Methods, apparatus and equipment for determining shale oil sweet spots

Abstract

This application discloses a method, apparatus, and equipment for determining shale oil sweet spots. The method includes: acquiring a reservoir identification model pre-constructed for the target reservoir; determining the correlation degree between the elements at each level of the reservoir identification model based on the membership relationships of the elements at each level and a preset importance discrimination index; calculating the importance coefficient of each reservoir parameter relative to the sweet spot index based on the correlation degree; acquiring the numerical values ​​of multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir; and determining the sweet spot index of the target reservoir based on the numerical values ​​of each reservoir parameter and the importance coefficient of each reservoir parameter, thereby identifying shale oil sweet spots in the target reservoir. This application calculates the correlation degree between the elements at each level based on a pre-constructed reservoir identification model to determine the importance coefficients of the reservoir parameters, and determines the sweet spot index based on the importance coefficients, thus obtaining more accurate and reliable results.

Description

Technical Field

[0001] This application relates to the field of petroleum exploration technology, and in particular to a method, apparatus and equipment for determining the sweet spot of shale oil. Background Technology

[0002] Shale oil is a type of petroleum stored in shale formations rich in organic matter and characterized by nanoscale pore size. It generally exists in both adsorbed and free states. Shale oil accumulation exhibits certain unique characteristics, primarily occurring in continental strata and characterized by multi-cycle tectonic evolution, significant lithofacies variations, and challenges in evaluation. Currently, commonly used methods for determining the sweet spot of shale oil reservoirs mainly include the following approaches:

[0003] (1) Based on production data and evaluation parameters, principal component analysis was used to select evaluation indicators, and a comprehensive evaluation model and chart of shale oil reservoir quality were constructed based on classification and discriminant analysis.

[0004] (2) Based on field experimental data from the oilfield and by comprehensively referencing various parameters, a set of multi-parameter “sweet spot” distribution prediction technologies covering geology, logging, geophysical exploration and engineering has been developed.

[0005] (3) By setting cutoff values ​​for parameters such as porosity, oil saturation, permeability, and formation stress, shale reservoir quality (RQ) and completion quality (CQ) are evaluated. The evaluation results of reservoir quality and completion quality are divided into two levels. Then, the results of the two are combined to form a new evaluation parameter RCQ, which is used to classify and evaluate the reservoir. The final evaluation results are divided into four levels.

[0006] It is evident that current research on methods for determining sweet spots in shale oil reservoirs focuses more on the calculation of parameters such as physical properties and brittleness, and lithological identification. In terms of the classification and evaluation of sweet spots in shale oil reservoirs, most current methods directly utilize geochemical and physical property parameters or single well logging curves, and classify sweet spot reservoirs by setting cutoff values. However, the above methods have limited reference information and data, resulting in low reliability and making it impossible to accurately and reliably determine the sweet spots in shale oil. Summary of the Invention

[0007] The purpose of this application is to provide a method, apparatus, and equipment for determining shale oil sweet spots, in order to solve the problem of the inability to accurately and reliably determine shale oil sweet spots.

[0008] To solve the above-mentioned technical problems, the first aspect of this specification provides a method for determining the sweet spot of shale oil, including:

[0009] Obtain a reservoir identification model pre-constructed for the 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, wherein each level includes at least one element.

[0010] Based on the membership relationships of each level element in the reservoir identification model and the preset importance discrimination index, the degree of correlation between each level element in the reservoir identification model is determined.

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

[0012] Obtain the values ​​of multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir;

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

[0014] In some embodiments, the reservoir identification model is constructed in the following manner:

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

[0016] Based on multiple preset evaluation indicators and the well logging information, some reservoir attribute parameters are selected from the multiple reservoir attribute parameters as reservoir parameters in the reservoir identification model, and the membership relationship between the reservoir parameters and the multiple evaluation indicators is determined. The multiple evaluation indicators are determined based on the sweet spot index.

[0017] Based on the membership relationship 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, a subset of reservoir attribute parameters are selected from the plurality of reservoir attribute parameters as reservoir parameters in the reservoir identification model, and the membership relationship between the reservoir parameters and the plurality of evaluation indicators is determined, including:

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

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

[0021] Based on the whitening number information, the reservoir parameter is selected from the plurality of reservoir attribute parameters, and the membership relationship 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 scheme layer composed of multiple reservoir parameters. Any one of the reservoir parameters belongs to at least one evaluation indicator, and the multiple evaluation indicators belong 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, oil saturation, and 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, the degree of correlation between elements at each level in the reservoir identification model is determined based on the membership relationships of elements at each level and a preset importance discrimination index, including:

[0026] Based on the preset importance discrimination index and the membership relationship, a judgment matrix of the reservoir identification model is constructed. Any element in the judgment matrix is ​​used to characterize the relative importance of the influence of two elements belonging to the same target element in the current layer on the target element.

[0027] A consistency check is performed on the judgment matrix, and if the consistency check of the judgment matrix is ​​passed, the judgment matrix is ​​used as the degree of association.

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

[0029] Based on the hierarchical structure and correlation degree in the reservoir identification model, the weight coefficients corresponding to each reservoir parameter are determined, and the weight coefficients corresponding to each reservoir parameter are used as the importance coefficients.

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

[0031] Based on the importance coefficients of each reservoir parameter, the values ​​of the multiple reservoir parameters are weighted and summed to obtain the sweet spot index.

[0032] Based on the dessert index and the preset correspondence between dessert levels and dessert index ranges, the dessert level of the target reservoir is determined.

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

[0034] Obtain the logging information of the target reservoir, and determine the value of each reservoir parameter based on the logging information and the preset calculation method of each reservoir parameter.

[0035] The second aspect of this specification provides an apparatus for determining the sweet spot of shale oil, comprising:

[0036] The model acquisition module is used to acquire a reservoir identification model pre-constructed for the 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. Each level includes at least one element.

[0037] The qualitative analysis module is used to determine the degree of correlation between elements at each level in the reservoir identification model based on the membership relationship of each level element and the preset importance discrimination index.

[0038] A quantitative calculation module is used to calculate the importance coefficient of each reservoir parameter relative to the sweet spot index based on the correlation degree.

[0039] The data acquisition module is used to acquire the values ​​of multiple reservoir parameters in the 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 values ​​of each reservoir parameter and the importance coefficient of each reservoir parameter, so as to identify the sweet spot of shale oil in the target reservoir.

[0041] A third aspect of this specification provides an electronic device, comprising: 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 executes the computer instructions to implement the steps of the method described in any of the first aspects.

[0042] A fourth aspect of this specification provides a computer storage medium storing computer program instructions that, when executed, implement the steps of the method described in any of the first aspects.

[0043] The method for determining shale oil sweet spots provided in the embodiments of this specification involves acquiring a reservoir identification model pre-constructed for the target reservoir. The reservoir identification model includes multiple reservoir parameters related to the sweet spot index of the target reservoir, and a multi-level structural relationship between these parameters and the sweet spot index, with each level including at least one element. Based on the membership relationships of elements at each level in the reservoir identification model and a preset importance discrimination index, the degree of correlation between elements at each level is determined. Based on the degree of correlation, 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 ​​of each reservoir parameter and its importance coefficient, the sweet spot index of the target reservoir is determined to identify shale oil sweet spots in the target reservoir. This application calculates the sweet spot index based on a pre-established reservoir identification model that characterizes the hierarchical relationship between reservoir parameters and sweet spot indicators of the target reservoir. This approach yields a more practical, systematic, and realistic correspondence between reservoir parameters and sweet spot indicators, providing a foundation for accurate calculation of the sweet spot index. Furthermore, based on the membership relationships of elements at each level in the reservoir identification model and pre-defined importance discrimination indicators, this application enables qualitative analysis of each level element's relationship to the sweet spot index, obtaining the degree of correlation between elements at each level in the reservoir identification model. Then, based on the degree of correlation, a quantitative evaluation of the reservoir parameters of the target reservoir can be achieved, obtaining the importance coefficient of each reservoir parameter relative to the sweet spot index. Finally, by combining the importance coefficients and values ​​of each reservoir parameter, the sweet spot index of the target reservoir can be determined, enabling more accurate and reliable identification of shale oil sweet spots. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 The diagram shown is a schematic representation of a method for determining shale oil sweetness according to an embodiment of this application.

[0046] Figure 2 The image shown is a schematic diagram of a reservoir identification model provided in an embodiment of this application;

[0047] Figure 3 The diagram shown is a schematic of a device for determining shale oil sweetness according to an embodiment of this application;

[0048] Figure 4The diagram shown is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0049] 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 with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0050] As mentioned earlier, current research on methods for determining sweet spots in shale oil reservoirs focuses more on the calculation of parameters such as physical properties and brittleness, as well as lithological identification. In terms of sweet spot classification and evaluation of shale oil reservoirs, the method of setting cutoff values ​​for parameters is used to classify sweet spot reservoirs. However, this method has limited reference information and data, and the results are unreliable, making it impossible to accurately and reliably determine the sweet spots in shale oil.

[0051] To address the aforementioned issues, this application provides a method for determining the sweet spot of shale oil, specifically including: acquiring 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, each level including at least one element; determining the degree of correlation between the elements at each level in the reservoir identification model based on the membership relationship of each level element and a preset importance discrimination index; calculating the importance coefficient of each reservoir parameter relative to the sweet spot index based on the degree of correlation; acquiring the numerical values ​​corresponding to the multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir; and determining the sweet spot index of the target reservoir based on the numerical values ​​corresponding to each reservoir parameter and the importance coefficient of each reservoir parameter, thereby identifying the sweet spot of shale oil in the target reservoir.

[0052] This application calculates the sweet spot index based on a pre-established reservoir identification model that characterizes the hierarchical relationship between reservoir parameters and sweet spot indicators of the target reservoir. This approach yields a more practical, systematic, and realistic correspondence between reservoir parameters and sweet spot indicators, providing a foundation for accurate calculation of the sweet spot index. Furthermore, based on the membership relationships of elements at each level in the reservoir identification model and pre-defined importance discrimination indicators, this application enables qualitative analysis of each level element's relationship to the sweet spot index, obtaining the degree of correlation between elements at each level in the reservoir identification model. Then, based on the degree of correlation, a quantitative evaluation of the reservoir parameters of the target reservoir can be achieved, obtaining the importance coefficient of each reservoir parameter relative to the sweet spot index. Finally, by combining the importance coefficients and values ​​of each reservoir parameter, the sweet spot index of the target reservoir can be determined, enabling more accurate and reliable identification of shale oil sweet spots.

[0053] It is understood that the methods provided in the embodiments of this application can be applied to electronic devices, which can refer to electronic devices with data computing, processing, and storage capabilities. These electronic devices can be terminals such as PCs (Personal Computers), tablets, smartphones, wearable devices, and intelligent robots; they can also be servers. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. This application does not impose any limitations on this.

[0054] The method for determining shale oil sweet spots provided in the embodiments of this application will be described below with reference to the accompanying drawings.

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

[0056] S101: Obtain the reservoir identification model pre-built for the target reservoir.

[0057] 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, wherein each level includes at least one element.

[0058] It is understood that the reservoir identification model aims to calculate the sweet spot index, using multiple reservoir parameters of the target reservoir as the basic elements for calculating the sweet spot index. There can be a multi-layered structure between the basic elements and the target, with a hierarchical relationship between lower-level elements and their corresponding upper-level elements; that is, a lower-level element belongs to at least one element in its corresponding upper-level layer. This embodiment of the application considers that directly determining the relationship between reservoir parameters and the sweet spot index may lead to significant complexity and uncertainty. By determining the effect of lower-level elements on upper-level elements through a hierarchical structure, a more accurate and reliable hierarchical relationship between reservoir parameters and the sweet spot index can be obtained, providing a foundation for the accurate calculation of the sweet spot index.

[0059] It is understood that a reservoir identification model can be constructed by analyzing the correlation between reservoir attribute parameters of the target reservoir and combining them with a preset sweet spot index evaluation index. Here, reservoir attribute parameters can be understood as basic attribute parameters related to the target reservoir, such as porosity, oil saturation, reservoir recoverability index, organic carbon content, brittleness index, and stiffness coefficient. This application does not impose such limitations.

[0060] In some embodiments, the reservoir attribute parameters of the target reservoir can be clustered based on the maximum membership principle, and a preset evaluation index can be used as the clustering category to constrain the clustering results of the reservoir attribute parameters, thereby determining 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 scheme layer. The target layer may correspond to the reservoir evaluation objective of sweet spot index calculation; the criterion layer may correspond to the rules, methods, and calculation logic of sweet spot index calculation; and the scheme layer may correspond to the basic elements participating in the sweet spot index calculation, i.e., the reservoir attribute parameters of the target reservoir. It is understood that the elements corresponding to the criterion layer and / or scheme layer may have a certain correlation. Furthermore, the criterion layer and / or scheme layer may be multi-layered, i.e., may include multiple criterion layers and / or scheme layers. Alternatively, the elements corresponding to the criterion layer and / or scheme layer may not be correlated. Furthermore, the criterion layer and / or scheme layer may be a single layer, specifically generated based on the target layer and the actual application scenario; this application does not impose any limitations on this.

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

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

[0064] Based on multiple preset evaluation indicators and the well logging information, some reservoir attribute parameters are selected from the multiple reservoir attribute parameters as reservoir parameters in the reservoir identification model, and the membership relationship between the reservoir parameters and the multiple evaluation indicators is determined. The multiple evaluation indicators are determined based on the sweet spot index.

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

[0066] It is understandable that reservoir identification models can be constructed by analyzing reservoir parameters and well logging information of the target reservoir to determine reservoir parameters related to the sweet spot index. The model is then built by combining the relationship between reservoir parameters and preset evaluation indicators, as well as the correlation between reservoir parameters themselves. The structure of the constructed reservoir identification model will be described below with reference to the accompanying figures, and will not be elaborated upon here.

[0067] In some embodiments, selecting reservoir parameters from multiple reservoir attribute parameters and determining the membership relationship between reservoir parameters and multiple evaluation indicators may include: determining at least one evaluation indicator for evaluating the sweet spot as a preset set of multiple evaluation indicators based on the evaluation criteria of the sweet spot level; then, based on the correlation between each reservoir attribute parameter and well logging information, and the principle of maximum membership, performing cluster analysis on the reservoir attribute parameters to determine the evaluation indicator to which each reservoir attribute parameter belongs, thereby achieving the purpose of selecting reservoir parameters and determining the membership relationship between reservoir parameters and evaluation indicators. It is understood that some reservoir attribute parameters may not belong to any of the evaluation indicators; therefore, when selecting reservoir parameters, reservoir attribute parameters belonging to any one of the evaluation indicators can be used as the selected reservoir parameters.

[0068] In some embodiments, certain reservoir parameters may be correlated, and the relationship between reservoir parameters and evaluation indicators can be indirect. For example, reservoir parameter A and reservoir parameter B are correlated, so reservoir parameter A and reservoir parameter B can belong to parameter C, and parameter C belongs to evaluation indicator D. Reservoir parameter A and reservoir parameter B are directly subordinate to parameter C and indirectly subordinate to evaluation indicator D, meaning the scheme layer is multi-layered. In other embodiments, reservoir parameters are not correlated, and the relationship between reservoir parameters and evaluation indicators can be direct, meaning the scheme layer is single-layered. This application does not impose any restrictions on this.

[0069] In some embodiments, reservoir parameters may belong 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, reservoir parameters may belong to one evaluation indicator. This application does not impose any restrictions on this.

[0070] It is understandable that by using the different relationships between the above 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 sweet spot indicators.

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

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

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

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

[0075] It is understandable that the whitening number can be used to characterize the membership degree 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, based on the principle of maximum membership, grey clustering analysis can be applied to classify and evaluate target reservoirs containing shale oil, according to the calculation requirements of the sweet spot index. The grey clustering analysis method is mainly based on the construction of a whitening weight function. By analyzing the whitening number of reservoir attribute parameters under different grey class conditions (i.e., different evaluation indicators), it can distinguish the evaluation indicators to which the analyzed object belongs and determine which category it should be classified into. Grey clustering analysis can predetermine the number and standards of evaluation indicators, avoiding situations where a certain category does not actually exist but other methods will definitely classify it. This better meets the actual needs of sweet spot index determination, considers the role of each reservoir attribute parameter in the classification evaluation, and uses fuzzy, overlapping classification boundaries between levels to divide the evaluation indicators into corresponding categories. The resulting reservoir identification model can contain richer information, better reflects reality, and the clustering results are more comprehensive, objective, reliable, and practical.

[0077] S102: Based on the membership relationship of each level element in the reservoir identification model and the preset importance discrimination index, determine the degree of correlation between each level element in the reservoir identification model.

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

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

[0080] In some embodiments, the degree of association can be the relative importance of the influence of two elements belonging to the same adjacent level on that adjacent level element. Accordingly, the preset importance discrimination index can be represented by the following table:

[0081] Table 1

[0082] Importance scale Importance scale description 1 The two elements are of equal importance 3 The former is slightly more important than the latter. 5 The former is clearly more important than the latter. 7 The former is more important than the latter. 9 The former is far more important than the latter. 2、4、6、8 intermediate scale 1 / k, k = 1, 2, 3, ..., 9 When the order of elements is changed, their importance ratios become reciprocals.

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

[0084] Based on the preset importance discrimination index and the membership relationship, a judgment matrix of the reservoir identification model is constructed. Any element in the judgment matrix is ​​used to characterize the relative importance of the influence of two elements belonging to the same target element in the current layer on the target element.

[0085] A consistency check is performed on the judgment matrix, and if the consistency check of the judgment matrix is ​​passed, the judgment matrix is ​​used as the degree of association.

[0086] As can be understood, the judgment matrix can represent the importance of each element in any layer of attributes relative to the elements in its adjacent layers. The method for constructing the judgment matrix will be introduced below and will not be elaborated here.

[0087] It is understandable that consistency checks are used to determine whether the degree of association between elements at different levels is contradictory. For example, if 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 consistency check matrix can ensure the rationality of the consistency check matrix construction.

[0088] In some embodiments, the judgment matrix can be generated based on membership relationships and user input information. Specifically, the user can input the importance of each element to elements in its adjacent layers based on a preset importance discrimination index. This importance can be represented numerically, and then the judgment matrix can be constructed based on the user input information and the membership relationships in the reservoir identification model.

[0089] In some embodiments, the importance of each element in the reservoir identification model to the elements of its adjacent layers can be analyzed based on well logging information of the target reservoir combined with experimental simulation analysis data. Then, a judgment matrix can be constructed based on the analysis results and the membership relationships in the reservoir identification model. Specifically, analyzing the importance of each element in the reservoir identification model to the elements of its adjacent layers based on well logging information of the target reservoir can include: fitting the change relationship between each element and the elements of its adjacent layers based on well logging information and experimentally acquired data, determining the degree of influence of each element on the elements of its adjacent layers, and then constructing a judgment matrix based on the degree of influence of each element on the elements of its adjacent layers.

[0090] In some embodiments, the consistency index can be calculated by calculating the largest eigenvalue of the judgment matrix, and then a consistency ratio can be calculated based on the largest eigenvalue. Then, based on the random consistency index, the consistency ratio is calculated. If the consistency ratio meets a preset threshold, the consistency verification is considered successful; otherwise, the consistency verification fails, and the correlation degree needs to be adjusted until the consistency verification passes. In some embodiments, the preset threshold can be 0.1. The consistency verification process will be described below with reference to specific embodiments, and will not be repeated here.

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

[0092] It can be understood that the importance coefficient represents the importance weight of each reservoir parameter when calculating the sweet spot index using reservoir parameters. Reservoir parameters can serve as evaluation parameters for the sweet spot index. The importance coefficient of each reservoir parameter relative to the sweet spot index can be obtained based on the correlation between the layers obtained from the reservoir rating model.

[0093] It can be understood that the degree of correlation is a measure of the importance of attributes at the same level using a unified standard. This can include the measurement of importance between levels, and the importance coefficient is the importance coefficient of the reservoir parameters in the lowest-level scheme layer to the sweet spot index of the target layer. In other words, determining the degree of correlation involves a qualitative analysis of each element in the reservoir identification model, while calculating the importance coefficient involves a quantitative calculation of the reservoir parameters in the scheme layer of the reservoir identification model. Based on the hierarchical structure of the constructed reservoir identification model, the degree of correlation is obtained by comparing the importance of elements at the same level using a unified standard. Then, the importance coefficient of each reservoir parameter to the sweet spot index is calculated using mathematical methods. This avoids the significant complexity and uncertainty caused by directly judging the relationship between too many reservoir parameters and the target layer, and thus possesses strong practicality, systematicity, and realism.

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

[0095] Based on the hierarchical structure and correlation degree in the reservoir identification model, the weight coefficients corresponding to each reservoir parameter are determined, and the weight coefficients corresponding to each reservoir parameter are used as the importance coefficients.

[0096] In some embodiments, the degree of correlation 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 eigenvectors of the judgment matrix can be calculated, and then the weight coefficients between each level can be constructed based on the eigenvectors corresponding to the largest eigenvalue of the judgment matrix. Then, the weight coefficients of each level relative to the target level can be determined based on the weight coefficients between each level, thereby obtaining the weight coefficients of the reservoir parameters in the scheme layer relative to the sweet spot index of the target layer. The specific calculation process of the weight coefficients will be described below in conjunction with specific embodiments, and will not be elaborated here.

[0097] S104: Obtain the values ​​of multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir.

[0098] It is understandable 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. Alternatively, the values ​​of the reservoir parameters can be calculated based on the logging information of the target reservoir.

[0099] In some embodiments, shale oil reservoirs are more sensitive to logging response characteristics such as acoustic, density, resistivity, and nuclear magnetic resonance (NMR). NMR 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 acoustic transit time curves and resistivity curves; and the rigidity coefficient can be calculated using density, P-wave and S-wave transit time, clay content, etc., and the brittleness index can be calculated using the elastic modulus method.

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

[0101] Obtain the logging information of the target reservoir, and determine the value of each reservoir parameter based on the logging information and the preset calculation method of each reservoir parameter.

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

[0103] In some embodiments, the porosity of the target reservoir can be calculated using a regression statistical method based on core analysis data, and the porosity logging curve with the best regional correlation can be selected for regression. The oil saturation can be calculated using the Archie formula, and the required rock electrical parameters a, b, m, and n in the Archie formula can be determined through rock electrical experiments. The reservoir recoverability index can be calculated using a formula that considers the negative effects of kerogen and bitumen; the specific calculation method is as follows:

[0104]

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

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

[0107]

[0108] in, It can represent the total porosity of the formation. and These can represent the volume fractions of asphalt and water, respectively. ρ can represent the volume fraction of shale oil. oil ρ can represent the density (g / cm3) of shale oil. bIt can represent bulk 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, which can be based on the amplitude difference between the two curves of acoustic transit time and resistivity; the brittleness index can be calculated using the elastic modulus method, which can be based on density, longitudinal and transverse wave transit time, and clay content to calculate the rigidity coefficient.

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

[0111] It is understandable that the importance coefficient can characterize the significance of the corresponding reservoir parameter in the calculation of the sweet spot index. The sweet spot index can be calculated by performing operations between the importance coefficient and the corresponding reservoir parameter values. For example, the importance coefficient can be used as the weight of the reservoir parameter value in the sweet spot index, and the sweet spot index can be obtained by weighted summing of the reservoir parameter values. It is also understood that other calculation methods can be used for the operation between the reservoir parameter values ​​and the importance coefficient, which will not be elaborated upon here.

[0112] It is understandable that the sweet spot index can be used to evaluate the sweet spot of the target reservoir, that is, to evaluate or rate the sweet spot reservoir in a quantitative way.

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

[0114] Based on the importance coefficients of each reservoir parameter, the values ​​of the multiple reservoir parameters are weighted and summed to obtain the sweet spot index.

[0115] Based on the dessert index and the preset correspondence between dessert levels and dessert index ranges, the dessert level of the target reservoir is determined.

[0116] In some embodiments, the preset dessert level may include, for example, a first-level dessert zone, a second-level dessert zone, a third-level dessert zone, etc. Each dessert zone may correspond to a numerical range of a dessert index, that is, a dessert index range. The dessert level corresponding to the target reservoir can be determined by matching the calculated dessert index of the target reservoir with the dessert index range and based on the matched dessert index region.

[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 coefficients can be used as weights for each reservoir parameter in this model, constructing a weighted summation model of multiple reservoir parameters. The result of the weighted summation can be used as a sweet spot index. Furthermore, for the target reservoir, reservoir parameters from different well sections can be substituted into this sweet spot reservoir prediction model to obtain sweet spot indices for different well sections. Based on these sweet spot indices, the sweet spot level of different well sections can be determined, enabling continuous prediction of the sweet spot level of the sweet spot reservoir.

[0118] The following section uses the target layer as the sweet spot index, with both the criterion layer and the scheme layer comprising one layer, as an example to introduce the reservoir identification model provided in the embodiments of this application.

[0119] Figure 2 The image shown is a schematic diagram of a reservoir identification model provided in an embodiment of this 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 scheme layer composed of multiple reservoir parameters. Any one of the reservoir parameters belongs to at least one evaluation indicator, and the multiple evaluation indicators belong to the sweet spot indicators.

[0120] It is understandable that, due to the varying importance and weight of different reservoir parameters in the sweet spot index calculation process, it is necessary to calculate the weight coefficients of the hierarchical elements. Specifically, this can be achieved by calculating the correlation between each level, for example, by constructing a judgment matrix using the Analytic Hierarchy Process (AHP), and then calculating the weight coefficients of each reservoir parameter. The AHP integrates qualitative analysis and quantitative calculation, establishing a recursive hierarchical structure (i.e., a reservoir identification model) based on multiple reservoir parameters and evaluation indicators in a logical order. By comparing the importance of elements at the same level, using a unified standard for measurement, and constructing a judgment matrix, the weight coefficients of each element to the target layer are finally calculated mathematically. This avoids the significant complexity and uncertainty arising from directly judging the relationship between too many elements and the target layer, and possesses strong practicality, systematicity, and realism.

[0121] It is understandable that the target layer is the rating of the reservoir sweet spot, which can be done by calculating the sweet spot index; the criterion layer can be the three important indicators calculated from the sweet spot index, that is, the evaluation indicators, which can also be understood as the three parts of the reservoir sweet spot rating. The criterion layer can be multi-layered, but in this embodiment, based on the application scenario of sweet spot evaluation, a single criterion layer can be selected; the scheme layer consists of the attribute elements belonging to the above three parts, that is, the three evaluation indicators, which can be understood as the specific evaluation parameters participating in the sweet spot rating. Due to the strong correlation between the evaluation parameters, the scheme layer can be multi-layered, but in this embodiment, based on the application scenario of sweet spot evaluation, the five reservoir parameters determined have no clear correlation in physical meaning, so the scheme layer can be single-layered. The number of attribute parameters in the scheme layer can be determined based on the specific evaluation indicators of the criterion layer. It is understood that... Figure 2 The reservoir identification model in this application is one example. Other model structures may be used in other embodiments, such as different membership relationships, different number of criterion layers and / or scheme layers, and may also include other evaluation indicators and / or reservoir parameters. This application does not limit these.

[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, oil saturation, and 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 be based on Figure 2 Taking the hierarchical structure of the reservoir identification model as an example, the importance coefficients of each reservoir parameter relative to the sweet spot index are calculated by combining the analytic hierarchy process (AHP) to establish a sweet spot reservoir prediction model for shale oil reservoirs in a certain region, so as to rate the sweet spot reservoirs in that region.

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

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

[0127] S2: Construct a reservoir identification model. It can be understood that the constructed reservoir identification model can be, for example... Figure 2 As shown, the specific construction method can be found in [reference needed]. Figure 1 The description of the relevant parts in step S101 is not repeated here.

[0128] S3: By constructing a judgment matrix, determine the importance of each element in the reservoir identification model to the elements of adjacent layers.

[0129] Specifically, the number of elements in the k-th layer of the reservoir identification model can be denoted as n. (k) (For simplicity, let's denote it as n), the set of elements is... any two elements and For a certain element in the previous layer The ratio of importance is This element relates to a certain element in the previous level. The ratio of the importance of each pair of elements is expressed using a matrix. In other words, matrix U can be called the k-th layer element X. (k) For the parent element X (k-1) The judgment matrix can be denoted as:

[0130]

[0131] In this embodiment, the importance of two elements can be measured using numbers 1 to 9 and their reciprocals. For example, in Table 1 above, the number 1 in Table 1 can represent that the two elements are of equal importance, the number 9 can represent that the former is extremely important than the latter, and the reciprocal of the number represents that the ratio of importance is reciprocal, which is equivalent to changing the order of the elements.

[0132] In this embodiment, the importance of attributes between different layers can be determined based on sensitivity analysis of reservoir properties of various elements in the region and common sense judgment. A judgment matrix for each layer is then constructed according to the importance scaling table shown in Table 1. To reduce complexity caused by too many elements, the judgment matrix can be constructed by including only relevant elements under each layer.

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

[0134]

[0135] Wherein, matrix U (2) This can be a judgment matrix for the three elements of the criterion layer and the dessert index of the target layer. This can be a judgment matrix for the first three elements of the scheme layer and the first element of the criterion layer. Since the scheme layers under the last two elements of the criterion layer each contain only one element, a judgment matrix is ​​not required. Therefore, the two judgment matrices here can be represented as follows:

[0136]

[0137] Understandable. Figure 2 The judgment matrix corresponding to porosity in Judgment matrix corresponding to oil saturation and the judgment matrix corresponding to the reservoir recoverability index This can form a judgment matrix between the first three elements of the scheme layer and the first element of the criterion layer. Matrix of organic carbon content This can be the judgment matrix of the fourth element in the scheme layer and the second element in the criterion layer. The matrix corresponding to the brittleness index This can be the judgment matrix of the fifth element in the scheme layer and the third element in the criterion layer. Judgment matrix corresponding to reservoir properties Judgment matrix corresponding to source rock characteristics and the judgment matrix corresponding to engineering brittleness This can form a judgment matrix U of the three elements in the criterion layer for the dessert index in the scheme layer. (2) .

[0138] S4: Check the consistency of the matrix.

[0139] Understandably, to avoid contradictory situations where the importance of elements in different levels contradicts each other—for example, element A might be more important than element B, element B more important than element C, but element C more important than element A—a consistency check is needed on the judgment matrix to ensure its reasonable construction. Specifically, the largest eigenvalue of the judgment matrix can be calculated, along with the Concordance Index (CI). The specific calculation method is as follows: Where n can represent the order of the judgment matrix; λ max This can represent the largest eigenvalue of the judgment matrix. Then, based on the RI coefficient table corresponding to the Random Index (RI), the RI coefficient for the corresponding n value can be found, and the Consistent Ratio (CR) can be calculated. The specific calculation method for the Consistent Ratio (CR) is as follows: If the consistency ratio CR < 0.1, the consistency of the judgment matrix is ​​acceptable; otherwise, the judgment matrix is ​​unreasonable and the importance of its elements needs to be adjusted until it passes the consistency test. The largest eigenvalue of the judgment matrix constructed in step 3 can be calculated and its consistency tested.

[0140] In this embodiment, the RI coefficients corresponding to the random consistency index RI are shown in Table 2 below:

[0141] Table 2

[0142] 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

[0143] In this embodiment, for the judgment matrix U constructed in step S3 (2) Its largest eigenvalue λ max =3.054, substituting this into the formula for calculating the consistency index, we can obtain the judgment matrix U. (2) The consistency index CI = 0.027, from which the judgment matrix U can be obtained. (2) Consistency ratio The consistency of the judgment matrix is ​​acceptable; for the judgment matrix Its largest eigenvalue λ max =3.018, substituting this into the formula for calculating the consistency index, the judgment matrix can be obtained. The consistency index CI = 0.009, and the judgment matrix... Consistency ratio The consistency of the judgment matrix is ​​acceptable.

[0144] S5: Calculate the weight coefficients of each element based on the hierarchical structure of the reservoir identification model and the judgment matrix, and then generate a sweet spot reservoir prediction model.

[0145] Specifically, we can calculate the eigenvector W = (w1 w2 … w) corresponding to the largest eigenvalue of the judgment matrix. 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 eigenvectors, that is, the eigenvectors W of each judgment matrix after averaging. arg It can be:

[0146]

[0147] Among them, i max can represent the maximum order value of the corresponding judgment matrix, and i can represent an element in the eigenvector.

[0148] Furthermore, the weight vector from layer k to layer (k-1) can be constructed based on the eigenvectors corresponding to the largest eigenvalues ​​of the judgment matrices at each layer. Where j is the number of judgment vectors in the k-th layer. Weight vector P (k) It can be represented as:

[0149]

[0150] Furthermore, the weight coefficient matrix of the k-th layer to the 1st layer (target layer) can be denoted as σ. (k) And calculate the weight coefficients of each layer to the target layer. The weight coefficient of the k-th 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 steps S2 and S3, the parameters of each reservoir (i.e., Figure 2 The weighting coefficients of reservoir parameters (porosity, oil saturation, reservoir recoverability index, organic carbon content, and brittleness index) to the sweet spot index are obtained, resulting in weighting coefficients σ = (0.253 0.440 0.097 0.042 0.168). A sweet spot reservoir prediction model can be generated based on these weighting coefficients. In this embodiment, the sweet spot reservoir prediction model can be expressed as:

[0151]

[0152] Where S can represent the dessert index, It can represent 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.

[0153] The method for determining shale oil sweet spots provided in this application constructs a reservoir identification model using the maximum membership principle. Based on this model, the weight coefficients of reservoir parameters are calculated using the analytic hierarchy process (AHP). Using these weight coefficients and reservoir parameters, a model for determining shale oil sweet spots is constructed. This model allows for the calculation of sweet spot indices, which in turn enable the grading and evaluation of sweet spot reservoirs. This significantly improves the prediction accuracy of sweet spot reservoirs. Compared with actual production data, the grading and evaluation results of sweet spot reservoirs are more accurate and reliable, possessing stronger practical geological significance. Furthermore, this method can be widely applied to the evaluation of various reservoirs, resulting in higher application effectiveness and providing crucial technical support for regional exploration.

[0154] This application also provides an apparatus for determining shale oil sweet spots. Figure 3 The diagram shown is a schematic of a device for determining shale oil sweetness according to an embodiment of this application.

[0155] like Figure 3 As shown, the apparatus 300 for determining the shale oil sweet spot may include:

[0156] The model acquisition module 301 is used to acquire a reservoir identification model pre-constructed for the 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. Each level includes at least one element.

[0157] The qualitative analysis module 302 is used to determine the degree of correlation between the elements at each level in the reservoir identification model based on the membership relationship of each level element in the reservoir identification model and the preset importance discrimination index.

[0158] 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.

[0159] The data acquisition module 304 is used to acquire the values ​​of multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir.

[0160] The sweet spot determination module 305 is used to determine the sweet spot index of the target reservoir based on the numerical values ​​of each reservoir parameter and the importance coefficient of each reservoir parameter, so as to identify the sweet spot of shale oil in the target reservoir.

[0161] The descriptions and functions of the above units can be understood by referring to the section on methods for determining shale oil sweet spots, and will not be repeated here.

[0162] This specification also provides a computer storage medium storing computer program instructions, which, when executed, implement the steps of the method for determining the sweet spot of shale oil described above.

[0163] This specification also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method for determining the sweet spot of shale oil described above.

[0164] This invention also 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 Taking the example of a connection between China and Israel via a bus.

[0165] Processor 401 may be a central processing unit (CPU). Processor 401 may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof.

[0166] Memory 402, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the shale porosity determination method in this embodiment of the invention (e.g., Figure 3 The model acquisition module 301, qualitative analysis module 302, quantitative calculation module 303, data acquisition module 304, and sweet spot determination module 305 are shown. The processor 401 executes various functional applications and data processing by running non-transitory software programs, instructions, and modules stored in the memory 402, thereby realizing the shale oil sweet spot determination method in the above method embodiment.

[0167] The memory 402 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 401, etc. Furthermore, the memory 402 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 402 may optionally include memory remotely located relative to the processor 401, and these remote memories may be connected to the processor 401 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0168] The one or more modules are stored in the memory 402, and when executed by the processor 401, they perform actions such as... Figure 1 The method for determining the sweet spot of shale oil in the illustrated embodiment.

[0169] The specific details of the aforementioned electronic device can be understood by referring to the relevant descriptions and effects in the above method embodiments, and will not be repeated here.

[0170] This specification also provides a computer storage medium storing computer program instructions, which, when executed, implement the steps of the method for determining the sweet spot of shale oil described above.

[0171] This specification also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method for determining the sweet spot of shale oil described above.

[0172] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0173] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, please refer to each other. The focus of each embodiment is to describe the differences from other embodiments.

[0174] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions.

[0175] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0176] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute certain parts of the methods of various embodiments of this application.

[0177] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0178] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0179] Although this application has been described through embodiments, those skilled in the art will know that this application has many modifications and variations without departing from the spirit of this application, and it is intended that the appended claims cover such modifications and variations without departing from the spirit of this application.

Claims

1. A method for determining the sweet spot of shale oil, characterized in that, include: Obtain a reservoir identification model pre-constructed for the 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, wherein each level includes at least one element. Based on the membership relationships of each level element in the reservoir identification model and the preset importance discrimination index, the degree of correlation between each level element in the reservoir identification model is determined. Based on the correlation, the importance coefficient of each reservoir parameter relative to the sweet spot index is calculated; Obtain the values ​​of multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir; Based on the numerical values ​​of each reservoir parameter and the importance coefficient of each reservoir parameter, the sweet spot index of the target reservoir is determined in order to identify the sweet spot of shale oil in the target reservoir. 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 scheme layer composed of multiple reservoir parameters. Any one of the reservoir parameters belongs to at least one evaluation indicator, and the multiple evaluation indicators belong to the sweet spot indicators. The reservoir parameters include porosity, oil saturation, reservoir recoverability index, organic carbon content, and brittleness index. The multiple evaluation indicators include reservoir physical properties, source rock characteristics, and engineering brittleness. The porosity, oil saturation, and 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.

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

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

4. The method according to claim 1, characterized in that, Based on the membership relationships of elements at each level in the reservoir identification model and the preset importance discrimination index, the degree of correlation between elements at each level in the reservoir identification model is determined, including: Based on the preset importance discrimination index and the membership relationship, a judgment matrix of the reservoir identification model is constructed. Any element in the judgment matrix is ​​used to characterize the relative importance of the influence of two elements belonging to the same target element in the current layer on the target element. A consistency check is performed on the judgment matrix, and if the consistency check of the judgment matrix is ​​passed, the judgment matrix is ​​used as the degree of association.

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

6. The method according to claim 1, characterized in that, Based on the numerical values ​​of each reservoir parameter and the importance coefficient of each reservoir parameter, the sweet spot index of the target reservoir is determined to identify the sweet spot of shale oil in the target reservoir, including: Based on the importance coefficients of each reservoir parameter, the values ​​of the multiple reservoir parameters are weighted and summed to obtain the sweet spot index. Based on the dessert index and the preset correspondence between dessert levels and dessert index ranges, the dessert level of the target reservoir is determined.

7. The method according to claim 1, characterized in that, Obtaining the values ​​of multiple reservoir parameters in the reservoir identification model corresponding to the target reservoir, including: Obtain the logging information of the target reservoir, and determine the value of each reservoir parameter based on the logging information and the preset calculation method of each reservoir parameter.

8. A device for determining shale oil sweet spots, characterized in that, include: The model acquisition module is used to acquire a reservoir identification model pre-constructed for the 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. Each level includes at least one element. The qualitative analysis module is used to determine the degree of correlation between elements at each level in the reservoir identification model based on the membership relationship of each level element and the preset importance discrimination index. A quantitative calculation module is used to calculate the importance coefficient of each reservoir parameter relative to the sweet spot index based on the correlation degree. The data acquisition module is used to acquire the values ​​of multiple reservoir parameters in the 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 values ​​of each reservoir parameter and the importance coefficient of each reservoir parameter, so as to identify the sweet spot of shale oil in the target reservoir. 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 scheme layer composed of multiple reservoir parameters. Any one of the reservoir parameters belongs to at least one evaluation indicator, and the multiple evaluation indicators belong to the sweet spot indicators. The reservoir parameters include porosity, oil saturation, reservoir recoverability index, organic carbon content, and brittleness index. The multiple evaluation indicators include reservoir physical properties, source rock characteristics, and engineering brittleness. The porosity, oil saturation, and 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.

9. An electronic device, characterized in that, include: A memory and a processor, the processor and the memory being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed, implement the steps of the method according to any one of claims 1 to 7.

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