the well logging information of the target reservoir, the importance of each element in the reservoir identification model to the elements of the adjacent layers to which it belongs can be analyzed, which can include: based on the well logging information, combined with the data collected by the experiment, fitting the change relationship between each element and the elements of the adjacent layers to which it belongs, determining the influence of each element on the elements of the adjacent layers to which it belongs, and then constructing a judgment matrix based on the influence of each element on the elements of the adjacent layers to which it belongs.

[0090] In some embodiments, the maximum eigenvalue of the judgment matrix can be calculated, and then the consistency index can be calculated based on the maximum eigenvalue, and then the consistency ratio can be calculated based on the random consistency index, and the consistency check is determined to pass when the consistency ratio meets the preset ratio threshold, otherwise the consistency check fails, and the degree of association needs to be adjusted until the consistency check passes. In some embodiments, the preset ratio threshold can be 0.1. The consistency check process will be introduced below in conjunction with specific embodiments, and will not be repeated here.

[0091] S103: Based on the correlation degree, calculating the importance coefficient of each reservoir parameter relative to the sweet spot index.

[0092] It can be understood that the importance coefficient can be the importance weight of each reservoir parameter when the reservoir parameter is used to calculate the sweet spot index. The reservoir parameter can be used as an evaluation parameter for evaluating the sweet spot index. The importance coefficient of each reservoir parameter relative to the sweet spot index can be obtained based on the correlation degree of the hierarchy obtained by the reservoir rating model.

[0093] It can be understood that the degree of association is a comparative measurement of the importance of attributes at the same level using a unified standard, which can include the measurement of importance between levels, and the importance coefficient is the importance coefficient of the reservoir parameters in the lowest solution layer to the sweet spot index of the target layer. That is, the determination of the degree of association is a qualitative analysis of each element in the reservoir identification model, and the calculation of the importance coefficient is a quantitative calculation of the reservoir parameters of the solution layer in the reservoir identification model. Based on the hierarchical structure in the constructed reservoir identification model, the importance of elements at the same level is compared with each other, and a unified standard is used for measurement to obtain the degree of association, and then the importance coefficient of each reservoir parameter to the sweet spot index is calculated by mathematical methods, which can avoid the greater complexity and uncertainty caused by directly judging the relationship between too many reservoir parameters and the target layer, and can have strong practicality, systematicness and authenticity.

[0094] In some embodiments, based on the correlation degree, calculating the importance coefficient of each reservoir parameter relative to the sweet spot index includes:

[0095] Based on the hierarchical structure in the reservoir identification model and the degree of association, the weight coefficient corresponding to each reservoir parameter is determined, and the weight coefficient corresponding to each reservoir parameter is used as the importance coefficient.

[0096] In some embodiments, the degree of association can be a judgment matrix. Further, the weight coefficients of each reservoir parameter can be calculated based on the hierarchical structure in the reservoir identification model and the judgment matrix. Specifically, the eigenvector of the judgment matrix can be calculated, and then the weight coefficients between the levels can be constructed based on the eigenvector corresponding to the maximum eigenvalue of the judgment matrix. Then, the weight coefficients of each layer for the target layer can be determined based on the weight coefficients between the levels, and then the weight coefficients of the reservoir parameters in the solution layer for the sweet spot indicators of the target layer can be obtained. The specific calculation process of the weight coefficient will be introduced below in conjunction with the specific embodiments, and will not be repeated here.

[0097] S104: Obtaining numerical values ​​corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir.

[0098] It can be understood that the values ​​corresponding to the reservoir parameters can be pre-calculated and stored, and the values ​​of the reservoir parameters can be directly obtained when determining the sweet spot. The values ​​of the reservoir parameters can also be calculated based on the logging information of the target reservoir.

[0099] In some embodiments, shale oil reservoirs are more sensitive to acoustic, density, resistivity, nuclear magnetic and other logging response characteristics. Nuclear magnetic logging information can be used to calculate oil saturation and reservoir recoverability index; the total organic carbon content can be calculated using the ΔlogR method of the acoustic time difference curve and the resistivity curve; the rigidity coefficient can be calculated using density, longitudinal and shear wave time differences, clay content, etc., and the brittleness index can be calculated using the elastic modulus method.

[0100] In some embodiments, obtaining the values ​​corresponding to the plurality of reservoir parameters in the reservoir identification model corresponding to the target reservoir may include:

[0101] The well logging information of the target reservoir is obtained, and based on the well logging information and a preset calculation method for each reservoir parameter, a numerical value corresponding to each reservoir parameter is determined.

[0102] In some embodiments, taking reservoir parameters including porosity, oil saturation, reservoir recoverability index, total organic carbon content and brittleness index as an example, the calculation process of the reservoir parameters is as follows.

[0103] In some embodiments, the porosity calculation of the target reservoir can be carried out by using the core analysis data regression statistics method, and the porosity logging curve regression with the best regional correlation can be selected; the calculation of oil saturation can be implemented by using the Archie formula, and the rock electrical parameters a, b, m, and n required in the Archie formula can be determined by rock electrical experiments; the reservoir recoverability index can be calculated by using the reservoir recoverability index calculation formula proposed after considering the negative effects of kerogen and asphalt, and the specific calculation method is as follows:

[0104]

[0105] Among them, RPI can represent the reservoir recoverability index, W c_org It can be expressed as total organic carbon (mg / g), W c_oil It can express the carbon content in shale oil, that is, the carbon content of shale oil per unit mass of formation (mg / g).

[0106] In some embodiments, the carbon content in oil can be calculated using NMR logging data:

[0107]

[0108] in, It can represent the total porosity of the formation. and can represent the volume fractions of asphalt and water, respectively. It can be expressed as the volume fraction of shale oil, ρ oil It can express the density of shale oil (g / cm3), ρ bIt can be expressed as volume density (g / cm3), and K is generally taken as 12 / 14.

[0109] In some embodiments, the total organic carbon content can be calculated using the ΔlogR method, and the total organic carbon content can be calculated based on the acoustic wave time difference and the amplitude difference between the resistivity curves; the brittleness index can be calculated using the elastic modulus method, and the rigidity coefficient can be calculated using the density, longitudinal and transverse wave time differences and clay content.

[0110] S105: Based on the numerical values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, determine the sweet spot index of the target reservoir to identify the shale oil sweet spot of the target reservoir.

[0111] It can be understood that the importance coefficient can characterize the importance of the corresponding reservoir parameter in the calculation of the sweet spot index. The sweet spot index can be calculated by calculating the importance coefficient and the value corresponding to the reservoir parameter. For example, the importance coefficient can be used as the weight of the value of the reservoir parameter in the sweet spot index, and the sweet spot index can be obtained by weighted summing the values ​​of the reservoir parameters. It can be understood that other calculation methods can be used for the calculation between the values ​​corresponding to the reservoir parameters and the importance coefficient, which will not be described in detail here.

[0112] It can be understood that the sweet spot index can be used to evaluate the sweet spot of the target reservoir, that is, to achieve evaluation or rating of the sweet spot reservoir in a quantitative manner.

[0113] In some embodiments, based on the values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, the sweet spot index of the target reservoir is determined to identify the shale oil sweet spot of the target reservoir, including:

[0114] Based on the importance coefficient of each reservoir parameter, weighted summation is performed on the values ​​of the plurality of reservoir parameters to obtain the sweet spot index;

[0115] The sweet spot level of the target reservoir is determined based on the sweet spot index and the corresponding relationship between the preset sweet spot level and the sweet spot index interval.

[0116] In some embodiments, the preset sweet spot levels may include, for example, a primary sweet spot area, a secondary sweet spot area, a tertiary sweet spot area, etc. Each level of the sweet spot area may correspond to a numerical range of a sweet spot indicator, that is, a sweet spot indicator range. The sweet spot indicator of the calculated target reservoir may be matched with the sweet spot indicator range, and the sweet spot level corresponding to the target reservoir may be determined based on the matched sweet spot indicator area.

[0117] In some embodiments, multiple reservoir parameters and their corresponding importance coefficients can be used to construct a sweet spot reservoir prediction model for the target reservoir, that is, the importance coefficient can be used as the weight of each reservoir parameter in the model to construct a weighted sum model of multiple reservoir parameters, and the result of the weighted summation can be used as a sweet spot index. Further, for the target reservoir, the reservoir parameters of different well sections can be substituted into the sweet spot reservoir prediction model to obtain the sweet spot index of different well sections, and the sweet spot level of different well sections can be determined based on the sweet spot index, so as to achieve continuous prediction of the sweet spot level of the sweet spot reservoir.

[0118] The following introduces the reservoir identification model provided in the embodiment of the present application by taking the target layer as the sweet spot indicator and the criterion layer and the scheme layer each including one layer as an example.

[0119] Figure 2 FIG. 1 is a schematic diagram of a reservoir identification model provided in an embodiment of the present application. Figure 2 As shown, in some embodiments, the reservoir identification model can be a three-layer structure, which includes from top to bottom: a target layer composed of sweet spot indicators, a quasi-measurement layer composed of multiple evaluation indicators, and a solution layer composed of multiple reservoir parameters. Any one of the reservoir parameters is subject to at least one evaluation indicator, and the multiple evaluation indicators are subject to the sweet spot indicators.

[0120] It can be understood that since the importance of each reservoir parameter is different in the calculation process of the sweet spot index, that is, the weight is different, it is necessary to calculate the weight coefficient of the hierarchical element. Specifically, the degree of association of each level can be calculated, for example, a judgment matrix can be constructed through the hierarchical analysis method, and then the weight coefficient of each reservoir parameter can be calculated. Among them, the hierarchical analysis method can integrate qualitative analysis and quantitative calculation, that is, multiple reservoir parameters and evaluation indicators can be established in a recursive hierarchical structure in a logical order, that is, a reservoir identification model, and then the importance of elements at the same level is compared with each other, and a unified standard is used for measurement, and a judgment matrix is ​​constructed. Finally, the weight coefficient of each element to the target is calculated by mathematical methods, avoiding the greater complexity and uncertainty caused by directly judging the relationship between too many elements and the target layer, and has strong practicality, systematicness and authenticity.

[0121] It can be understood that the target layer is the rating of the reservoir sweet spot, which can be rated by calculating the sweet spot indicators; the criterion layer can be three important indicators for calculating the sweet spot indicators, that is, the evaluation indicators, which can also be understood as the three parts of the rating of the reservoir sweet spot. The criterion layer can be multiple layers, but in this embodiment, based on the application scenario of the sweet spot evaluation, one layer of criterion layer can be selected; the solution layer belongs to the above three parts, that is, the attribute elements under the three evaluation indicators, which can be understood as the specific evaluation parameters involved in the sweet spot rating. Due to the strong correlation between the evaluation parameters, the solution layer can be multiple layers, but in this embodiment, based on the application scenario of the sweet spot evaluation, the five determined reservoir parameters have no clear correlation in physical meaning, so the solution layer can be one layer, and the number of attribute parameters of the solution layer can be determined based on the specific evaluation indicators of the criterion layer. It can be understood that Figure 2 The reservoir identification model in the present application is an example. In other embodiments, other model structures may be used, such as different affiliations, different numbers of criterion layers and / or scheme layers, and may also include other evaluation indicators and / or reservoir parameters, which are not limited in the present application.

[0122] like Figure 2 As shown, in some embodiments, the reservoir parameters include porosity, oil saturation, reservoir recoverability index, organic carbon content and brittleness index, and the preset multiple evaluation indicators include reservoir physical properties, source rock characteristics and engineering brittleness;

[0123] The porosity, the saturation content and the reservoir recoverability index belong to the reservoir physical properties, the organic carbon content belongs to the source rock characteristics, and the brittleness index belongs to the engineering brittleness.

[0124] The following will Figure 2 Taking the hierarchical structure of the reservoir identification model in as an example, the importance coefficient of each reservoir parameter relative to the sweet spot index is calculated by combining the hierarchical analysis method, and a sweet spot reservoir prediction model for shale oil reservoirs in a certain area is established to rate the sweet spot reservoirs in this area.

[0125] In this embodiment, the method for determining the shale oil sweet spot may specifically include the following steps:

[0126] S1: Calculate the value of the reservoir parameter corresponding to the target reservoir based on the well logging information. It can be understood that the calculation method of the value of the reservoir parameter can refer to Figure 1 The description of the relevant parts in step S104 is not repeated here.

[0127] S2: Constructing a reservoir identification model. It is understood that the constructed reservoir identification model can be, for example, Figure 2 For the specific construction method, please refer to Figure 1 The description of the relevant part of step S101 in is not repeated here.

[0128] S3: Determine the importance of each element between each layer in the reservoir identification model on the elements of the adjacent layer by constructing a judgment matrix.

[0129] Specifically, the number of elements in the kth layer of the reservoir identification model can be recorded as n (k) (Simply denoted as n when there is no confusion), the element set is Any two elements and To an element in the previous layer The importance ratio is The element of this layer is related to an element of the previous layer The ratio of the importance of each pair is expressed as a matrix Indicates that the matrix U can be called the k-th layer element X (k) For the previous element X (k-1) The judgment matrix can be written as:

[0130]

[0131] In this embodiment, the importance of two elements can be calibrated using numbers 1 to 9 and their reciprocals. For example, in Table 1 in the previous text, the number 1 in Table 1 can represent that the importance of the two elements is the same, the number 9 can represent that the former is extremely more important than the latter, and the reciprocal of the number represents that the importance ratio is in a reciprocal relationship, which is equivalent to swapping the order of the elements.

[0132] In this embodiment, the importance of attributes between different levels can be judged based on the sensitivity analysis of reservoir properties of each element in the region and common sense judgment, and a judgment matrix of each level can be constructed according to the importance scale table shown in Table 1. In order to reduce the complexity caused by too many elements, the construction of the judgment matrix can only include relevant elements under each level element.

[0133] In this embodiment, the constructed partial judgment matrix can be shown as follows:

[0134]

[0135] Among them, the matrix U (2) It can be the judgment matrix of the three elements of the criterion layer for the sweet spot index of the target layer. It can be the judgment matrix of the first three elements in the solution layer to the first element in the criterion layer. Since the solution layer under the last two elements of the criterion layer contains only one element, the judgment matrix can be omitted, that is, the two judgment matrices here can be expressed as

[0136] Understandably, Figure 2 The judgment matrix corresponding to the porosity in Judgment matrix corresponding to oil saturation And the judgment matrix corresponding to the reservoir recoverability index It can form the judgment matrix of the first three elements in the solution layer against the first element in the criterion layer Matrix corresponding to organic carbon content It can be the judgment matrix of the fourth element in the solution layer against the second element in the criterion layer Matrix corresponding to the brittleness index It can be the judgment matrix of the fifth element in the solution layer against the third element in the criterion layer Judgment matrix corresponding to reservoir physical properties Judgment matrix corresponding to source rock characteristics And the judgment matrix corresponding to engineering brittleness The three elements in the criterion layer can form the judgment matrix U of the sweet spot index in the solution layer (2) .

[0137] S4: Judgment matrix consistency check.

[0138] It can be understood that in order to avoid the situation where the importance of each graded element is contradictory, for example, element A is more important than element B, element B is more important than element C, but element C is more important than element A, it is necessary to perform a consistency check on the judgment matrix to ensure the rationality of the construction of the judgment matrix. Specifically, the maximum eigenvalue of the judgment matrix can be calculated, and the consistency index (Concordance Index, CI) can be calculated. The specific calculation method is: Among them, n can represent the order of the judgment matrix; λ max It can represent the maximum eigenvalue of the judgment matrix. Then, according to the RI coefficient table corresponding to the random consistency index (RI), the RI coefficient corresponding to the n value can be found and the consistency ratio (CR) can be calculated. The specific calculation method of the consistency ratio CR can be: If the consistency ratio CR is less than 0.1, it means that the consistency of the judgment matrix is ​​acceptable. Otherwise, it means that the judgment matrix is ​​unreasonable and the importance of the elements needs to be adjusted until the judgment matrix passes the consistency test. The maximum eigenvalue of the judgment matrix constructed in step 3 can be calculated and a consistency test can be performed on it.

[0139] In this embodiment, the RI coefficient table 2 corresponding to the random consistency index RI is as follows:

[0140] Table 2

[0141] n 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

[0142] In this embodiment, for the judgment matrix U constructed in step A3 (2) , whose maximum eigenvalue λ max =3.054, substituting it into the calculation formula of consistency index, the judgment matrix U can be calculated. (2) The consistency index CI = 0.027, and then the judgment matrix U can be obtained (2) The consistency ratio The consistency of the judgment matrix is ​​acceptable; for the judgment matrix Its maximum eigenvalue λ max =3.018, and the calculation formula of consistency index can be used to calculate the judgment matrix The consistency index CI = 0.009, the judgment matrix The consistency ratio The consistency of the judgment matrix is ​​acceptable.

[0143] S5: Calculate the weight coefficient of each element according to the hierarchical relationship of the reservoir identification model and the judgment matrix, and then generate a sweet spot reservoir prediction model.

[0144] Specifically, we can calculate the eigenvector W corresponding to the maximum eigenvalue of the judgment matrix = (w1 w2…w i ), where i is the order of the judgment matrix. Since the orders of the judgment matrices in the same layer are not necessarily the same, the arithmetic mean method can be used to average the elements in the eigenvector, that is, the eigenvector W of each judgment matrix after the average arg Can be:

[0145]

[0146] Among them, i max It can represent the maximum order value of the corresponding judgment matrix, and i can represent the element in the eigenvector.

[0147] Furthermore, the weight vector of the kth layer to the k-1th layer can be constructed based on the eigenvector corresponding to the maximum eigenvalue of the judgment matrix of each layer. Where j is the number of judgment vectors in the kth layer. Weight vector P (k) It can be expressed as:

[0148]

[0149] Furthermore, the weight coefficient matrix of the kth layer to the first layer (target layer) can be recorded as σ (k) , and calculate the weight coefficient of each layer to the target layer. Among them, the weight coefficient of the kth layer to the target layer can be expressed as: σ (k) =σ (k-1) ×P (k)Based on the reservoir identification model and judgment matrix established in step S2 and step S3, each reservoir parameter (i.e. Figure 2 The weight coefficient of the reservoir parameters (porosity, oil saturation, reservoir recoverability index, organic carbon content, brittleness index) to the sweet spot index is obtained, and the weight coefficient σ = (0.253 0.440 0.097 0.042 0.168) is obtained, and the sweet spot reservoir prediction model can be generated based on the weight coefficients of each reservoir parameter obtained. In this embodiment, the sweet spot reservoir prediction model can be expressed as:

[0150]

[0151] Among them, S can represent the sweet spot index, It can be expressed as porosity, S O It can represent oil saturation, RPI can represent reservoir recoverability index, TOC can represent organic carbon content, and BI can represent brittleness index.

[0152] The method for determining shale oil sweet spots provided in the embodiment of the present application utilizes the maximum membership principle to construct a reservoir identification model, and based on the constructed reservoir identification model, the weight coefficients of reservoir parameters are calculated in combination with the hierarchical analysis method, and a shale oil sweet spot determination model is constructed based on the calculated weight coefficients and reservoir parameters. The sweet spot index of the reservoir can be calculated based on the model, and then the sweet spot reservoir can be graded and evaluated based on the sweet spot index, which can greatly improve the prediction accuracy of the sweet spot reservoir. Compared with the actual production data, the graded evaluation results of the sweet spot reservoir are more accurate and reliable than the reservoir crude oil graded evaluation scheme, have stronger practical geological significance, can be widely used in the evaluation of various reservoirs, have higher application effects, and can provide key technical support for regional exploration.

[0153] The embodiment of the present application also provides a device for determining shale oil sweet spots. Figure 3 Shown is a schematic diagram of a device for determining shale oil sweet spots provided in an embodiment of the present application.

[0154] like Figure 3 As shown, the shale oil sweet spot determination device 300 may include:

[0155] The model acquisition module 301 is used to acquire a reservoir identification model pre-constructed for a target reservoir, wherein the reservoir identification model includes a plurality of reservoir parameters related to determining a sweet spot index of the target reservoir, and a multi-level structural relationship between the plurality of reservoir parameters and the sweet spot index of the target reservoir, wherein any level includes at least one element.

[0156] The qualitative analysis module 302 is used to determine the degree of association between the elements at each level in the reservoir identification model based on the affiliation relationship of the elements at each level in the reservoir identification model and a preset importance discrimination index.

[0157] The quantitative calculation module 303 is used to calculate the importance coefficient of each reservoir parameter relative to the sweet spot index based on the correlation degree.

[0158] The data acquisition module 304 is used to obtain numerical values ​​corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir.

[0159] The sweet spot determination module 305 is used to determine the sweet spot index of the target reservoir based on the numerical value corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, so as to identify the shale oil sweet spot of the target reservoir.

[0160] The description and functions of the above units can be understood by referring to the content of the method for determining the shale oil sweet spot, which will not be repeated here.

[0161] The present specification also provides a computer storage medium storing computer program instructions, which implement the steps of the above-mentioned method for determining shale oil sweet spots when executed.

[0162] The present specification also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the above-mentioned method for determining the shale oil sweet spot are implemented.

[0163] The embodiment of the present invention further provides an electronic device, such as Figure 4 As shown, the electronic device may include a processor 401 and a memory 402, wherein the processor 401 and the memory 402 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0164] The processor 401 may be a central processing unit (CPU). The processor 401 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0165] The memory 402 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the method for determining shale porosity in an embodiment of the present invention (for example, Figure 3 The processor 401 executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory 402, that is, the method for determining the shale oil sweet spot in the above method embodiment is implemented.

[0166] The memory 402 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created by the processor 401, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 402 may optionally include a memory remotely arranged relative to the processor 401, and these remote memories may be connected to the processor 401 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0167] The one or more modules are stored in the memory 402, and when executed by the processor 401, the following is performed: Figure 1 A method for determining shale oil sweet spots in the illustrated embodiment.

[0168] The specific details of the above electronic device can be understood by referring to the corresponding descriptions and effects in the above method embodiments, and will not be repeated here.

[0169] The present specification also provides a computer storage medium storing computer program instructions, which implement the steps of the above-mentioned method for determining shale oil sweet spots when executed.

[0170] The present specification also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the above-mentioned method for determining the shale oil sweet spot are implemented.

[0171] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0172] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0173] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0174] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0175] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute certain parts of the methods of each implementation method of the present application.

[0176] The present application can be used in many general or special computer system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0177] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0178] Although the present application has been described through embodiments, those skilled in the art will appreciate that there are many modifications and variations to the present application without departing from the spirit of the present application, and it is intended that the appended claims include these modifications and variations without departing from the spirit of the present application.

Claims

1. A method for determining a shale oil sweet spot, characterized in that: include: Acquire a reservoir identification model pre-constructed for a target reservoir, wherein the reservoir identification model includes a plurality of reservoir parameters related to determining a sweet spot index of the target reservoir, and a multi-level structural relationship between the plurality of reservoir parameters and the sweet spot index of the target reservoir, wherein each level includes at least one element; Determining the degree of association between the elements at each level in the reservoir identification model based on the affiliation relationship between the elements at each level in the reservoir identification model and a preset importance discrimination index; Based on the correlation degree, calculating the importance coefficient of each reservoir parameter relative to the sweet spot index; Acquire numerical values ​​corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir; Based on the numerical values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, the sweet spot index of the target reservoir is determined to identify the shale oil sweet spot of the target reservoir.

2. The method according to claim 1, characterized in that The reservoir identification model is constructed in the following way: Acquire multiple reservoir attribute parameters and well logging information of the target reservoir; Based on a plurality of preset evaluation indicators and the well logging information, some reservoir attribute parameters are selected from the plurality of reservoir attribute parameters as reservoir parameters in a reservoir identification model, and a subordinate relationship between the reservoir parameters and the plurality of evaluation indicators is determined, wherein the plurality of evaluation indicators are determined based on the sweet spot indicators; Based on the affiliation and the reservoir parameters, the reservoir identification model is constructed.

3. The method according to claim 2, characterized in that Based on the preset multiple evaluation indicators and the logging information, some reservoir attribute parameters are selected from the multiple reservoir attribute parameters as reservoir parameters in the reservoir identification model, and the affiliation between the reservoir parameters and the multiple evaluation indicators is determined, including: Based on the well logging information, determining a whitening weight function of each evaluation index; Based on the whitening weight function, determining whitening number information of the plurality of reservoir attribute parameters under each evaluation index; Based on the whitening number information, the reservoir parameter is selected from the plurality of reservoir attribute parameters, and the affiliation between the reservoir parameter and the evaluation index is determined.

4. The method according to any one of claims 1 to 3, characterized in that: The reservoir identification model has a three-layer structure, which includes from top to bottom: a target layer composed of sweet spot indicators, a quasi-measurement layer composed of multiple evaluation indicators, and a solution layer composed of multiple reservoir parameters. Any reservoir parameter belongs to at least one evaluation indicator, and the multiple evaluation indicators belong to the sweet spot indicators.

5. The method according to claim 4, characterized in that The reservoir parameters include porosity, oil saturation, reservoir recoverability index, organic carbon content and brittleness index, and the preset multiple evaluation indicators include reservoir physical properties, source rock characteristics and engineering brittleness; The porosity, the saturation content and the reservoir recoverability index belong to the reservoir physical properties, the organic carbon content belongs to the source rock characteristics, and the brittleness index belongs to the engineering brittleness.

6. The method according to claim 1, characterized in that Based on the affiliation of the elements at each level in the reservoir identification model and the preset importance discrimination index, the correlation degree between the elements at each level in the reservoir identification model is determined, including: Based on the preset importance discrimination index and the affiliation, a judgment matrix of the reservoir identification model is constructed, wherein any one element in the judgment matrix is ​​used to characterize the relative importance of the influence of two elements in the current level that belong to the same target element in the adjacent level on the target element; A consistency check is performed on the judgment matrix, and when it is determined that the judgment matrix passes the consistency check, the judgment matrix is ​​used as the degree of association.

7. The method according to claim 1, characterized in that Based on the correlation degree, the importance coefficient of each reservoir parameter relative to the sweet spot index is calculated, including: Based on the hierarchical structure in the reservoir identification model and the degree of association, the weight coefficient corresponding to each reservoir parameter is determined, and the weight coefficient corresponding to each reservoir parameter is used as the importance coefficient.

8. The method according to claim 1, characterized in that Based on the values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, the sweet spot index of the target reservoir is determined to identify the shale oil sweet spot of the target reservoir, including: Based on the importance coefficient of each reservoir parameter, weighted summation is performed on the values ​​of the plurality of reservoir parameters to obtain the sweet spot index; The sweet spot level of the target reservoir is determined based on the sweet spot index and the corresponding relationship between the preset sweet spot level and the sweet spot index interval.

9. The method according to claim 1, characterized in that: Acquiring numerical values ​​corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir, including: The well logging information of the target reservoir is obtained, and based on the well logging information and a preset calculation method for each reservoir parameter, a numerical value corresponding to each reservoir parameter is determined.

10. A device for determining shale oil sweet spots, characterized in that: include: A model acquisition module, used to acquire a reservoir identification model pre-constructed for a target reservoir, wherein the reservoir identification model includes a plurality of reservoir parameters related to determining a sweet spot index of the target reservoir, and a multi-level structural relationship between the plurality of reservoir parameters and the sweet spot index of the target reservoir, wherein each level includes at least one element; A qualitative analysis module, used to determine the degree of association between the elements at each level in the reservoir identification model based on the affiliation of the elements at each level in the reservoir identification model and a preset importance discrimination index; A quantitative calculation module, used for calculating the importance coefficient of each reservoir parameter relative to the sweet spot index based on the correlation degree; A data acquisition module, used to acquire numerical values ​​corresponding to a plurality of reservoir parameters in a reservoir identification model corresponding to the target reservoir; The sweet spot determination module is used to determine the sweet spot index of the target reservoir based on the numerical value corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, so as to identify the shale oil sweet spot of the target reservoir.

11. An electronic device, characterized in that: include: A memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor implements the steps of the method according to any one of claims 1 to 9 by executing the computer instructions.

12. A computer storage medium, characterized in that: The computer storage medium stores computer program instructions, and when the computer program instructions are executed, the steps of the method according to any one of claims 1 to 9 are implemented.

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