Shale dominant lithofacies combination body screening method, device, equipment and storage medium

Abstract

Embodiments of the present application provide a shale dominant lithofacies association screening method, device, equipment and storage medium. The method comprises: obtaining petrological characteristics, total organic carbon content, pyrolysis analysis data of a shale core sample, and logging and pressure data of a target well; determining a plurality of division thresholds based on the pyrolysis and total organic carbon content data; obtaining a plurality of lithofacies types in combination with a preset lithofacies division threshold; performing source and reservoir function analysis on the lithofacies types according to the total organic carbon content, yield index and each threshold to obtain a plurality of source and reservoir function end members; dividing a vertical lithofacies association boundary according to the logging and pressure data, and identifying a plurality of lithofacies association types according to the spatial configuration of the source and reservoir function end members; obtaining physical property and oiliness parameters of each association, and screening to obtain a target lithofacies association. The method realizes accurate division of a strong heterogeneous shale dominant lithofacies association, and improves the efficiency and accuracy of shale oil exploration target selection.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration, and in particular to a method, apparatus, equipment and storage medium for screening dominant shale lithofacies assemblages. Background Technology

[0002] In the field of oil and gas exploration and development, the highly heterogeneous shale in continental rift basins possesses unique and complex sedimentary geological characteristics. Lithofacies types exhibit diverse distributions, with dramatic variations in vertical sedimentary sequences and complex and variable source-reservoir spatial relationships. The combination and configuration patterns among different facies also show significant differences. The enrichment capacity of shale oil and gas is not independently determined by the geological characteristics of a single facies, but is constrained by multiple factors, including the vertical combination and configuration of different facies and the synergistic effect of source-reservoir functions. The rationality of the source-reservoir configuration of facies assemblages directly affects the enrichment degree and exploration potential of shale oil and gas. Therefore, the accurate selection of advantageous facies assemblages has become a core and crucial link in the exploration and development of shale oil in continental rift basins.

[0003] Existing technologies for evaluating and screening oil-bearing properties and dominant lithofacies assemblages in shale exhibit significant limitations, lacking a systematic and scientific approach. In lithofacies assemblage analysis, current techniques typically rely on isolated analyses based on the physical and geochemical parameters of individual lithofacies, failing to consider the combined relationships and synergistic effects between different lithofacies, thus hindering a comprehensive reflection of the overall geological characteristics of the assemblages. Furthermore, the delineation of assemblage boundaries often depends on qualitative classification using macroscopic sedimentary interfaces, neglecting constraints based on reservoir pressure-related parameters. This fails to ensure that the delineated lithofacies assemblages possess independent reservoir pressure systems, resulting in geologically unfounded boundary delineations. Regarding parameter thresholds and source-reservoir-end-unit identification, existing technologies frequently employ uncorrected pyrolysis data to determine oil-bearing thresholds, leading to significant data distortion. Simultaneously, the identification of source-reservoir-end-unit types relies heavily on empirical values, lacking objective quantitative analytical basis and failing to accurately reflect the source-reservoir functional attributes of the lithofacies.

[0004] The aforementioned deficiencies in existing technologies result in a lack of systematic quantitative support for the screening process of lithofacies assemblages. The division of source and reservoir end-units is highly subjective, and the boundary delineation of lithofacies assemblages lacks precision. It is impossible to establish a precise correspondence between lithofacies types and source and reservoir functions, which in turn leads to low accuracy in identifying advantageous lithofacies assemblages. It is difficult to accurately locate target geological units with high oil and gas enrichment potential, and it cannot meet the actual needs of efficient exploration and development of highly heterogeneous shale oil in continental rift basins. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and storage medium for screening dominant shale lithofacies assemblages, in order to improve the efficiency and accuracy of shale oil exploration target selection.

[0006] In a first aspect, embodiments of this application provide a method for screening dominant shale lithofacies assemblages, including:

[0007] Obtain petrological characteristic data, total organic carbon content data, pyrolysis analysis data, and well logging data and pressure test data from the target well from shale core samples;

[0008] Based on the pyrolysis analysis data and total organic carbon content data, the thresholds for classifying organic matter abundance and oil content were determined.

[0009] The yield index is calculated based on the pyrolysis analysis data, and the yield index threshold is determined.

[0010] Based on the organic matter abundance classification threshold, the preset mineral content classification threshold, the preset sedimentary structure thickness classification threshold, petrological characteristic data, and total organic carbon content data, multiple lithofacies types are obtained.

[0011] Based on the total organic carbon content data, yield index, organic matter abundance classification threshold, oil-bearing classification threshold, and yield index classification threshold, source-reservoir function analysis is performed on the lithofacies type to obtain multiple source-reservoir function end-members.

[0012] Based on the well logging data and pressure test data, the vertical boundaries of the lithofacies assemblages are delineated, and multiple lithofacies assemblage types are identified within the assemblage boundaries based on the spatial configuration relationship of the source and reservoir functional units.

[0013] Obtain the physical properties and oil-bearing parameters of the lithofacies assemblage type, and screen the target lithofacies assemblage based on the physical properties and oil-bearing parameters.

[0014] In one possible implementation, determining the organic matter abundance threshold and oil content threshold based on the pyrolysis analysis data and total organic carbon content data includes:

[0015] Obtain the raw oil content data from the pyrolysis analysis data, and correct the raw oil content data based on the preset contamination correction model to obtain the corrected oil content data;

[0016] A first cross plot is generated based on the corrected oil content data and total organic carbon content data;

[0017] Identify the upper inflection point in the first intersection graph, and determine the organic matter abundance threshold based on the total organic carbon content value corresponding to the upper inflection point;

[0018] Identify the lower inflection point in the first intersection graph, and determine the oil content classification threshold based on the corrected oil content data value corresponding to the lower inflection point.

[0019] In one possible implementation, the yield index is calculated based on the pyrolysis analysis data, and a yield index threshold is determined, including:

[0020] The yield index is calculated based on the oil content parameters and hydrocarbon generation potential parameters in the pyrolysis analysis data.

[0021] The difference in hydrocarbon generation potential is determined based on the pyrolysis analysis data.

[0022] A second cross plot is generated based on the yield index and the difference in hydrocarbon generation potential.

[0023] Identify the intersection point of the trend line and the hydrocarbon generation potential difference axis in the second intersection plot, and determine the yield index division threshold based on the yield index value corresponding to the intersection point.

[0024] In one possible implementation, based on the organic matter abundance classification threshold, the preset mineral content classification threshold, the preset sedimentary structure thickness classification threshold, petrological characteristic data, and total organic carbon content data, multiple lithofacies types are obtained, including:

[0025] The shale core samples are divided according to the organic matter abundance classification threshold to obtain organic matter abundance classification results, which include organic matter-rich shale and organic matter-poor shale.

[0026] The shale core samples are divided according to a preset mineral content classification threshold to obtain mineral content classification results, which include clayey shale, carbonate shale, felsic shale and mixed shale.

[0027] The shale core samples are divided according to a preset sedimentary structure thickness threshold to obtain structural division results, which include laminated shale, layered shale and massive shale.

[0028] By cross-combining the results of organic matter abundance classification, mineral content classification, and structural classification, multiple lithofacies types are obtained.

[0029] In one possible implementation, source-reservoir function analysis is performed on the lithofacies type based on the total organic carbon content data, yield index, organic matter abundance classification threshold, oil-bearing classification threshold, and yield index classification threshold, resulting in multiple source-reservoir function end-members, including:

[0030] A third cross plot is generated based on the total organic carbon content data and yield index, and the sample points corresponding to each lithofacies type are projected onto the third cross plot.

[0031] Based on the total organic carbon content value corresponding to the organic matter abundance threshold, the oil content value corresponding to the oil content threshold, and the yield index value corresponding to the yield index threshold, the endmember type of each projected sample point is determined to obtain the endmember type corresponding to each sample point.

[0032] Based on the end-member types of the sample points included in each lithofacies type, the source-reservoir functional end-members corresponding to each lithofacies type are determined.

[0033] In one possible implementation, vertical lithofacies assemblages are delineated based on the well logging data and pressure test data, and multiple lithofacies assemblages are identified within these assemblages based on the spatial configuration of the source and reservoir functional units, including:

[0034] The sedimentary cycle interface is identified based on the sonic transit time logging curve and density logging curve in the logging data.

[0035] Based on the pressure test data, identify pressure change feature points, and determine pressure inflection points based on the pressure values ​​corresponding to the pressure change feature points;

[0036] The vertical boundary of the lithofacies assemblage is defined by taking the pressure inflection point as the main boundary and combining it with the sedimentary cycle interface.

[0037] Within the boundary of the assembly, obtain the source and storage function terminal type corresponding to each depth point, and generate a vertical distribution sequence of the source and storage function terminal types;

[0038] Based on the vertical stacking order of the source endmember and the storage endmember in the vertical distribution sequence, the types of the combined structures are identified to obtain a lower source upper storage type combined structure and an upper source lower storage type combined structure.

[0039] Based on the thickness ratio of source endmembers to reservoir endmembers and the interlayer distribution characteristics in the vertical distribution sequence, the types of the assemblies are identified as thick source-thin reservoir assemblies and thick reservoir-thin source assemblies.

[0040] Based on the frequency of alternating appearance of source end-units and storage end-units and the thickness of a single layer in the vertical distribution sequence, the combination of alternating source end-units and storage end-units with a single layer thickness less than a preset interlayer thickness threshold is identified as a source-storage symbiotic combination.

[0041] The combination of the vertically distributed sequence that does not contain source endmembers and storage endmembers but only interlayer endmembers is identified as an interlayer type combination.

[0042] In one possible implementation, the physical properties and oil-bearing parameters of the lithofacies assemblage type are obtained, and target lithofacies assemblages are screened based on the physical properties and oil-bearing parameters, including:

[0043] The physical properties and oil-bearing parameters of each lithofacies assemblage type are obtained, wherein the physical properties include porosity and permeability, and the oil-bearing parameters include total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index.

[0044] Calculate the overall average values ​​of the parameters for porosity, permeability, total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index for all lithofacies assemblage types;

[0045] Calculate the difference between the mean value of the parameters and the total mean value of the corresponding parameters for each type of lithofacies assemblage under each parameter;

[0046] The differences between the lithofacies assemblage types under each parameter are summed to obtain the comprehensive reference value corresponding to the lithofacies assemblage type;

[0047] The lithofacies assemblage types are sorted based on the comprehensive reference value, and the lithofacies assemblage types with the comprehensive reference value ranking first or second in the sequence are identified as the target lithofacies assemblage.

[0048] Secondly, embodiments of this application provide a shale dominant lithofacies assemblage screening device, comprising:

[0049] The acquisition module is used to acquire petrological characteristic data, total organic carbon content data, pyrolysis analysis data, and well logging data and pressure test data of the target well from shale core samples;

[0050] The analysis module is used to determine the organic matter abundance threshold and oil content threshold based on the pyrolysis analysis data and total organic carbon content data.

[0051] The analysis module is also used to calculate the yield index based on the pyrolysis analysis data and determine the yield index division threshold.

[0052] The type differentiation module is used to classify multiple lithofacies types based on the organic matter abundance classification threshold, the preset mineral content classification threshold, the preset sedimentary structure thickness classification threshold, petrological characteristic data, and total organic carbon content data.

[0053] The source-reservoir module is used to perform source-reservoir function analysis on the lithofacies type based on the total organic carbon content data, yield index, organic matter abundance classification threshold, oil-bearing classification threshold and yield index classification threshold, and obtain multiple source-reservoir function end-units.

[0054] The identification module is used to delineate the vertical boundaries of lithofacies assemblages based on the well logging data and pressure test data, and to identify multiple lithofacies assemblages types within the boundaries of the assemblages based on the spatial configuration relationship of the source and reservoir functional units.

[0055] The screening module is used to obtain the physical property parameters and oil-bearing parameters of the lithofacies assemblage type, and to screen the target lithofacies assemblage based on the physical property parameters and oil-bearing parameters.

[0056] In one possible implementation, the analysis module is specifically used for:

[0057] Obtain the raw oil content data from the pyrolysis analysis data, and correct the raw oil content data based on the preset contamination correction model to obtain the corrected oil content data;

[0058] A first cross plot is generated based on the corrected oil content data and total organic carbon content data;

[0059] Identify the upper inflection point in the first intersection graph, and determine the organic matter abundance threshold based on the total organic carbon content value corresponding to the upper inflection point;

[0060] Identify the lower inflection point in the first intersection graph, and determine the oil content classification threshold based on the corrected oil content data value corresponding to the lower inflection point.

[0061] In one possible implementation, the analysis module is further configured to:

[0062] The yield index is calculated based on the oil content parameters and hydrocarbon generation potential parameters in the pyrolysis analysis data.

[0063] The difference in hydrocarbon generation potential is determined based on the pyrolysis analysis data.

[0064] A second cross plot is generated based on the yield index and the difference in hydrocarbon generation potential.

[0065] Identify the intersection point of the trend line and the hydrocarbon generation potential difference axis in the second intersection plot, and determine the yield index division threshold based on the yield index value corresponding to the intersection point.

[0066] In one possible implementation, the type differentiation module is specifically used for:

[0067] The shale core samples are divided according to the organic matter abundance classification threshold to obtain organic matter abundance classification results, which include organic matter-rich shale and organic matter-poor shale.

[0068] The shale core samples are divided according to a preset mineral content classification threshold to obtain mineral content classification results, which include clayey shale, carbonate shale, felsic shale and mixed shale.

[0069] The shale core samples are divided according to a preset sedimentary structure thickness threshold to obtain structural division results, which include laminated shale, layered shale and massive shale.

[0070] By cross-combining the results of organic matter abundance classification, mineral content classification, and structural classification, multiple lithofacies types are obtained.

[0071] In one possible implementation, the source storage module is specifically used for:

[0072] A third cross plot is generated based on the total organic carbon content data and yield index, and the sample points corresponding to each lithofacies type are projected onto the third cross plot.

[0073] Based on the total organic carbon content value corresponding to the organic matter abundance threshold, the oil content value corresponding to the oil content threshold, and the yield index value corresponding to the yield index threshold, the endmember type of each projected sample point is determined to obtain the endmember type corresponding to each sample point.

[0074] Based on the end-member types of the sample points included in each lithofacies type, the source-reservoir functional end-members corresponding to each lithofacies type are determined.

[0075] In one possible implementation, the identification module is specifically used for:

[0076] The sedimentary cycle interface is identified based on the sonic transit time logging curve and density logging curve in the logging data.

[0077] Based on the pressure test data, identify pressure change feature points, and determine pressure inflection points based on the pressure values ​​corresponding to the pressure change feature points;

[0078] The vertical boundary of the lithofacies assemblage is defined by taking the pressure inflection point as the main boundary and combining it with the sedimentary cycle interface.

[0079] Within the boundary of the assembly, obtain the source and storage function terminal type corresponding to each depth point, and generate a vertical distribution sequence of the source and storage function terminal types;

[0080] Based on the vertical stacking order of the source endmember and the storage endmember in the vertical distribution sequence, the types of the combined structures are identified to obtain a lower source upper storage type combined structure and an upper source lower storage type combined structure.

[0081] Based on the thickness ratio of source endmembers to reservoir endmembers and the interlayer distribution characteristics in the vertical distribution sequence, the types of the assemblies are identified as thick source-thin reservoir assemblies and thick reservoir-thin source assemblies.

[0082] Based on the frequency of alternating appearance of source end-units and storage end-units and the thickness of a single layer in the vertical distribution sequence, the combination of alternating source end-units and storage end-units with a single layer thickness less than a preset interlayer thickness threshold is identified as a source-storage symbiotic combination.

[0083] The combination of the vertically distributed sequence that does not contain source endmembers and storage endmembers but only interlayer endmembers is identified as an interlayer type combination.

[0084] In one possible implementation, the filtering module is specifically used for:

[0085] The permeability, porosity, and oil saturation index values ​​of each lithofacies assemblage type were extracted from the physical and oil-bearing parameters.

[0086] The permeability values ​​of the multiple lithofacies assemblage types are sorted respectively, and a set of multiple first lithofacies assemblage types with the highest permeability values ​​is determined according to a preset first quantity threshold.

[0087] The porosity values ​​of the multiple lithofacies assemblage types are sorted respectively, and a set of multiple second lithofacies assemblage types with the highest porosity values ​​is determined according to a preset second quantity threshold.

[0088] The oil saturation index values ​​of the multiple lithofacies assemblages are sorted, and a set of multiple third lithofacies assemblages with the highest oil saturation index values ​​is determined according to a preset third quantity threshold.

[0089] A lithofacies assemblage type that appears in at least two of the first lithofacies assemblage type set, the second lithofacies assemblage type set, and the third lithofacies assemblage type set is identified as a candidate lithofacies assemblage type.

[0090] The lower-source upper-reservoir type assemblages and the thick-source thin-reservoir type assemblages among the candidate lithofacies assemblages are identified as target lithofacies assemblages.

[0091] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0092] The memory stores computer-executed instructions;

[0093] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0094] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0095] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0096] The method, apparatus, equipment, and storage medium for screening dominant shale facies assemblages provided in this application acquire multi-source geological data from shale core samples and determine thresholds for organic matter abundance, oil content, and yield index. This allows for the classification of facies types and source-reservoir functional units, delineation of vertical assemblage boundaries based on pressure inflection points, identification of facies assemblage types, and screening of target facies assemblages based on physical properties and oil content parameters. This achieves precise classification of dominant facies assemblages in strongly heterogeneous shale, improving the efficiency and accuracy of shale oil exploration target selection. Attached Figure Description

[0097] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0098] Figure 1 A schematic diagram illustrating an application scenario of the shale dominant lithofacies assemblage screening method provided in this application embodiment;

[0099] Figure 2 A schematic flowchart illustrating the method for screening dominant shale lithofacies assemblages provided in this application embodiment;

[0100] Figure 3 A three-terminal element diagram of shale mineral composition provided in the embodiments of this application;

[0101] Figure 4 This application provides a shale TOC-S1 intersection diagram for an embodiment of the present application.

[0102] Figure 5 A frequency and percentage chart of 11 types of lithofacies shale provided for embodiments of this application;

[0103] Figure 6 This is a diagram showing the TOC-PI endmember partitioning of shale rocks provided in an embodiment of this application.

[0104] Figure 7 This application provides a well logging diagram for delineating the boundaries of lithofacies assemblages.

[0105] Figure 8 This is a schematic diagram of the source-reservoir structure configuration of a lithofacies assemblage system provided in an embodiment of this application.

[0106] Figure 9 Comparison diagrams of basic characteristics of different types of shale lithofacies assemblages provided in the embodiments of this application;

[0107] Figure 10 This is a schematic diagram of the structure of the shale dominant lithofacies assemblage screening device provided in the embodiments of this application;

[0108] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0109] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0110] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0111] In existing technologies, a lithofacies classification system is established by collecting shale core samples and testing indicators such as total organic carbon content and mineral composition. The boundaries of lithofacies assemblages are delineated based on the macroscopic interface of sedimentary cycles. However, there are technical problems such as uncorrected oil-bearing parameters, reliance on empirical thresholds for source-reservoir end-member division, and lack of correlation between the assemblage boundaries and pressure systems. These issues result in low accuracy in identifying dominant lithofacies assemblages.

[0112] The method for screening dominant facies assemblages in shale provided in this application obtains petrological characteristic data, total organic carbon content data, pyrolysis analysis data, and well logging and pressure test data from target wells from shale core samples. Based on the pyrolysis analysis and total organic carbon content data, it determines thresholds for organic matter abundance, oil-bearing capacity, and yield index. Combining these with preset mineral content and sedimentary structure thickness thresholds, it classifies facies types. Based on the total organic carbon content data, yield index, and each classification threshold, it performs source-reservoir function analysis on the facies types to obtain source-reservoir function end-members. It delineates vertical facies assemblage boundaries based on well logging and pressure test data and identifies facies assemblage types based on the spatial configuration of source-reservoir function end-members. It obtains physical property parameters and oil-bearing parameters for each facies assemblage type and screens the target facies assemblages. This method solves the technical problems of distorted oil-bearing parameters, lack of objective basis for source-reservoir end-member classification, and disconnection between assemblage boundaries and pressure systems, achieving accurate classification of dominant facies assemblages in strongly heterogeneous shale.

[0113] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0114] Figure 1This is a schematic diagram illustrating an application scenario of the shale dominant lithofacies assemblage screening method provided in the embodiments of this application, such as... Figure 1 As shown, it includes: terminal 101 and server 102.

[0115] Terminal 101 is used to collect petrological characteristic data, total organic carbon content data, pyrolysis analysis data, and well logging and pressure test data from the target well from shale core samples. It uploads the collected data to the server, receives the lithofacies assemblage classification results and target dominant lithofacies assemblage information returned by the server, and displays them. Server 102 receives the various data uploaded by the terminal, executes the relevant calculation process for lithofacies assemblage classification, determines the organic matter abundance classification threshold, oil-bearing classification threshold, and yield index classification threshold, classifies multiple lithofacies types and source-reservoir end-members, delineates the vertical boundaries of lithofacies assemblages and identifies lithofacies assemblage types, selects the target dominant lithofacies assemblage, and feeds the relevant results back to the terminal.

[0116] Figure 2 This is a flowchart illustrating the method for screening dominant shale lithofacies assemblages provided in this application embodiment. The execution entity in this embodiment can be... Figure 1 The server 102 in the illustrated embodiment can also be other computer-related devices, and this embodiment is not particularly limited.

[0117] like Figure 2 As shown, the screening method for the dominant lithofacies assemblage of shale includes the following steps:

[0118] Step S201: Obtain petrological characteristic data, total organic carbon content data, pyrolysis analysis data, and well logging data and pressure test data of the target well from the shale core sample.

[0119] Specifically, shale core samples from the target strata in the study area were obtained through core sampling. Core description information, thin section identification, and whole-rock mineral analysis using X-ray diffraction were used to obtain petrological characteristic data. Total organic carbon (TOC) content data was obtained through total organic carbon (TOC) testing, and pyrolysis analysis data was obtained through rock pyrolysis analysis. Well logging data was obtained by conducting sonic transit time logging and density logging on the target well using logging equipment, and pressure testing data was obtained through reservoir pressure testing. By collecting all the basic data required for shale lithofacies classification, source-reservoir-end-unit determination, and assemblage boundary delineation, multi-source data integration was achieved, providing data support for subsequent quantitative analysis steps and ensuring that each subsequent analytical step has complete and systematic data support.

[0120] Step S202: Determine the threshold for organic matter abundance and the threshold for oil content based on pyrolysis analysis data and total organic carbon content data.

[0121] Specifically, the raw oil-bearing data is extracted from the pyrolysis analysis data, and the raw oil-bearing data is standardized and corrected to eliminate interference factors such as drilling fluid contamination, resulting in corrected oil-bearing data. Based on the corrected oil-bearing data and total organic carbon content data, a first cross plot is generated. The upper and lower inflection points in the first cross plot are identified. The total organic carbon content value corresponding to the upper inflection point determines the organic matter abundance threshold, and the corrected oil-bearing data value corresponding to the lower inflection point determines the oil-bearing threshold.

[0122] Total organic carbon content is an indicator of the enrichment of organic matter in shale. Oil-bearing data reflects the characteristics of movable oil in shale. By analyzing the inflection points of cross plots, the critical characteristics between geological parameters can be identified. The values ​​corresponding to the inflection points can effectively distinguish the essential differences in shale geological characteristics and become a reasonable threshold for quantitative classification.

[0123] By eliminating interference factors in the original data through data correction, the accuracy of the data is improved. By using cross-plot inflection point analysis, the thresholds for organic matter abundance and oil-bearing properties can be quantitatively determined, replacing the traditional empirical threshold definition method. This provides an objective standard for geochemical parameter thresholds for subsequent lithofacies classification and source-reservoir-end-unit determination. The corrected oil-bearing property data and quantitative organic matter abundance and oil-bearing property thresholds are obtained, solving the problems of parameter distortion and strong subjectivity in threshold definition in existing technologies. This improves the objectivity and accuracy of geochemical parameter thresholds and provides a standardized judgment basis for subsequent threshold-based quantitative analysis.

[0124] Step S203: Calculate the yield index based on the pyrolysis analysis data and determine the yield index classification threshold.

[0125] Specifically, the yield index refers to the ratio of oil-bearing parameters to hydrocarbon generation potential parameters in shale pyrolysis analysis, used to characterize the hydrocarbon generation efficiency of shale. The oil-bearing parameters and hydrocarbon generation potential parameters are extracted from the pyrolysis analysis data, and the yield index is calculated using their ratio. The hydrocarbon generation potential difference is determined based on the pyrolysis analysis data. A second cross plot is generated based on the yield index and the hydrocarbon generation potential difference. A trend line is obtained by fitting the distribution characteristics of the data points in the plot. The intersection of the trend line and the hydrocarbon generation potential difference axis is identified, and the yield index threshold corresponding to the intersection point is used to determine the yield index classification threshold.

[0126] Yield index can quantitatively reflect the efficiency of shale organic matter conversion into mobile oil, hydrocarbon generation potential difference can amplify the numerical change characteristics of hydrocarbon generation potential, and the fitting trend line of data points through parameter correlation analysis can reflect the overall change law between parameters. The intersection of the trend line and the coordinate axis represents the critical state of the parameters, which can effectively distinguish the characteristic differences in shale hydrocarbon generation efficiency.

[0127] The basic parameters of pyrolysis analysis are transformed into yield indices that characterize hydrocarbon generation efficiency. By analyzing the trend lines of multi-parameter cross plots, the threshold for dividing the yield index is quantitatively determined. This provides key hydrocarbon generation efficiency parameter thresholds for defining the reservoir end-unit in the subsequent source-reservoir functional unit, thus completing the quantitative characterization of shale hydrocarbon generation efficiency, obtaining objective and quantitative yield index division thresholds, and improving the quantitative level of subsequent source-reservoir functional analysis.

[0128] Step S204: Based on the organic matter abundance classification threshold, the preset mineral content classification threshold, the preset sedimentary structure thickness classification threshold, petrological characteristic data, and total organic carbon content data, multiple lithofacies types are obtained.

[0129] Specifically, a three-dimensional lithofacies classification system based on organic matter abundance, mineral composition, and sedimentary structures enables a refined division of shale lithofacies types, clarifies the basic geological attributes of each lithofacies, and establishes a matching basis between lithofacies types and source-reservoir functions for subsequent source-reservoir function analysis. This allows the determination of source-reservoir functions to accurately correspond to specific lithofacies types, resulting in multiple lithofacies types covering the multi-dimensional geological characteristics of shale. This breaks through the limitations of traditional single-dimensional lithofacies classification, achieves a refined and systematic division of lithofacies types, establishes a correspondence between lithofacies types and basic geological attributes, and provides a clear lithofacies classification basis for subsequent accurate source-reservoir function analysis.

[0130] Step S205: Based on the total organic carbon content data, yield index, organic matter abundance classification threshold, oil-bearing classification threshold, and yield index classification threshold, source-reservoir function analysis is performed on the lithofacies type to obtain multiple source-reservoir function end-members.

[0131] Specifically, source-reservoir functional end-units refer to shale geological units with specific hydrocarbon generation or storage functions, and are divided into source end-units, reservoir end-units, integrated source-reservoir end-units, and interlayer end-units. Through comprehensive discrimination using multiple thresholds, lithofacies types can be accurately matched with source-reservoir functions, realizing the transformation from lithofacies types to source-reservoir functional end-units, establishing a precise correspondence between lithofacies types and source-reservoir functions, transforming the geological characteristics of lithofacies into source-reservoir functional attributes, identifying source-reservoir functional end-units with different generation and storage functions, providing a functional unit basis for the subsequent identification of lithofacies assemblages, and enabling the identification of assemblages to be carried out based on the spatial configuration of source-reservoir functions.

[0132] By completing the transformation from lithofacies type to source-reservoir functional end-units, multiple source-reservoir functional end-units with clear source-reservoir functions are obtained, solving the problem of insufficient correlation between lithofacies classification and source-reservoir functions in existing technologies, realizing accurate matching between lithofacies geological characteristics and source-reservoir functions, and laying the functional unit foundation for subsequent identification of assemblages based on source-reservoir functions.

[0133] Step S206: Delineate the vertical boundaries of lithofacies assemblages based on well logging data and pressure test data, and identify multiple lithofacies assemblages within the assemblage boundaries based on the spatial configuration relationship of source and reservoir functional units.

[0134] Specifically, a lithofacies assemblage refers to a shale geological unit with an independent pressure system, formed by the vertical combination of multiple source-reservoir functional units. The spatial configuration of these source-reservoir functional units determines the type characteristics of the assemblage, and accurate identification of the assemblage type can be achieved through characteristic analysis of the vertical distribution sequence. Quantitative delineation of lithofacies assemblage boundaries is achieved using pressure inflection points, ensuring that the delineated assemblages possess the geological attributes of an independent pressure system. Refined identification of assemblage types is achieved based on the spatial configuration of source-reservoir functional units, clarifying the source-reservoir configuration characteristics of each assemblage. This solves the problem of qualitative delineation of assemblage boundaries in existing technologies, realizing quantitative delineation of assemblage boundaries and refined identification of assemblage types.

[0135] Step S207: Obtain the physical properties and oil-bearing parameters of the lithofacies assemblage type, and screen the target lithofacies assemblage based on the physical properties and oil-bearing parameters.

[0136] Specifically, by comprehensively comparing physical properties and oil-bearing parameters, the oil and gas enrichment potential of different lithofacies assemblages is quantitatively evaluated. Target lithofacies assemblages with optimal oil and gas enrichment potential are selected, and the analytical results of the entire selection method are applied to specific exploration targets, providing direct target basis for shale oil exploration deployment. By selecting the target lithofacies assemblages with the optimal oil and gas enrichment potential, the entire process analysis from lithofacies classification to source-reservoir-end-factor determination and then to the selection of dominant assemblages is completed. This achieves accurate identification of dominant shale lithofacies assemblages, providing clear and reliable target basis for shale oil exploration deployment and improving the accuracy of exploration target selection.

[0137] The shale dominant lithofacies assemblages screening method provided in this invention solves the problems of parameter distortion, subjective thresholds, boundary qualitative analysis, and disconnect between lithofacies and source-reservoir functions in existing technologies by integrating multi-source basic data, quantitatively determining multi-parameter thresholds, finely classifying lithofacies types, accurately defining source-reservoir functional end-units, quantitatively classifying and identifying lithofacies assemblages boundaries, and comprehensively screening for oil-bearing properties. This method achieves accurate identification of shale dominant lithofacies assemblages, providing reliable technical support for the exploration and development of highly heterogeneous shale oil in continental rift basins, and significantly improving the accuracy and efficiency of shale oil exploration target selection.

[0138] This embodiment provides a detailed description of the process in the above embodiments for determining the organic matter abundance threshold and oil content threshold based on pyrolysis analysis data and total organic carbon content data. The specific implementation of this process includes the following steps:

[0139] Step a1: Obtain the raw oil content data from the pyrolysis analysis data, and correct the raw oil content data based on the preset contamination correction model to obtain the corrected oil content data.

[0140] Specifically, raw oil-bearing data characterizing the movable oil content of shale is extracted from the pyrolysis analysis data of shale core samples. This data is then input into a preset contamination correction model. The model eliminates the deviations caused by external interference factors such as drilling fluid contamination, and outputs standardized oil-bearing data to form basic oil-bearing parameters that can be used for subsequent quantitative analysis.

[0141] Raw oil-bearing data is susceptible to numerical deviations caused by external factors such as drilling fluid contamination. A pre-set contamination correction model restores the true geological characteristics of the oil-bearing data through quantitative analysis and numerical correction of interfering factors. It establishes a correspondence between the interference-free oil-bearing data and the actual oil-bearing characteristics of shale, eliminates non-geological interference deviations in the raw oil-bearing data, and achieves standardized correction of the oil-bearing data. This allows the oil-bearing data to truly reflect the actual oil-bearing characteristics of shale, providing accurate and reliable quantitative parameters for subsequent correlation analysis with total organic carbon content data, and avoiding subsequent analysis deviations caused by distorted data.

[0142] By obtaining corrected oil-bearing data after removing interference factors, the problem of parameter distortion caused by directly using raw pyrolysis data in existing technologies is solved, thereby improving the accuracy and reliability of oil-bearing data and providing real geological data support for subsequent threshold determination based on this data.

[0143] Step a2: Generate the first cross plot based on the corrected oil content data and total organic carbon content data.

[0144] Specifically, total organic carbon content refers to the mass percentage of organic carbon elements in shale, used to characterize the enrichment degree of organic matter in shale; the first cross plot is a two-dimensional geological parameter cross plot constructed with total organic carbon content as the horizontal axis and corrected oil-bearing data as the vertical axis, used to analyze the correlation characteristics of the two parameters and define the quantitative threshold; a two-dimensional rectangular coordinate system is established with total organic carbon content as the horizontal axis and corrected oil-bearing data as the vertical axis, and the total organic carbon content values ​​and corrected oil-bearing data values ​​corresponding to multiple shale samples are projected into this coordinate system in the form of data points to form the first cross plot that can intuitively reflect the numerical correlation characteristics of the two parameters.

[0145] There is an inherent geological correlation between total organic carbon content and shale oil-bearing capacity. By transforming the numerical relationship between two quantitative geological parameters into the distribution of data points in a coordinate system, the geological correlation between the parameters is intuitively presented based on the clustering and variation characteristics of the data points. This transforms the definition of thresholds for organic matter abundance and oil-bearing capacity from a single numerical analysis to a visual analysis based on data distribution trends. A first cross-plot is generated that can intuitively reflect the correlation characteristics between total organic carbon content and corrected oil-bearing capacity data, clearly showing the distribution patterns of the two parameters for different shale samples. This provides an intuitive graphical basis for subsequent threshold definition, avoiding the blindness caused by relying solely on numerical comparisons, and improving the intuitiveness and scientific nature of threshold definition.

[0146] Step a3: Identify the upper inflection point in the first intersection graph and determine the organic matter abundance threshold based on the total organic carbon content value corresponding to the upper inflection point.

[0147] Specifically, the trend of data points in the first intersection plot with the increase of total organic carbon content is analyzed, and the characteristic points of upward abrupt change in the distribution trend of data points are identified, namely the upper turning point. The specific value of total organic carbon content corresponding to the upper turning point on the horizontal axis is extracted, and this value is determined as the threshold for organic matter abundance classification, which serves as a quantitative standard for defining the degree of organic matter enrichment in shale.

[0148] The upper inflection point in the first cross plot corresponds to the critical characteristic of shale organic matter abundance. When the total organic carbon content reaches this inflection point, the organic matter enrichment characteristics of the shale undergo a fundamental change. The value corresponding to the inflection point can effectively distinguish different levels of shale organic matter abundance, becoming a reasonable threshold for quantitatively classifying organic matter abundance. By identifying the upper inflection point in the first cross plot, the visualized graphic features are transformed into quantitative organic matter abundance classification thresholds, realizing the transformation from graphic analysis to numerical thresholds. This provides a clear quantitative standard for organic matter abundance determination for subsequent shale lithofacies classification and source-reservoir functional end-member determination, unifying the quantitative basis for organic matter abundance level classification, replacing the traditional experience-based organic matter abundance definition method, and allowing the determination of organic matter abundance thresholds to have an objective geological data distribution basis, improving the objectivity and quantification of threshold definition, and providing a standardized classification standard for various subsequent geological analyses based on organic matter abundance.

[0149] Step a4: Identify the lower inflection point in the first intersection graph, and determine the oil content classification threshold based on the corrected oil content data value corresponding to the lower inflection point.

[0150] Specifically, the distribution trend of data points in the first intersection plot with the change of total organic carbon content is analyzed, and the characteristic points of downward abrupt change in the distribution trend of data points are identified, namely the lower turning point. The specific value of the corrected oil-bearing data corresponding to the lower turning point on the vertical axis is extracted, and the value is determined as the oil-bearing classification threshold, which serves as a quantitative standard for defining the oil content of shale.

[0151] The downward inflection point in the first cross plot corresponds to the critical characteristic of shale oil-bearing properties. When the oil-bearing data reaches this inflection point, the movable oil-bearing characteristics of the shale undergo a fundamental change. The value corresponding to the inflection point can effectively distinguish different levels of shale oil-bearing degree, becoming a reasonable threshold for quantitatively classifying shale oil-bearing properties. By identifying the downward inflection point in the first cross plot, the visualized graphic features are transformed into quantitative oil-bearing property classification thresholds, realizing a secondary transformation from graphic analysis to numerical thresholds. This forms a matching quantitative parameter system with the organic matter abundance classification threshold, providing a clear quantitative standard for determining oil-bearing properties for subsequent shale lithofacies classification and source-reservoir functional end-member determination.

[0152] This invention achieves an objective and quantitative definition of organic matter abundance and oil content thresholds by standardizing and correcting the original oil content data, performing visual cross-plot analysis with total organic carbon content data, and quantitatively identifying the critical inflection points of the cross-plot. This replaces the traditional empirical threshold definition method, solves the core problems of parameter distortion and subjective threshold definition in the prior art, and improves the accuracy and objectivity of parameter threshold definition.

[0153] This embodiment provides a detailed explanation of the process in the above embodiment for calculating the yield index based on pyrolysis analysis data and determining the yield index threshold. The specific implementation of this process includes the following steps:

[0154] Step b1: Calculate the yield index based on the oil content parameters and hydrocarbon generation potential parameters in the pyrolysis analysis data.

[0155] Specifically, oil-bearing parameters and hydrocarbon generation potential parameters are extracted from the pyrolysis analysis data of shale core samples. The yield index refers to the ratio of oil-bearing parameters to hydrocarbon generation potential parameters in shale pyrolysis analysis, which is used to characterize the hydrocarbon generation efficiency of shale organic matter in converting into mobile oil. By calculating the ratio of oil-bearing parameters to hydrocarbon generation potential parameters, the yield index value corresponding to each shale sample is obtained, forming a standardized set of hydrocarbon generation efficiency characterization parameters.

[0156] In rock pyrolysis analysis, oil-bearing parameters reflect the content of mobile oil already generated in shale, while hydrocarbon generation potential parameters reflect the total hydrocarbon generation potential of shale organic matter. The ratio of these two parameters can quantify the efficiency of the conversion of organic matter into mobile oil after hydrocarbon generation, establishing a quantitative correlation between basic pyrolysis parameters and shale hydrocarbon generation efficiency. By fusing these two basic parameters in pyrolysis analysis and transforming them into yield indices that can individually characterize shale hydrocarbon generation efficiency, a quantitative characterization of hydrocarbon generation efficiency for each shale sample is achieved. This yield index parameter set, which accurately reflects the hydrocarbon generation and expulsion capacity of organic matter, overcomes the limitation that a single pyrolysis parameter cannot reflect hydrocarbon conversion efficiency.

[0157] Step b2: Determine the difference in hydrocarbon generation potential based on pyrolysis analysis data.

[0158] Specifically, multiple sets of hydrocarbon generation potential parameters are extracted from the pyrolysis analysis data of shale core samples. The hydrocarbon generation potential difference refers to the value obtained by difference calculation of multiple sets of hydrocarbon generation potential parameters in shale pyrolysis analysis, which is used to help characterize the changing characteristics of shale hydrocarbon generation potential. The extracted hydrocarbon generation potential parameters are subjected to difference calculation to obtain the hydrocarbon generation potential difference corresponding to each shale sample, forming a set of derived parameters that can reflect the changes in hydrocarbon generation potential values.

[0159] By processing the differences in the basic parameters of hydrocarbon generation potential, the numerical differences in hydrocarbon generation potential among different shale samples can be amplified, highlighting the changing characteristics of hydrocarbon generation potential. This allows the geological characteristics of hydrocarbon generation potential to form a suitable numerical analysis dimension with the yield index, achieving an effective correlation between the two. The basic parameters of hydrocarbon generation potential are transformed into hydrocarbon generation potential differences that can reflect their changing characteristics, serving as complementary analysis parameters to the yield index. This enriches the quantitative analysis dimensions of shale hydrocarbon generation-related characteristics, resulting in a set of hydrocarbon generation potential difference parameters that can reflect the changing characteristics of shale hydrocarbon generation potential. This addresses the deficiency of a single yield index in reflecting the basic changing characteristics of hydrocarbon generation potential, forming a complementary analytical parameter system with the yield index. This provides a complete data foundation for subsequent multi-parameter cross-plot analysis, improving the comprehensiveness of hydrocarbon generation-related characteristic analysis.

[0160] Step b3: Generate a second cross plot based on the difference between the yield index and the hydrocarbon generation potential.

[0161] Specifically, the second cross plot refers to a two-dimensional geological parameter cross plot constructed with the yield index as the vertical axis and the difference in hydrocarbon generation potential as the horizontal axis. It is used to analyze the correlation characteristics of the two parameters and define the critical value of the yield index. A two-dimensional rectangular coordinate system is established with the difference in hydrocarbon generation potential as the horizontal axis and the yield index as the vertical axis. The yield index value and the difference in hydrocarbon generation potential value corresponding to each shale sample are projected into this coordinate system in the form of data points to form a second cross plot that can intuitively reflect the correlation characteristics and distribution patterns of the two parameters.

[0162] By visually presenting the numerical correlation between the two parameters through the cluster distribution and trend changes of data points, the hidden hydrocarbon generation characteristics hidden in the data are visualized. The geological correlation characteristics and data distribution trends of the two parameters are intuitively displayed, transforming the definition of the yield index threshold from a simple numerical comparison to a visual analysis based on data distribution trends. A second cross plot is generated that clearly reflects the correlation characteristics between the yield index and the difference in hydrocarbon generation potential, intuitively presenting the distribution patterns of the two parameters of different shale samples. This provides a visual graphical basis for determining the yield index threshold, avoiding the definition bias caused by relying solely on numerical analysis, and improving the intuitiveness and scientific nature of the threshold definition process.

[0163] Step b4: Identify the intersection of the trend line and the hydrocarbon generation potential difference axis in the second cross plot, and determine the yield index division threshold based on the yield index value corresponding to the intersection point.

[0164] Specifically, a linear fit is performed on the distribution characteristics of all data points in the second intersection plot to obtain a trend line that reflects the overall pattern of the yield index change with the difference in hydrocarbon generation potential. The intersection points of the trend line and the hydrocarbon generation potential difference axis are identified, and the specific yield index values ​​corresponding to the intersection points on the vertical axis are extracted. These values ​​are then determined as the yield index classification threshold.

[0165] The fitting trend line of the data points in the second intersection plot can reflect the overall change pattern of the yield index and the difference between hydrocarbon generation potential. The intersection of the trend line and the hydrocarbon generation potential difference axis represents the critical state of the yield index when the difference between hydrocarbon generation potential is a specific value. The critical value can effectively distinguish the essential differences in the efficiency of shale hydrocarbon generation.

[0166] By identifying the intersections of trend lines and coordinate axes, the graphical features of the second cross plot are transformed into quantitative yield index thresholds. This achieves the transformation from visual graphical analysis to quantitative numerical thresholds, providing a clear quantitative standard for determining hydrocarbon generation efficiency for the subsequent definition of source-reservoir functional units. This replaces the traditional experience-based yield index threshold definition method, allowing the determination of thresholds to be based on objective geological data distribution and trend analysis, thus improving the objectivity and quantification of yield index threshold definition. This lays a crucial parameter foundation for the accurate determination of subsequent source-reservoir functional units.

[0167] This invention, through converting basic pyrolysis parameters into the difference between yield index and hydrocarbon generation potential, constructing a cross-plot of the two and identifying the critical intersection point of the trend line, achieves an objective and quantitative definition of the yield index threshold, replacing the traditional empirical threshold definition method. This solves the problem of strong subjectivity in the yield index threshold definition in the prior art, improves the accuracy and scientific nature of the threshold definition, and provides core quantitative parameters for hydrocarbon generation efficiency for the accurate determination of shale source-reservoir functional end-units.

[0168] This embodiment details the process of classifying multiple lithofacies types based on organic matter abundance threshold, preset mineral content threshold, preset sedimentary structure thickness threshold, petrological characteristic data, and total organic carbon content data, as described in the above embodiment. The specific implementation of this process includes the following steps:

[0169] Step c1: The shale core samples are divided according to the organic matter abundance classification threshold to obtain the organic matter abundance classification results, which include organic matter-rich shale and organic matter-poor shale.

[0170] Specifically, the organic matter abundance classification threshold refers to the critical value of total organic carbon content used to define the degree of organic matter enrichment in shale. The total organic carbon content data of each shale core sample is extracted, and the total organic carbon content data is compared with the organic matter abundance classification threshold. Samples with total organic carbon content data higher than the threshold are classified as organic matter-rich shale, and samples with total organic carbon content lower than the threshold are classified as organic matter-poor shale, thus forming the organic matter abundance classification result of the shale sample.

[0171] Step c2: Divide the shale core sample according to the preset mineral content classification threshold to obtain the mineral content classification results. The mineral content classification results include clayey shale, carbonate shale, felsic shale and mixed shale.

[0172] Specifically, the preset mineral content classification threshold refers to the critical value of the proportion of a single mineral used to define the mineral component type of shale. The mineral component proportion data in the petrological characteristic data of each shale core sample is extracted, and the proportion data of each type of mineral is compared with the preset mineral content classification threshold. Samples with a single mineral proportion higher than the threshold are classified as clayey, carbonate, and felsic shale, respectively, while samples with no single mineral proportion higher than the threshold are classified as mixed shale. This forms the mineral content classification result of the shale sample and clarifies the basic reservoir performance attributes of each shale sample.

[0173] Step c3: Divide the shale core sample according to the preset sedimentary structure thickness division threshold to obtain the structural division results, which include laminated shale, layered shale and massive shale.

[0174] Specifically, the preset sedimentary structure thickness classification threshold refers to the critical value of sedimentary structure thickness used to define the sedimentary structure type of shale. The sedimentary structure thickness data is extracted from the petrological characteristic data of each shale core sample, and this data is compared with the two preset sedimentary structure thickness classification thresholds. Sedimentary structures with a thickness below the first threshold are classified as laminated shale, those between the two thresholds are classified as layered shale, and those above the second threshold are classified as massive shale. This forms the structural classification result of the shale sample. By comparing the sedimentary structure thickness with the predetermined two thresholds, the accurate classification of shale sedimentary structure types can be achieved, clarifying the sedimentary environment and basic characteristics of pore and fracture development of shale, and allowing the lithofacies type to fully reflect the sedimentary characteristics of shale.

[0175] Step c4 involves cross-combining the results of organic matter abundance classification, mineral content classification, and structural classification to obtain multiple lithofacies types.

[0176] Specifically, lithofacies refers to shale geological units possessing specific organic matter abundance, mineral composition, and sedimentary structural characteristics. Based on the organic matter abundance, mineral content, and structural classification results corresponding to each shale core sample, these three classification results are systematically cross-combined to fuse the type characteristics of different dimensions, resulting in multiple lithofacies types covering multi-dimensional geological features. By fusing the single classification results of organic matter abundance, mineral composition, and sedimentary structure, the transformation from single-dimensional classification to multi-dimensional lithofacies type classification is achieved, clarifying the comprehensive geological attributes of each lithofacies type and laying a foundation for accurate matching between lithofacies type and geological characteristics in the subsequent determination of source-reservoir functional units.

[0177] This invention describes a shale facies classification system that classifies shale samples step-by-step from three geological dimensions: organic matter abundance, mineral composition, and sedimentary structure. The system then systematically combines the results of each dimension to construct a shale lithofacies classification system. This system replaces the traditional single-dimensional lithofacies classification method and solves the problem that existing lithofacies classifications cannot fully reflect the comprehensive geological characteristics of shale. It achieves a refined and systematic classification of shale lithofacies types, allowing for precise matching of lithofacies types with the core geological attributes of shale, such as hydrocarbon generation and reservoir functions. This lays a solid foundation for subsequent determination of source-reservoir functional units and identification of lithofacies assemblages.

[0178] This embodiment details the process described in the above embodiment of performing source-reservoir function analysis on lithofacies types based on total organic carbon content data, yield index, organic matter abundance threshold, oil-bearing threshold, and yield index threshold to obtain multiple source-reservoir function end-members. The specific implementation of this process includes the following steps:

[0179] Step d1: Generate a third cross plot based on the total organic carbon content data and yield index, and project the sample points corresponding to each lithofacies type onto the third cross plot.

[0180] Specifically, the third cross plot refers to a two-dimensional geological parameter cross plot constructed with total organic carbon content as the horizontal axis and yield index as the vertical axis. It is used to analyze the correlation characteristics of the two parameters and to carry out source-reservoir functional end-member identification. A two-dimensional rectangular coordinate system is established with total organic carbon content as the horizontal axis and yield index as the vertical axis. The total organic carbon content data and yield index values ​​of each shale core sample are extracted, and the data are classified according to the lithofacies type. All sample points corresponding to each lithofacies type are projected one by one onto the coordinate system to form a third cross plot with the distribution characteristics of sample points of lithofacies type.

[0181] By converting the numerical correlation between total organic carbon content and yield index into a visual graph, the parameter distribution of sample points of various lithofacies types is visualized. This transforms the identification of source-reservoir functional end-members from a single numerical comparison to a graphical analysis based on the cluster distribution of sample points. This provides an intuitive analytical foundation for subsequent end-member type identification using multiple thresholds, avoids the end-member identification bias caused by relying solely on numerical analysis, and provides graphical data support for accurate identification of end-member types.

[0182] Step d2: Based on the total organic carbon content value corresponding to the organic matter abundance threshold, the oil content value corresponding to the oil content threshold, and the yield index value corresponding to the yield index threshold, the endmember type of each projected sample point is determined to obtain the endmember type corresponding to each sample point.

[0183] Specifically, the critical values ​​of total organic carbon content corresponding to the organic matter abundance threshold, the corrected critical value of oil content corresponding to the oil content threshold, and the critical value of yield index corresponding to the yield index threshold are extracted. These three critical values ​​are used as the criteria for end-member type discrimination. For each projected sample point in the third cross plot, combined with its corresponding oil content data, a comprehensive judgment is made from the dimensions of hydrocarbon generation capacity and storage capacity to distinguish four types of end-members: source end-member, storage end-member, source-storage integrated end-member, and interlayer end-member. The end-member type result corresponding to each sample point is recorded.

[0184] By transforming multi-dimensional quantitative thresholds into criteria for identifying end-member types, the source-reservoir functional attributes of individual samples can be accurately determined. This enables the determination of source-reservoir functional end-members to accurately correspond to specific lithofacies types, establishes the correspondence between sample geochemical parameters and source-reservoir functional end-members, and solves the problem of the disconnect between lithofacies types and source-reservoir functional attributes in existing technologies.

[0185] Step d3: Based on the end-member types of the sample points included in each lithofacies type, determine the source-reservoir functional end-members corresponding to each lithofacies type.

[0186] Specifically, source-reservoir functional end-members refer to shale geological units with specific hydrocarbon generation, single or combined reservoir functions, or no generation-reservoir functions. They are divided into source end-members, reservoir end-members, integrated source-reservoir end-members, and interlayer end-members. The end-member type results of all sample points included in each lithofacies type are summarized. Based on the dominant distribution characteristics of the end-member types of the sample points, the source-reservoir functional end-member types corresponding to each lithofacies type are determined. The end-member determination results of all lithofacies types are integrated to obtain multiple source-reservoir functional end-members with clear lithofacies type correspondences.

[0187] By aggregating the end-member type results of single sample points to their respective lithofacies types, source-reservoir function determination is achieved from the sample point scale to the lithofacies type scale. This establishes a precise matching relationship between lithofacies types and source-reservoir function end-members, clarifies the lithofacies type corresponding to each source-reservoir function end-member, and provides a functional unit basis for subsequent lithofacies assemblages identification based on source-reservoir function end-members.

[0188] In an optional implementation, the method further includes: analyzing the distribution frequency of various end-member types in the sample points included in each lithofacies type; for each lithofacies type, calculating the frequency or proportion of source end-members, reservoir end-members, source-reservoir integrated end-members, and interlayer end-members in its sample points; and determining the end-member type with the highest frequency or proportion exceeding a preset ratio as the source-reservoir functional end-member corresponding to that lithofacies type.

[0189] By establishing a correspondence between lithofacies types and source-reservoir functional end-members, multiple source-reservoir functional end-members with clear functional attributes are obtained. Due to similar diagenetic environments and material compositions, lithofacies types typically possess relatively stable source-reservoir functional characteristics. By statistically analyzing the end-member types of numerous sample points, the representative functions of that lithofacies type can be summarized, achieving precise matching between lithofacies types and source-reservoir functions. Establishing a correspondence between lithofacies types and source-reservoir functions clarifies the specific roles of various lithofacies in hydrocarbon generation and reservoir concentration, providing functional units for subsequent assemblage identification. Obtaining the source-reservoir functional end-members corresponding to each lithofacies type completes the mapping from lithofacies classification to functional classification, giving lithofacies types clear geological significance and engineering applications.

[0190] This invention, through the construction of a cross-plot of total organic carbon content and yield index and projection onto lithofacies sample points, combined with multi-dimensional thresholds to determine the end-member type of a single sample, and based on the end-member aggregation results of lithofacies type, determines the corresponding source-reservoir functional end-members. This achieves precise matching between lithofacies type and source-reservoir functional end-members, replacing the traditional source-reservoir analysis method without a clear correspondence. It solves the problem of insufficient correlation between lithofacies classification and source-reservoir function in existing technologies, providing accurate functional unit basis for subsequent identification and advantage screening of lithofacies assemblages, and improving the quantification and accuracy of source-reservoir function analysis in shale oil exploration.

[0191] This embodiment details the process described in the above embodiment of delineating the vertical boundaries of lithofacies assemblages based on well logging data and pressure test data, and identifying multiple lithofacies assemblage types within the assemblage boundaries based on the spatial configuration relationship of source and reservoir functional units. The specific implementation of this process includes the following steps:

[0192] Step m1: Identify the sedimentary cycle interface based on the sonic transit time logging curve and density logging curve in the well logging data.

[0193] Specifically, sedimentary cycle interfaces refer to geological interfaces that reflect the phased changes in the vertical sedimentary environment of shale. By extracting sonic transit time and density logging curves from well logging data, analyzing the numerical variation characteristics and morphological fluctuation patterns of the two curves, identifying abrupt change points and regular cycle change nodes in the curves, and determining the nodes that can characterize significant changes in the sedimentary environment as sedimentary cycle interfaces, a vertical sedimentary cycle interface distribution sequence is formed.

[0194] Sonic transit time and rock density are core logging parameters reflecting the lithology and sedimentary characteristics of shale. Changes in the sedimentary environment lead to regular alterations in shale lithology, resulting in abrupt changes in the values ​​and morphologies of two types of logging curves. Analysis of curve characteristics can accurately identify the boundaries of changes in the sedimentary environment, i.e., sedimentary cycle interfaces. Identifying the vertical geological interfaces of shale from the perspective of sedimentary environment evolution provides a fundamental boundary basis for delineating the boundaries of lithofacies assemblages, ensuring that the division of the assemblages aligns with the sedimentary evolution patterns of shale and avoids a disconnect between boundary delineation and sedimentary geological characteristics.

[0195] Step m2: Identify pressure change characteristic points based on pressure test data, and determine the pressure inflection point based on the pressure value corresponding to the pressure change characteristic point.

[0196] Specifically, the pressure inflection point refers to the critical point that characterizes the essential change in the shale reservoir pressure system. The process involves extracting reservoir pressure test data from the target well, analyzing the numerical evolution of pressure data with drilling depth, identifying pressure change feature points with significant increases or decreases in the pressure data, extracting the pressure and depth values ​​corresponding to each feature point, and determining the feature points that can define an independent reservoir pressure system as pressure inflection points.

[0197] Lithofacies assemblages are geological units with independent reservoir pressure systems. Abrupt changes in reservoir pressure are the core boundary characteristics of independent pressure systems. By analyzing the variation characteristics of pressure test data, the pressure inflection points of independent pressure systems can be accurately identified and defined. From the perspective of reservoir pressure systems, critical geological points in the vertical direction of shale can be identified, providing core pressure system boundary basis for the delineation of lithofacies assemblage boundaries. This ensures that the division of assemblages conforms to the reservoir pressure distribution patterns of shale, and that the divided assemblages possess independent pressure system attributes.

[0198] Step m3, using the pressure inflection point as the main boundary, and combining the sedimentary cycle interface, delineates the vertical boundary of the lithofacies assemblage.

[0199] Specifically, a lithofacies assemblage refers to a shale geological unit formed by the vertical combination of multiple source-reservoir functional end-units, possessing an independent reservoir pressure system and conforming to the sedimentary evolution law. The pressure inflection point is used as the core basis for delineating the boundary of the lithofacies assemblage. Combined with the distribution characteristics of sedimentary cycle interfaces, the pressure inflection points and sedimentary cycle interfaces in the vertical direction are comprehensively matched and selected. Pressure inflection points and sedimentary cycle interfaces with similar depth positions are merged to finally delineate the boundary of the vertically continuously distributed lithofacies assemblage.

[0200] By integrating the dual geological characteristics of reservoir pressure and sedimentary environment, the boundaries of lithofacies assemblages are delineated both quantitatively and qualitatively. This ensures that the delineated assemblages possess independent reservoir pressure systems while conforming to the sedimentary evolution patterns of shale. It also clarifies the vertical depth range of each independent lithofacies assemblage, overcoming the limitations of traditional methods that rely solely on macroscopic sedimentary interfaces to define boundaries. This ensures that the delineated assemblages are effective reservoir-controlling units with independent pressure systems, thereby improving the accuracy and geological rationality of assemblage boundary delineation.

[0201] Step m4: Within the boundary of the composite body, obtain the source and storage function terminal types corresponding to each depth point, and generate a vertical distribution sequence of source and storage function terminal types.

[0202] Specifically, based on the defined boundaries of lithofacies assemblages, the vertical depth range of a single lithofacies assemblage is determined, and the shale geological data corresponding to each depth point within this depth range is extracted. Combined with the established correspondence between lithofacies types and source-reservoir end-members, the source-reservoir end-member types corresponding to each depth point are matched to obtain the end-member types corresponding to each depth point. The end-member types of each depth point are arranged in order from shallow to deep to generate a vertical distribution sequence of source-reservoir end-member types.

[0203] The source-reservoir functional end-member types in shale exhibit a regular distribution with vertical depth. The geological characteristics at each depth point determine the corresponding source-reservoir functional end-member type. By matching geological data at depth points, the vertical distribution characteristics of end-member types can be obtained. By accurately correlating source-reservoir functional end-member types with vertical depth, the spatial distribution of source-reservoir functional end-members within the shale complex can be visualized, allowing the source-reservoir configuration characteristics within the complex to form a clear sequential distribution. This provides an intuitive analytical basis for subsequent identification of complex types based on source-reservoir spatial configuration.

[0204] Step m5: Based on the vertical stacking order of the source endmember and the storage endmember in the vertical distribution sequence, the type of the combination is identified to obtain the lower source upper storage type combination and the upper source lower storage type combination.

[0205] Specifically, the spatial superposition relationship between source end-units and reservoir end-units in the vertical distribution sequence of source-reservoir functional end-unit types is analyzed to identify their vertical distribution order. Lithofacies assemblages in which source end-units are concentrated in the lower part of the assemblages and reservoir end-units are concentrated in the upper part of the assemblages are identified as lower source-upper reservoir type assemblages; and lithofacies assemblages in which source end-units are concentrated in the upper part of the assemblages and reservoir end-units are concentrated in the lower part of the assemblages are identified as upper source-lower reservoir type assemblages.

[0206] Identifying lithofacies assemblages based on the vertical stacking sequence of source and reservoir allows for the identification of two types of assemblages, enabling precise reflection of the basic spatial configuration characteristics of source and reservoir. This facilitates the accurate identification of lower-source-upper-reservoir and upper-source-lower-reservoir assemblages, clarifying the vertical stacking characteristics of the two types of assemblages. This provides a typological basis based on the source-reservoir stacking sequence for the subsequent selection of advantageous assemblages, ensuring that the classification of assemblages accurately corresponds to the basic spatial configuration rules of source and reservoir.

[0207] Step m6: Based on the thickness ratio of source endmembers to reservoir endmembers and the interlayer distribution characteristics in the vertical distribution sequence, the types of the composite are identified to obtain thick source-thin reservoir composite and thick reservoir-thin source composite.

[0208] Specifically, the vertical development thickness of source and reservoir end-members in the vertical distribution sequence of source-reservoir functional end-member types is analyzed, and their thickness ratio is calculated. Simultaneously, the distribution characteristics of interlayers within the source and reservoir end-members are analyzed. Lithofacies assemblages with a significantly higher proportion of source-end-member thickness, where reservoir end-members are dispersed in thin layers within the source end-members and interlayers are not well-developed, are identified as thick-source-interlayer-thin-reservoir assemblages. Conversely, lithofacies assemblages with a significantly higher proportion of reservoir-end-member thickness, where source end-members are dispersed in thin layers within the reservoir end-members and interlayers are not well-developed, are identified as thick-reservoir-interlayer-thin-source assemblages. The proportion threshold can be set according to actual conditions (e.g., >50%).

[0209] The thickness ratio of source endmembers to reservoir endmembers and the interlayer distribution characteristics reflect the development scale and symbiotic characteristics of source and reservoir within the assemblages. They serve as the basis for distinguishing between thick source-thin reservoir and thick reservoir-thin source assemblages. Through thickness ratio and interlayer analysis, the two types of assemblages can be accurately identified, and the development scale and internal symbiotic characteristics of source and reservoir within the two types of assemblages can be clearly defined.

[0210] Step m7: Based on the frequency of alternating source end-members and storage end-members in the vertical distribution sequence and the thickness of a single layer, the combination of alternating source end-members and storage end-members with a single layer thickness less than a preset interlayer thickness threshold is identified as a source-storage symbiotic combination.

[0211] Specifically, the frequency of occurrence and single-layer development thickness of source end-members and reservoir end-members in the vertical distribution sequence of source-reservoir functional end-member types are analyzed. The number of times the two alternate vertically is summarized, the development thickness of each single-layer end-member is extracted and compared with the preset interlayer thickness threshold. Lithofacies assemblages in which source end-members and reservoir end-members frequently alternate vertically and whose single-layer development thickness is less than the preset interlayer thickness threshold are identified as source-reservoir symbiotic assemblages.

[0212] Step m8 identifies assemblies in the vertical distribution sequence that do not contain source endmembers and storage endmembers but only contain interlayer endmembers as interlayer-type assemblies.

[0213] Specifically, the distribution characteristics of various end-members in the vertical distribution sequence of source-reservoir end-member types are analyzed to confirm the end-member type composition within the sequence. Lithofacies assemblages that lack source and reservoir end-members in the vertical distribution sequence and only have interlayer end-members developed throughout the entire depth range are identified as interlayer-type assemblages. Interlayer end-members have no hydrocarbon generation or reservoir function; they only serve as geological separators within the lithofacies assemblages. When only interlayer end-members are developed within an assemblage, it lacks the basic conditions for hydrocarbon enrichment. Accurate identification of interlayer-type assemblages can be achieved through end-member type composition verification.

[0214] This invention, through combining well logging curves and pressure test data, delineates the boundaries of lithofacies assemblages. Then, based on the vertical distribution sequence of source and reservoir end-members within the assemblages, it identifies assemblage types from multiple dimensions, including stacking order, thickness ratio, co-occurrence characteristics, and end-member composition. This constructs a systematic lithofacies assemblage boundary delineation and type identification system, replacing the traditional method of delineating boundaries solely based on sedimentary interfaces and identifying types from a single dimension. It solves the problems of qualitative boundary delineation and incomplete type identification in existing technologies, achieving precise boundary delineation and systematic type identification of lithofacies assemblages. This allows the delineated assemblages to possess dual geological attributes of an independent pressure system and sedimentary evolution patterns, providing accurate assemblage type criteria for subsequent screening of dominant shale lithofacies assemblages and significantly improving the accuracy and systematic nature of lithofacies assemblage analysis in shale oil exploration.

[0215] This embodiment details the process of obtaining the physical properties and oil-bearing parameters of the lithofacies assemblage type in the above embodiments, and selecting the target lithofacies assemblage based on the physical properties and oil-bearing parameters. The specific implementation of this process includes the following steps:

[0216] Step n1: Obtain the physical properties and oil-bearing parameters of each lithofacies assemblage type. The physical properties include porosity and permeability, while the oil-bearing parameters include total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index.

[0217] Specifically, porosity refers to the proportion of pore volume to the total volume of shale rock, characterizing the degree of development of shale reservoir space; permeability refers to the ability of shale to allow fluids to pass through, characterizing the efficiency of fluid transport within shale; and oil saturation index is a quantitative indicator characterizing the degree of mobile oil occurrence in shale. For each identified lithofacies assemblages, measured porosity and permeability data are extracted from the corresponding geological units. At the same time, geochemical test data on total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index are retrieved. The above parameters are classified and collected according to lithofacies assemblages to form parameter datasets corresponding to each type.

[0218] Step n2: Calculate the total average values ​​of parameters such as porosity, permeability, total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index for all types of lithofacies assemblages.

[0219] Specifically, for the six parameters of porosity, permeability, total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index, the mean values ​​of the parameters corresponding to each lithofacies assemblage type are extracted. The arithmetic mean of the mean values ​​of each parameter is then calculated based on the lithofacies assemblage type to obtain the total mean value of each of the six parameters, forming a reference total mean value for the six parameters.

[0220] The overall mean of parameters can reflect the average level of all lithofacies assemblage types in a certain parameter dimension. By calculating the overall mean, a unified reference benchmark for the evaluation of parameters of each lithofacies assemblage type can be established, realizing the quantitative comparison between the parameters of each type and the average level of the group, avoiding the evaluation bias caused by the comparison of parameter values ​​without a benchmark, and providing a quantitative reference for the subsequent judgment of the relative superiority or inferiority of parameters of each type.

[0221] Step n3: Calculate the difference between the mean value of the parameters and the total mean value of the corresponding parameters for each type of lithofacies assemblage under each parameter.

[0222] Specifically, for each lithofacies assemblage type, the mean values ​​of six parameters—porosity, permeability, total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index—are extracted sequentially. The difference values ​​are then calculated with the total mean values ​​of the corresponding parameters, retaining the positive or negative attributes and numerical magnitude of the differences. This yields the parameter difference values ​​for each lithofacies assemblage type under the six parameters, forming parameter difference datasets for each type.

[0223] The difference between the parameter mean and the overall mean can quantitatively characterize the superiority or inferiority of a single lithofacies assemblage type relative to the average level of the population in a certain parameter dimension. A positive difference indicates that the type is better than the average level in that parameter dimension, while a negative difference indicates that it is worse than the average level. The relative quantification of parameter characteristics of each type is achieved through difference calculation. By converting the absolute parameter values ​​of each lithofacies assemblage type into quantitative differences relative to the average level of the population, the superiority or inferiority of each type in a single parameter dimension can be accurately determined. This allows the characteristic differences in different parameter dimensions to have a unified quantitative expression form, providing an assemblable quantitative indicator for subsequent comprehensive evaluation.

[0224] Step n4: The differences between the lithofacies assemblage types under each parameter are summed to obtain the comprehensive reference value corresponding to the lithofacies assemblage type.

[0225] Specifically, for a single lithofacies assemblage type, the parameter differences under six parameters—porosity, permeability, total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index—are retrieved. Arithmetic summation is then performed on these six differences. During the summation process, weighting coefficients are assigned to porosity, permeability, total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index based on the control effect of each parameter on shale oil enrichment. The sum of these weighting coefficients is 1. The positive / negative attributes and numerical values ​​of the calculation results are retained, and the results are determined as the comprehensive reference value corresponding to that lithofacies assemblage type. This process completes the calculation of comprehensive reference values ​​for all lithofacies assemblage types.

[0226] The comprehensive reference value, through the superposition of differences among multiple parameters, achieves a comprehensive quantitative characterization of the reservoir, migration, hydrocarbon generation, and oil-bearing characteristics of lithofacies assemblage types. Following the superposition principle of multi-index comprehensive evaluation, it transforms the relative advantages and disadvantages of a single dimension into a comprehensive quantitative evaluation result, comprehensively reflecting the overall enrichment potential of the assemblage. This realizes the transformation from single-parameter evaluation to multi-parameter comprehensive evaluation, giving each type of overall enrichment potential a unique quantitative comparison index, avoiding the one-sidedness of single-parameter evaluation, and improving the comprehensiveness and accuracy of lithofacies assemblage type evaluation.

[0227] Step n5: Sort the lithofacies assemblage types based on the comprehensive reference value, and determine the lithofacies assemblage types with the comprehensive reference value ranking first or second in the sequence as the target lithofacies assemblage.

[0228] Specifically, comprehensive reference values ​​for all lithofacies assemblage types are collected, and each lithofacies assemblage type is sorted in descending order of comprehensive reference value to form a comprehensive evaluation sequence of lithofacies assemblage types. The lithofacies assemblage types with the top two comprehensive reference values ​​in the sequence are extracted and identified as target lithofacies assemblages with the best shale oil enrichment potential.

[0229] The magnitude of the comprehensive reference value is positively correlated with the overall shale oil enrichment potential of the lithofacies assemblage type. The larger the value, the better the comprehensive characteristics of the type in multiple dimensions such as reservoir, migration, hydrocarbon generation, and oil content. By sorting in descending order, the enrichment potential of each type can be ranked, the target lithofacies assemblage can be accurately identified, and the lithofacies assemblage type screening can be realized. This provides a clear target geological unit for shale oil exploration deployment and improves the accuracy and targeting of exploration target selection.

[0230] This invention constructs a multi-parameter, comprehensive quantitative screening system for shale dominant lithofacies assemblages by collecting core parameters of physical properties and oil-bearing potential of lithofacies assemblages, establishing a reference benchmark for the overall average value of parameters, quantifying the relative differences of parameters, superimposing comprehensive reference values, and sorting and screening them. This system replaces the traditional single-parameter or qualitative evaluation and screening methods, solving the problems of lack of quantitative basis and one-sided evaluation in the selection of exploration targets in existing technologies. It achieves a comprehensive quantitative evaluation of the enrichment potential of lithofacies assemblages and accurate screening of dominant types, providing clear and reliable target basis for the exploration deployment of strongly heterogeneous shale oil in continental rift basins, and improving the scientificity and accuracy of shale oil exploration target selection.

[0231] In one specific embodiment, a concrete example is provided for illustration:

[0232] like Figures 3 to 9 As shown, Figure 3 A three-terminal element diagram of shale mineral composition provided in the embodiments of this application; Figure 4 This application provides a shale TOC-S1 intersection diagram for an embodiment of the present application. Figure 5 A frequency and percentage chart of 11 types of lithofacies shale provided for embodiments of this application; Figure 6 This is a diagram showing the TOC-PI endmember partitioning of shale rocks provided in an embodiment of this application. Figure 7 This application provides a well logging diagram for delineating the boundaries of lithofacies assemblages. Figure 8 This is a schematic diagram of the source-reservoir structure configuration of a lithofacies assemblage system provided in an embodiment of this application. Figure 9 Comparison diagram of basic characteristics of different types of shale lithofacies assemblages provided in the embodiments of this application.

[0233] 119 shale core samples were collected from the target area for core observation, thin section identification, whole-rock XRD (X-ray Diffraction) analysis, TOC (Total Organic Carbon) testing, sedimentary structure observation, and rock pyrolysis analysis (N=119). The organic matter abundance boundary was determined based on the upper inflection point of the cross plot: TOC=1%; the oil-bearing boundary was determined based on the lower inflection point of the cross plot: oil-bearing correction value S1 > 2 mg / g (…). Figure 4Based on the total organic carbon (TOC) content data of 1%, the preset mineral content threshold of 50%, and the preset sedimentary structure thickness thresholds of 1 mm and 10 mm, the samples were divided into 11 lithofacies, of which organic-rich lithofacies accounted for 42% and organic-poor lithofacies accounted for 58%. Figure 3 , Figure 5 );

[0234] Rock pyrolysis data analysis was performed on 11 types of lithofacies samples to obtain parameters such as oil-bearing parameter S1, pyrolysis hydrocarbon content S2, and production index PI. A cross-plot of S1 / (S1+S2)-ΔQ was plotted. S2 is one of the core parameters in rock pyrolysis analysis, and ΔQ represents the difference in hydrocarbon generation potential. The production index PI was determined based on the characteristics of the cross-plot, thereby defining the range of high-yield shale. A TOC-PI cross-plot was plotted to classify end-members into four types: source end-members, reservoir end-members, integrated source-reservoir end-members, and interlayer end-members. Figure 4 , Figure 6 ).

[0235] Select key wells and, in conjunction with sedimentary cycle characteristics and pressure inflection point data, delineate the boundaries of lithofacies assemblages. Figure 7 A total of 6 types of lithofacies assemblages were identified. Figure 8 By comparing the physical properties and oil-bearing parameters of various assemblages, and through calculation and comparison, lower-source upper-reservoir assemblages and thick-source thin-reservoir assemblages were selected as the target lithofacies assemblages. Figure 9 ); Figure 9 In this context, S1 represents the oil content parameter, PG represents the hydrocarbon generation potential parameter, and PG = S1 + S2; OSI represents the oil saturation index.

[0236] Figure 10 This is a schematic diagram of the shale dominant lithofacies assemblage screening device provided in an embodiment of this application. Figure 10 As shown, the shale dominant lithofacies assemblage screening device 100 includes:

[0237] The acquisition module 1001 is used to acquire petrological characteristic data, total organic carbon content data, pyrolysis analysis data, and well logging data and pressure test data of the target well from shale core samples;

[0238] Analysis module 1002 is used to determine the organic matter abundance threshold and oil content threshold based on pyrolysis analysis data and total organic carbon content data;

[0239] The analysis module 1002 is also used to calculate the yield index based on the pyrolysis analysis data and determine the yield index classification threshold.

[0240] The type differentiation module 1003 is used to classify multiple lithofacies types based on organic matter abundance classification threshold, preset mineral content classification threshold, preset sedimentary structure thickness classification threshold, petrological characteristic data, and total organic carbon content data.

[0241] The source-reservoir module 1004 is used to perform source-reservoir function analysis on lithofacies types based on total organic carbon content data, yield index, organic matter abundance classification threshold, oil-bearing classification threshold and yield index classification threshold, and obtain multiple source-reservoir function end-members.

[0242] The identification module 1005 is used to delineate the vertical boundaries of lithofacies assemblages based on well logging data and pressure test data, and to identify multiple lithofacies assemblages types within the boundaries of the assemblages based on the spatial configuration relationship of source and reservoir functional units.

[0243] The screening module 1006 is used to obtain the physical property parameters and oil-bearing parameters of the lithofacies assemblage type, and to screen the target lithofacies assemblage based on the physical property parameters and oil-bearing parameters.

[0244] In one possible implementation, the analysis module 1002 is specifically used for:

[0245] Obtain the raw oil content data from the pyrolysis analysis data, and correct the raw oil content data based on the preset contamination correction model to obtain the corrected oil content data;

[0246] The first cross plot was generated based on the corrected oil content data and total organic carbon content data;

[0247] Identify the upper inflection point in the first intersection graph and determine the organic matter abundance threshold based on the total organic carbon content value corresponding to the upper inflection point;

[0248] Identify the lower inflection point in the first intersection graph and determine the oil content classification threshold based on the corrected oil content data value corresponding to the lower inflection point.

[0249] In one possible implementation, the analysis module 1002 is further configured to:

[0250] The yield index is calculated based on the oil content parameters and hydrocarbon generation potential parameters in the pyrolysis analysis data.

[0251] The difference in hydrocarbon generation potential is determined based on pyrolysis analysis data;

[0252] A second cross plot is generated based on the difference between the yield index and the hydrocarbon generation potential.

[0253] Identify the intersection points of the trend line and the hydrocarbon generation potential difference axis in the second cross plot, and determine the yield index division threshold based on the yield index value corresponding to the intersection point.

[0254] In one possible implementation, the type differentiation module 1003 is specifically used for:

[0255] Shale core samples were classified according to organic matter abundance thresholds to obtain organic matter abundance classification results, which included organic matter-rich shale and organic matter-poor shale.

[0256] The shale core samples were classified according to the preset mineral content classification thresholds to obtain the mineral content classification results, which include clayey shale, carbonate shale, felsic shale and mixed shale.

[0257] The shale core samples were divided according to the preset sedimentary structure thickness threshold to obtain the structural division results, which include laminated shale, layered shale and massive shale.

[0258] By cross-combining the results of organic matter abundance classification, mineral content classification, and structural classification, multiple lithofacies types are obtained.

[0259] In one possible implementation, the source storage module 1004 is specifically used for:

[0260] A third cross plot was generated based on total organic carbon content data and yield index, and sample points corresponding to each lithofacies type were projected onto the third cross plot.

[0261] Based on the total organic carbon content value corresponding to the organic matter abundance threshold, the oil content value corresponding to the oil content threshold, and the yield index value corresponding to the yield index threshold, the endmember type of each projected sample point is determined to obtain the endmember type of each sample point.

[0262] Based on the end-member types of the sample points included in each lithofacies type, the source-reservoir functional end-members corresponding to each lithofacies type are determined.

[0263] In one possible implementation, the identification module 1005 is specifically used for:

[0264] Identify sedimentary cycle interfaces based on sonic transit time logging curves and density logging curves in well logging data;

[0265] Identify pressure change characteristic points based on pressure test data, and determine pressure inflection points based on the pressure values ​​corresponding to these characteristic points.

[0266] The vertical boundaries of lithofacies assemblages are defined by taking the pressure inflection point as the main boundary and combining it with the sedimentary cycle interface.

[0267] Within the boundary of the composite body, obtain the source and storage function terminal type corresponding to each depth point, and generate a vertical distribution sequence of the source and storage function terminal type;

[0268] Based on the vertical stacking order of the source endmember and the storage endmember in the vertical distribution sequence, the types of the assemblies are identified as lower source upper storage type assemblies and upper source lower storage type assemblies.

[0269] Based on the thickness ratio of source endmembers to reservoir endmembers and the interlayer distribution characteristics in the vertical distribution sequence, the types of the assemblies are identified as thick source-thin reservoir assemblies and thick reservoir-thin source assemblies.

[0270] Based on the frequency of alternating source end-units and storage end-units in the vertical distribution sequence and the thickness of a single layer, combinations in which source end-units and storage end-units alternate and the thickness of a single layer is less than a preset interlayer thickness threshold are identified as source-storage symbiotic combinations.

[0271] Assemblies containing only interlayer endmembers but no source or storage endmembers in the vertical distribution sequence are identified as interlayer-type assemblies.

[0272] In one possible implementation, the filtering module 1006 is specifically used for:

[0273] The physical properties and oil-bearing parameters of each lithofacies assemblage type were obtained. The physical properties included porosity and permeability, while the oil-bearing parameters included total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index.

[0274] Calculate the overall mean values ​​of parameters such as porosity, permeability, total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index for all types of lithofacies assemblages.

[0275] Calculate the difference between the mean value of the parameters and the total mean value of the corresponding parameters for each type of lithofacies assemblage under each parameter;

[0276] The differences between lithofacies assemblage types under various parameters are summed to obtain the comprehensive reference value corresponding to the lithofacies assemblage type.

[0277] The lithofacies assemblage types are sorted based on comprehensive reference values, and the lithofacies assemblage types with the top two comprehensive reference values ​​in the sequence are identified as target lithofacies assemblages.

[0278] The shale dominant lithofacies assemblage screening device provided in this embodiment can be used to perform the above-described shale dominant lithofacies assemblage screening method. Its implementation principle and technical effect are similar, and will not be described again in this embodiment.

[0279] Figure 11 A schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application, such as... Figure 11 As shown, the electronic device 110 includes at least one processor 1101 and a memory 1102. Optionally, the electronic device 110 also includes a communication component 1103. The processor 1101, memory 1102, and communication component 1103 are connected via a bus 1104.

[0280] In the specific implementation process, at least one processor 1101 executes computer execution instructions stored in memory 1102, causing at least one processor 1101 to perform the above method.

[0281] The specific implementation process of processor 1101 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0282] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0283] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0284] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0285] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0286] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0287] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0288] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0289] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0290] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0291] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0292] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0293] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0294] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for screening dominant lithofacies assemblages of shale, characterized in that, include: Obtain petrological characteristic data, total organic carbon content data, pyrolysis analysis data, and well logging data and pressure test data from the target well from shale core samples; Based on the pyrolysis analysis data and total organic carbon content data, the thresholds for classifying organic matter abundance and oil content were determined. The yield index is calculated based on the pyrolysis analysis data, and the yield index threshold is determined. Based on the organic matter abundance classification threshold, the preset mineral content classification threshold, the preset sedimentary structure thickness classification threshold, petrological characteristic data, and total organic carbon content data, multiple lithofacies types are obtained. Based on the total organic carbon content data, yield index, organic matter abundance classification threshold, oil-bearing classification threshold, and yield index classification threshold, source-reservoir function analysis is performed on the lithofacies type to obtain multiple source-reservoir function end-members. Based on the well logging data and pressure test data, the vertical boundaries of the lithofacies assemblages are delineated, and multiple lithofacies assemblage types are identified within the assemblage boundaries based on the spatial configuration relationship of the source and reservoir functional units. Obtain the physical properties and oil-bearing parameters of the lithofacies assemblage type, and screen the target lithofacies assemblage based on the physical properties and oil-bearing parameters.

2. The method according to claim 1, characterized in that, Based on the pyrolysis analysis data and total organic carbon content data, the thresholds for organic matter abundance and oil content were determined, including: Obtain the raw oil content data from the pyrolysis analysis data, and correct the raw oil content data based on the preset contamination correction model to obtain the corrected oil content data; A first cross plot is generated based on the corrected oil content data and total organic carbon content data; Identify the upper inflection point in the first intersection graph, and determine the organic matter abundance threshold based on the total organic carbon content value corresponding to the upper inflection point; Identify the lower inflection point in the first intersection graph, and determine the oil content classification threshold based on the corrected oil content data value corresponding to the lower inflection point.

3. The method according to claim 1, characterized in that, The yield index is calculated based on the pyrolysis analysis data, and the yield index threshold is determined, including: The yield index is calculated based on the oil content parameters and hydrocarbon generation potential parameters in the pyrolysis analysis data. The difference in hydrocarbon generation potential is determined based on the pyrolysis analysis data. A second cross plot is generated based on the yield index and the difference in hydrocarbon generation potential. Identify the intersection point of the trend line and the hydrocarbon generation potential difference axis in the second intersection plot, and determine the yield index division threshold based on the yield index value corresponding to the intersection point.

4. The method according to claim 1, characterized in that, Based on the organic matter abundance threshold, preset mineral content threshold, preset sedimentary structure thickness threshold, petrological characteristic data, and total organic carbon content data, multiple lithofacies types are obtained, including: The shale core samples are divided according to the organic matter abundance classification threshold to obtain organic matter abundance classification results, which include organic matter-rich shale and organic matter-poor shale. The shale core samples are divided according to a preset mineral content classification threshold to obtain mineral content classification results, which include clayey shale, carbonate shale, felsic shale and mixed shale. The shale core samples are divided according to a preset sedimentary structure thickness threshold to obtain structural division results, which include laminated shale, layered shale and massive shale. By cross-combining the results of organic matter abundance classification, mineral content classification, and structural classification, multiple lithofacies types are obtained.

5. The method according to claim 1, characterized in that, Based on the total organic carbon content data, yield index, organic matter abundance classification threshold, oil-bearing classification threshold, and yield index classification threshold, source-reservoir function analysis is performed on the lithofacies type to obtain multiple source-reservoir function end-members, including: A third cross plot is generated based on the total organic carbon content data and yield index, and the sample points corresponding to each lithofacies type are projected onto the third cross plot. Based on the total organic carbon content value corresponding to the organic matter abundance threshold, the oil content value corresponding to the oil content threshold, and the yield index value corresponding to the yield index threshold, the endmember type of each projected sample point is determined to obtain the endmember type corresponding to each sample point. Based on the end-member types of the sample points included in each lithofacies type, the source-reservoir functional end-members corresponding to each lithofacies type are determined.

6. The method according to claim 1, characterized in that, Based on the well logging data and pressure test data, the vertical boundaries of the lithofacies assemblages are delineated, and multiple lithofacies assemblage types are identified within the assemblage boundaries based on the spatial configuration of the source and reservoir functional units, including: The sedimentary cycle interface is identified based on the sonic transit time logging curve and density logging curve in the logging data. Based on the pressure test data, identify pressure change feature points, and determine pressure inflection points based on the pressure values ​​corresponding to the pressure change feature points; The vertical boundary of the lithofacies assemblage is defined by taking the pressure inflection point as the main boundary and combining it with the sedimentary cycle interface. Within the boundary of the assembly, obtain the source and storage function terminal type corresponding to each depth point, and generate a vertical distribution sequence of the source and storage function terminal types; Based on the vertical stacking order of the source endmember and the storage endmember in the vertical distribution sequence, the types of the combined structures are identified to obtain a lower source upper storage type combined structure and an upper source lower storage type combined structure. Based on the thickness ratio of source endmembers to reservoir endmembers and the interlayer distribution characteristics in the vertical distribution sequence, the types of the assemblies are identified as thick source-thin reservoir assemblies and thick reservoir-thin source assemblies. Based on the frequency of alternating appearance of source end-units and storage end-units and the thickness of a single layer in the vertical distribution sequence, the combination of alternating source end-units and storage end-units with a single layer thickness less than a preset interlayer thickness threshold is identified as a source-storage symbiotic combination. The combination of the vertically distributed sequence that does not contain source endmembers and storage endmembers but only interlayer endmembers is identified as an interlayer type combination.

7. The method according to claim 1, characterized in that, Obtain the physical properties and oil-bearing parameters of the lithofacies assemblage type, and screen for target lithofacies assemblages based on the physical properties and oil-bearing parameters, including: The physical properties and oil-bearing parameters of each lithofacies assemblage type are obtained, wherein the physical properties include porosity and permeability, and the oil-bearing parameters include total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index. Calculate the overall average values ​​of the parameters for porosity, permeability, total organic carbon content, oil content, hydrocarbon generation potential, and oil saturation index for all lithofacies assemblage types; Calculate the difference between the mean value of the parameters and the total mean value of the corresponding parameters for each type of lithofacies assemblage under each parameter; The differences between the lithofacies assemblage types under each parameter are summed to obtain the comprehensive reference value corresponding to the lithofacies assemblage type; The lithofacies assemblage types are sorted based on the comprehensive reference value, and the lithofacies assemblage types with the comprehensive reference value ranking first or second in the sequence are identified as the target lithofacies assemblage.

8. A screening device for dominant shale lithofacies assemblages, characterized in that, include: The acquisition module is used to acquire petrological characteristic data, total organic carbon content data, pyrolysis analysis data, and well logging data and pressure test data of the target well from shale core samples; The analysis module is used to determine the organic matter abundance threshold and oil content threshold based on the pyrolysis analysis data and total organic carbon content data. The analysis module is also used to calculate the yield index based on the pyrolysis analysis data and determine the yield index division threshold. The type differentiation module is used to classify multiple lithofacies types based on the organic matter abundance classification threshold, the preset mineral content classification threshold, the preset sedimentary structure thickness classification threshold, petrological characteristic data, and total organic carbon content data. The source-reservoir module is used to perform source-reservoir function analysis on the lithofacies type based on the total organic carbon content data, yield index, organic matter abundance classification threshold, oil-bearing classification threshold and yield index classification threshold, and obtain multiple source-reservoir function end-units. The identification module is used to delineate the vertical boundaries of lithofacies assemblages based on the well logging data and pressure test data, and to identify multiple lithofacies assemblages types within the boundaries of the assemblages based on the spatial configuration relationship of the source and reservoir functional units. The screening module is used to obtain the physical property parameters and oil-bearing parameters of the lithofacies assemblage type, and to screen the target lithofacies assemblage based on the physical property parameters and oil-bearing parameters.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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