Coal reservoir geological modeling method, device and equipment based on macroscopic coal rock type and medium

Abstract

The invention relates to the field of oil and gas reservoir geologic modeling, and discloses a coal reservoir geologic modeling method, device and equipment based on macroscopic coal and rock types and a medium. According to the method, the three-dimensional geologic model and the gas content model of the coal reservoir are established under the constraint of the fine stratigraphic framework on the basis of fine observation and description of the rock core and in combination with macroscopic coal rock type logging electrical research, the spatial distribution rule of the favorable coal reservoir is finely described, and finally the favorable development area of the coal bed gas is determined. According to the method, a scientific basis is provided for deeply revealing the heterogeneity of the internal space of the coal reservoir, the prediction reliability of the coal reservoir can be remarkably improved, important technical support is provided for key links such as well location optimization deployment and fracturing design, the space distribution rule of the internal gas content of the coal reservoir is comprehensively and finely described, and the prediction accuracy is improved. And sweet spot prediction of the coal reservoir can be realized.

Description

Technical Field

[0001] The present invention relates to the field of geological modeling of oil and gas reservoirs, and particularly to a method, device, equipment and medium for coal reservoir geological modeling based on macroscopic coal rock types. Background Art

[0002] In recent years, with the continuous growth of the global demand for oil and gas resources, coalbed methane, as an important part of unconventional oil and gas resources, has gradually become a new research focus in the field of oil and gas resource exploration and development. In particular, the further exploration and development of deep coal seams have become the core focus of the current coalbed methane industry.

[0003] Related technologies generally only focus on the hydrocarbon source rock properties of coal, ignoring the reservoir rock properties and heterogeneity characteristics of coal seams. Specifically, the heterogeneity within coal seams is mainly manifested in two aspects: First, there are significant differences in resource properties among different coal rock types, which are mainly reflected in the differences in the internal material composition, adsorption capacity, and reservoir capacity of coal seams; Second, different coal rock types show obvious differences in transformability, and this heterogeneity will significantly affect the development characteristics of the original joints and fractures in coal seams, and further have an important impact on the extension direction and range of fracturing fractures.

[0004] The systematic research methods for the spatial heterogeneity within coal seams in related technologies are not yet mature, making it difficult to comprehensively and accurately depict the heterogeneous characteristics within coal seams, posing a huge challenge to the sweet spot prediction of coal reservoirs. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for coal reservoir geological modeling based on macroscopic coal rock types, to solve the defect in related technologies that it is difficult to comprehensively and accurately depict the heterogeneous characteristics within coal seams, establish a gas content model, comprehensively and finely depict the spatial distribution law of gas content within coal reservoirs, and facilitate the realization of sweet spot prediction of coal reservoirs.

[0006] In a first aspect, the present invention proposes a method for coal reservoir geological modeling based on macroscopic coal rock types, including:

[0007] Preprocess the logging data and seismic data in the study area to obtain processed logging data and processed seismic data;

[0008] Based on the macroscopic coal rock types observed from core samples and the processed logging data, establish a classification standard for macroscopic coal rock types; and, based on the macroscopic coal rock types and the processed logging data, conduct fine stratigraphic correlation to obtain a fine stratigraphic framework;

[0009] According to the fine stratigraphic framework and the processed seismic data, conduct stratigraphic modeling and fault modeling to obtain the structural model of the study area;

[0010] Determine the macro-lithotype of each well in the study area based on the macro-lithotype classification standard, determine the distribution characteristics of the macro-lithotype based on the macro-lithotype of each well, and construct a three-dimensional macro-lithotype model of the study area based on the macro-lithotype of each well, the distribution characteristics of the macro-lithotype, and the structural model;

[0011] Construct a gas-bearing property model of the study area according to the three-dimensional macro-lithotype model and the relationship between the macro-lithotype and the gas-bearing property; the gas-bearing property model is used to quantitatively reflect the spatial distribution characteristics of the gas content in the coal reservoir in the study area.

[0012] Optionally, the establishment of the macro-lithotype classification standard based on the macro-lithotype observed from core samples and the processed well logging data includes:

[0013] Refinely divide the macro-lithotype based on the core observation data of the study area, and match the core data with the processed well logging data through depth attribution correction; construct multi-dimensional cross-plot plates of multiple key well logging parameters and the macro-lithotype to determine the processed well logging data corresponding to different categories of macro-lithotypes; use statistical methods to determine the distribution intervals of each macro-lithotype in multiple key well logging parameters, and determine the critical thresholds for distinguishing different macro-lithotypes; establish a discrimination function or a machine learning classification model based on multi-parameter characteristics to establish the macro-lithotype classification standard; wherein, the macro-lithotype classification standard includes the value ranges of well logging parameters corresponding to multiple macro-lithotypes, and each value range of the well logging parameters includes the value ranges of multiple key well logging parameters.

[0014] Optionally, the construction of the structural model of the study area by performing formation modeling and fault modeling according to the fine stratigraphic framework and the processed seismic data includes:

[0015] Determine the model grid size and direction according to the well pattern density and the scale of geological bodies in the study area, and establish a skeleton grid model of the study area according to the model grid size and direction;

[0016] On the basis of the skeleton grid model, construct the fault model based on the processed seismic data, and generate the formation model by interpolating the formation surfaces using the well logging data under the constraint of the seismic trend;

[0017] If there are horizontal wells in the study area, make full use of the multi-branch horizontal well data to correct the structural surfaces of the formation model by forward gamma ray logging while drilling to obtain a corrected formation model, and then determine the corrected formation model and the fault model as the structural model as a whole;

[0018] If there is no horizontal well in the study area, directly determine the overall fault model and the formation model as the structural model.

[0019] Optionally, the fine stratigraphic correlation based on the macroscopic coal petrological type and the processed well logging data to obtain a fine stratigraphic framework includes:

[0020] Reveal the water body change during the coal accumulation period according to the processed well logging data and the macroscopic coal petrological type combination, draw the coal seam formation curve based on the geochemical characteristic parameters, identify the sedimentary cycle units and secondary layer interfaces in the study area, and conduct fine stratigraphic correlation under the control of the cycle to obtain the fine stratigraphic framework.

[0021] Optionally, the determination of the single-well macroscopic coal petrological type in the study area based on the macroscopic coal petrological type classification standard includes:

[0022] Based on the macroscopic coal petrological type classification standard, conduct macroscopic coal petrological division on the single well in the study area to obtain the single-well macroscopic coal petrological type;

[0023] The determination of the macroscopic coal petrological type distribution characteristics based on the single-well macroscopic coal petrological type includes:

[0024] Conduct statistical analysis on the single-well macroscopic coal petrological type, calculate the thickness values of the macroscopic coal petrological types in different single wells and the proportions of the macroscopic coal petrological types at the same vertical grid depth, so as to determine the overall distribution characteristics and vertical spatial distribution laws of the macroscopic coal petrological types in the study area at the regional scale, and take them as the macroscopic coal petrological type distribution characteristics of the study area as a whole.

[0025] Optionally, the construction of the three-dimensional macroscopic coal petrological type model of the study area based on the single-well macroscopic coal petrological type, the macroscopic coal petrological type distribution characteristics, and the structural model includes:

[0026] Map the single-well macroscopic coal petrological type to the geological space defined by the structural model through grid coarsening and retain the vertical resolution; combine the processed well logging data, the processed seismic data and the macroscopic coal petrological type distribution characteristics, and use geological statistical algorithms to generate an initial three-dimensional macroscopic coal petrological type model under the constraint of the structural model; conduct reliability tests on the initial three-dimensional macroscopic coal petrological type model through comparison with the actual observation data of new wells or blind wells and statistical characteristic parameter matching degree analysis. When it is determined that the reliability test passes, determine the initial three-dimensional macroscopic coal petrological type model as the three-dimensional macroscopic coal petrological type model.

[0027] Optionally, the macroscopic coal rock types include: bright coal, semi-bright coal, semi-dark coal and dark coal; and constructing the gas-bearing property model of the study area based on the three-dimensional macroscopic coal rock type model and the relationship between the macroscopic coal rock type and the gas-bearing property includes:

[0028] Based on core description and gas content test analysis, a first relationship between macroscopic coal rock type and gas content was determined, showing that bright coal and semi-bright coal have higher gas content than semi-dark coal and dark coal. Based on petrophysical analysis, a second relationship between macroscopic coal rock type and gas content was determined, showing that the development of pore and fracture structures in bright coal and semi-bright coal is conducive to gas adsorption. Based on production dynamic data analysis, a third relationship between macroscopic coal rock type and gas content was determined, showing that the productivity of dark coal and semi-dark coal wells is significantly lower than that of bright coal and semi-bright coal wells.

[0029] Based on the first, second and third relationships between macroscopic coal rock types and gas content, under the constraints of the three-dimensional macroscopic coal rock type model, the gas content data measured on the well is used as hard constraint data, and the gas content model is constructed using a phase-controlled simulation method.

[0030] In a second aspect, the present invention proposes a coal reservoir geological modeling method based on macroscopic coal rock types, comprising:

[0031] A preprocessing unit, used for preprocessing the well logging data and seismic data in the study area to obtain processed well logging data and processed seismic data;

[0032] A standard establishing unit, for establishing a macro coal and rock type classification standard based on the macro coal and rock types observed by coring and the processed logging data;

[0033] A stratigraphic correlation unit, configured to perform fine stratigraphic correlation based on macroscopic coal and rock types and the processed well logging data to obtain a fine stratigraphic framework;

[0034] a structural modeling unit, configured to perform stratigraphic modeling and fault modeling based on the refined stratigraphic framework and the processed seismic data to obtain a structural model of the study area;

[0035] a single-well macroscopic coal and rock type determination unit, configured to determine the single-well macroscopic coal and rock type of the study area based on the macroscopic coal and rock type classification standard;

[0036] a feature determination unit, configured to determine a macroscopic coal rock type distribution feature based on the macroscopic coal rock type of the single well;

[0037] A macro coal-rock type modeling unit, configured to construct a three-dimensional macro coal-rock type model of the study area based on the macro coal-rock type of the single well, the distribution characteristics of the macro coal-rock type, and the structural model;

[0038] A gas-bearing property modeling unit for constructing a gas-bearing property model of the study area according to the three-dimensional macroscopic coal petrographic type model and the relationship between the macroscopic coal petrographic type and the gas-bearing property; the gas-bearing property model is used to quantitatively reflect the spatial distribution characteristics of the gas content in the coal reservoir in the study area.

[0039] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the coal reservoir geological modeling method based on the macroscopic coal petrographic type in the first aspect or any corresponding embodiment thereof.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the coal reservoir geological modeling method based on the macroscopic coal petrographic type in the first aspect or any corresponding embodiment thereof.

[0041] The coal reservoir geological modeling method, device, equipment and medium provided by the present invention can preprocess the logging data and seismic data in the study area to obtain the processed logging data and the processed seismic data. Based on the macroscopic coal petrographic type observed from core samples and the processed logging data, a classification standard for macroscopic coal petrographic types is established; and, based on the macroscopic coal petrographic type and the processed logging data, a fine stratigraphic correlation is performed to obtain a fine stratigraphic framework. According to the fine stratigraphic framework and the processed seismic data, a stratigraphic modeling and a fault modeling are carried out to obtain a structural model of the study area. Based on the classification standard of macroscopic coal petrographic types, the distribution characteristics of the macroscopic coal petrographic types in the study area are determined, and based on the distribution characteristics of the macroscopic coal petrographic types and the structural model, a three-dimensional macroscopic coal petrographic type model of the study area is constructed. According to the three-dimensional macroscopic coal petrographic type model and the relationship between the macroscopic coal petrographic type and the gas-bearing property, a gas-bearing property model of the study area is constructed; the gas-bearing property model is used to quantitatively reflect the spatial distribution characteristics of the gas content in the coal reservoir in the study area. The present invention provides a scientific basis for deeply revealing the internal spatial heterogeneity of the coal reservoir, can significantly improve the reliability of favorable coal reservoir prediction, provides important technical support in key links such as well location optimization deployment and fracturing design, comprehensively and finely depicts the spatial distribution law of the gas content inside the coal reservoir, and is conducive to realizing the prediction of sweet spots in the coal reservoir. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Figure 1 A flow chart of a coal reservoir geological modeling method based on macroscopic coal rock types provided by an embodiment of the present invention;

[0044] Figure 2 A flow chart of another method for coal reservoir geological modeling based on macroscopic coal rock types provided by an embodiment of the present invention;

[0045] Figure 3 A macroscopic coal and rock type interpretation diagram for a single well provided in an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of a construction model provided by an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of a three-dimensional macroscopic coal and rock type model provided by an embodiment of the present invention;

[0048] Figure 6 A schematic diagram of a gas-containing model provided in an embodiment of the present invention;

[0049] Figure 7 A schematic structural diagram of a coal reservoir geological modeling device based on macroscopic coal rock types provided by an embodiment of the present invention;

[0050] Figure 8 A schematic structural diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0052] Macroscopic coal rock types are crucial for the detailed characterization of coal reservoirs. Different macroscopic coal rock types exhibit significant differences in hydrocarbon generation, adsorption, and storage capacity. This example, based on detailed core observation and characterization, combined with well logging electrical property studies of macroscopic coal rock types, establishes a three-dimensional geological model and gas content model of the coal reservoir within the constraints of a detailed stratigraphic framework. This allows for a detailed characterization of the spatial distribution of favorable coal reservoirs and ultimately identifies favorable areas for coalbed methane development.

[0053] The following combination Figures 1-6 The present invention describes the coal reservoir geological modeling method based on macroscopic coal rock types.

[0054] like Figure 1As shown in the figure, the first coal reservoir geological modeling method based on the macroscopic coal rock type is proposed in this embodiment. This method may include the following steps:

[0055] S101. Preprocess the logging data and seismic data in the study area to obtain the processed logging data and the processed seismic data.

[0056] Specifically, this embodiment may collect and sort out various types of basic data in the study area, mainly including regional geological reports, stratigraphic division and correlation data, core descriptions, basic well data, seismic data, etc. These rich data together constitute an important basis for coal reservoir modeling.

[0057] It should be noted that data preprocessing is a crucial and important way to improve the modeling accuracy and analysis reliability. This embodiment may perform a number of systematic preprocessing on the logging data and seismic data, including but not limited to data cleaning to remove outliers and duplicate values in the data; using a denoising algorithm for effective noise reduction; and performing standardization processing on the data. These preprocessing methods can significantly improve the data quality and provide a reliable basis for subsequent analysis and modeling.

[0058] S102. Establish a classification standard for macroscopic coal rock types based on the macroscopic coal rock types observed from the cores and the processed logging data.

[0059] Optionally, step S102 may include:

[0060] Based on the core observation data in the study area, finely divide the macroscopic coal rock types, and match the core data with the processed logging data through depth attribution correction; construct multi-dimensional cross-plot plates of multiple key logging parameters and macroscopic coal rock types to determine the processed logging data corresponding to different categories of macroscopic coal rock types; use statistical methods to determine the distribution intervals of each macroscopic coal rock type in multiple key logging parameters, and determine the critical thresholds for distinguishing different macroscopic coal rock types; establish a discriminant function or a machine learning classification model based on multi-parameter characteristics to establish a classification standard for macroscopic coal rock types; among them, the classification standard for macroscopic coal rock types includes the logging parameter value intervals corresponding to various macroscopic coal rock types, and each logging parameter value interval includes the value intervals of multiple key logging parameters.

[0061] Among them, the classification standard for macroscopic coal rock types includes the logging parameter value intervals corresponding to various macroscopic coal rock types, and each logging parameter value interval includes the value intervals of multiple key logging parameters.

[0062] Specifically, this embodiment can perform quantitative analysis of macroscopic coal petrological types. Macroscopic coal petrological types are widely used in coalbed methane reservoir evaluation, coal mining, and coal geology research based on different microscopic components, structural characteristics, and coal formation processes in coal seams. By observing coal rocks, four macroscopic coal petrological types can be divided: bright coal, semi-bright coal, semi-dull coal, and dull coal. These coal petrological types not only determine the heterogeneity characteristics of coal reservoirs, but their differences also directly affect the physical and chemical properties of coal seams. This embodiment uses a core-logging joint calibration method to quantitatively identify coal petrological types, and the main technical process includes four steps:

[0063] (a) In the stage of quantitative analysis of macroscopic coal petrology logging, based on core observation data, macroscopic coal petrological types are finely divided, and accurate matching of core data and logging curves is achieved through depth attribution correction; (b) In the crossplot analysis stage, multi-dimensional crossplot plates of key logging parameters such as natural gamma (GR), density (DEN), and acoustic interval transit time (AC) and macroscopic coal petrological types are constructed to reveal the logging response characteristics of different coal petrological types; (c) In the parameter determination stage, statistical methods are used to calculate the distribution intervals of each coal petrological type in parameter domains such as gamma value, density, and acoustic wave, and the critical thresholds for distinguishing different macroscopic coal petrological types are determined; (d) In the model construction stage, discriminant functions or machine learning classification models are established by integrating multi-parameter characteristics to form a systematic classification standard for macroscopic coal petrological types; (e) Divide the macroscopic coal petrological types of a single well according to the logging quantitative interpretation standard.

[0064] S103. Based on the macroscopic coal petrological types of a single well and the processed logging data, conduct fine stratigraphic correlation to obtain a fine stratigraphic framework.

[0065] Optionally, step S103 may include:

[0066] Reveal the changes in water bodies during the coal-accumulating period based on the processed logging data and the combination of macroscopic coal petrological types, draw the coal seam formation curve based on geochemical characteristic parameters, identify the sedimentary cycle units and secondary stratigraphic interfaces in the study area, and conduct fine stratigraphic correlation under the control of the cycle to obtain a fine stratigraphic framework.

[0067] Among them, this embodiment can conduct fine stratigraphic correlation. Specifically, before constructing a geological model, this embodiment finely divides coal seams and establishes a complete stratigraphic framework, which is a basic work for in-depth study of coal reservoir characteristics. This process not only requires more in-depth and detailed geological analysis of the internal structure of coal seams, but more importantly, it provides a solid geological basis for subsequent quantitative characterization of the heterogeneity of coal reservoirs.

[0068] In practical applications, this fine stratigraphic division mainly has the following important significances: (a) It can improve the reliability of coalbed methane resource assessment and is a necessary basis for coalbed methane reserve estimation; (b) It provides a scientific basis for reasonable development design and helps to formulate the optimal development plan; (c) It establishes the necessary conditions for carrying out the connectivity analysis of coal reservoirs, thus effectively solving the key technical problems encountered in actual production.

[0069] S104. Conduct stratigraphic modeling and fault modeling based on the fine stratigraphic framework and the processed seismic data to obtain the structural model of the study area.

[0070] Optionally, step S104 may include:

[0071] Determine the model grid size and direction according to the well pattern density and geological body scale of the study area, and establish the skeleton grid model of the study area based on the model grid size and direction;

[0072] On the basis of the skeleton grid model, construct a fault model based on the processed seismic data, and generate a stratigraphic model by interpolating the layer surface conversion of the well - based stratification data under the constraint of the seismic trend;

[0073] If there are horizontal wells in the study area, make full use of the multi - branch horizontal well data to correct the structural surface of the stratigraphic model by forward gamma logging while drilling, obtain the corrected stratigraphic model, and then determine the corrected stratigraphic model and the fault model as the structural model as a whole;

[0074] If there are no horizontal wells in the study area, directly determine the fault model and the stratigraphic model as the structural model as a whole.

[0075] It should be noted that structural modeling is the basis of geological modeling, mainly used to describe the geometric shape, stratigraphic structure, fault system and their mutual relationships of underground geological structures. Generally, it mainly includes surface modeling and fault modeling. Among them, the fault model is constructed from the fault data obtained by seismic interpretation, which can comprehensively reflect the three - dimensional spatial distribution characteristics and development patterns of faults. The stratigraphic model is generated by interpolating the layer surface conversion of the well - based stratification data under the constraint of the seismic trend. Structural modeling provides reliable basic data and three - dimensional visualization models for subsequent geological research and resource assessment.

[0076] S105. Determine the single - well macroscopic coal - rock type of the study area based on the macroscopic coal - rock type classification standard.

[0077] Optionally, step S105 may include:

[0078] Based on the macroscopic coal - rock type classification standard, conduct macroscopic coal - rock division on the single wells in the study area to obtain the single - well macroscopic coal - rock type.

[0079] S106. Determine the distribution characteristics of the macroscopic coal petrographic types based on the macroscopic coal petrographic types of a single well.

[0080] Optionally, step S106 may include:

[0081] Conduct a statistical analysis of the macroscopic coal petrographic types of a single well, calculate the thickness values of the macroscopic coal petrographic types in different single wells and the proportion of the macroscopic coal petrographic types at the same vertical grid depth, so as to determine the overall distribution characteristics and longitudinal spatial distribution law of the macroscopic coal petrographic types in the study area at the regional scale, and take them as the distribution characteristics of the macroscopic coal petrographic types in the study area as a whole.

[0082] Specifically, in this embodiment, a statistical analysis can be conducted on the macroscopic coal petrographic types of a single well, and the thickness values of the macroscopic coal petrographic types in different single wells and the proportion they account for at the same vertical grid depth can be calculated, so as to determine the overall distribution characteristics and longitudinal spatial distribution law of the macroscopic coal petrographic types in the study area at the regional scale.

[0083] S107. Construct a three-dimensional macroscopic coal petrographic type model of the study area based on the macroscopic coal petrographic types of a single well, the distribution characteristics of the macroscopic coal petrographic types, and the structural model.

[0084] Optionally, step S107 includes:

[0085] Map the macroscopic coal petrographic types of a single well to the geological space defined by the structural model through grid coarsening, and retain the vertical resolution; combine the processed logging data, processed seismic data and the distribution characteristics of the macroscopic coal petrographic types, and use geological statistical algorithms to generate an initial three-dimensional macroscopic coal petrographic type model under the constraint of the structural model; conduct a reliability test on the initial three-dimensional macroscopic coal petrographic type model through comparison with the actual observation data of new drilled wells or blind wells and statistical characteristic parameter matching degree analysis. When it is determined that the reliability test passes, the initial three-dimensional macroscopic coal petrographic type model is determined as the three-dimensional macroscopic coal petrographic type model.

[0086] Specifically, in this embodiment, the distribution characteristics of the macroscopic coal petrographic types in the study area can be utilized, and a three-dimensional macroscopic coal petrographic type model can be constructed by means of geostatistical co-simulation under the constraint of the seismic conversion probability volume.

[0087] Specifically, the establishment of a three-dimensional macroscopic coal-lithology model consists of three main steps: (a) Grid coarsening: Using grid coarsening techniques, single-well macroscopic coal-lithology data are mapped to the geological space defined by the structural model while maintaining vertical resolution; (b) Collaborative simulation: Combining processed well logging data with processed seismic data, and applying geostatistical algorithms, a three-dimensional macroscopic coal-lithology model is generated within the constraints of the structural model; and (c) Model validation: The reliability of the established geological model is verified through comparison with actual observations from newly drilled or blind wells and statistical characteristic parameter matching analysis. In this process, the key input data consists of two main components: discretized well-point macroscopic coal-lithology data as hard data; and macroscopic coal-lithology probability volumes derived from seismic data as three-dimensional trend field constraints. It is important to note that the variogram is the core control parameter in the modeling process. The determination of its structural characteristic parameters requires a comprehensive understanding of the sedimentary evolution of the study area and the results of geological background analysis. Different parameters can significantly affect the spatial distribution of macroscopic coal-lithology types.

[0088] S108. Based on the three-dimensional macroscopic coal rock type model and the relationship between the macroscopic coal rock type and gas content, a gas content model of the study area is constructed; the gas content model is used to quantitatively reflect the spatial distribution characteristics of the gas content of the coal reservoir in the study area.

[0089] Optionally, the macroscopic coal rock types include: bright coal, semi-bright coal, semi-dark coal and dark coal. Step S108 may include:

[0090] Based on core description and gas content test analysis, the first relationship between macroscopic coal rock type and gas content was determined. The first relationship is that bright coal and semi-bright coal have higher gas content than semi-dark coal and dark coal. Based on rock physics analysis, the second relationship between macroscopic coal rock type and gas content was determined. The second relationship is that the development of pore and fracture structures in bright coal and semi-bright coal is conducive to gas adsorption. Based on production dynamic data analysis, the third relationship between macroscopic coal rock type and gas content was determined. The third relationship is that the production capacity of dark coal and semi-dark coal wells is significantly lower than that of bright coal and semi-bright coal wells.

[0091] Based on the first, second and third relationships between macroscopic coal and rock types and gas content, and under the constraints of a three-dimensional macroscopic coal and rock type model, a gas content model was constructed using phase-controlled simulation with the gas content data measured on the well as hard constraint data.

[0092] Specifically, when conducting gas content modeling under the constraint of macroscopic coal petrographic types in this embodiment, the relationship between different macroscopic coal petrographic types and gas content can be analyzed first: (a) Based on core description and gas content test analysis, it is revealed that the gas content of bright coal and semi-bright coal is generally higher than that of semi-dull coal and dull coal. (b) Rock physics analysis confirms that the pore-fracture structures of bright coal and semi-bright coal are well-developed, which is conducive to gas adsorption. (c) Based on the analysis of production dynamic data: The production capacity of wells rich in dull coal petrographic types (dull coal and semi-dull coal) is significantly lower than that of wells rich in bright coal petrographic types (bright coal and semi-bright coal). Secondly, based on the core gas content test data, under the guidance of the correlation between macroscopic coal petrographic types and gas content, a gas content model is constructed with the three-dimensional macroscopic coal petrographic type model as the constraint framework. Based on the macroscopic coal petrographic type - gas content spatial coupling model, high-gas sweet spots are delineated, and well location deployment is optimized.

[0093] It should be noted that this embodiment can significantly improve the reliability of favorable coal reservoir prediction, provide important technical support for key links such as well location optimization deployment and fracturing design, and strongly promote the efficient development of coalbed methane.

[0094] The method for coal reservoir geological modeling based on macroscopic coal petrographic types proposed in this embodiment can preprocess the logging data and seismic data in the study area to obtain the processed logging data and processed seismic data. Based on the macroscopic coal petrographic types observed from core sampling and the processed logging data, a classification standard for macroscopic coal petrographic types is established to divide the macroscopic coal petrographic types of single wells; and, based on the macroscopic coal petrographic types of single wells and the processed logging data, fine stratigraphic correlation is carried out to obtain a fine stratigraphic framework. According to the fine stratigraphic framework and the processed seismic data, stratigraphic modeling and fault modeling are carried out to obtain the structural model of the study area. Based on the macroscopic coal petrographic types of single wells, the distribution characteristics of macroscopic coal petrographic types in the study area are determined, and based on the macroscopic coal petrographic types of single wells, the distribution characteristics of macroscopic coal petrographic types, and the structural model, a three-dimensional macroscopic coal petrographic type model of the study area is constructed. According to the three-dimensional macroscopic coal petrographic type model and the relationship between macroscopic coal petrographic types and gas content, a gas content model of the study area is constructed; the gas content model is used to quantitatively reflect the spatial distribution characteristics of the gas content in the coal reservoirs in the study area. This embodiment provides a scientific basis for deeply revealing the internal spatial heterogeneity of coal reservoirs, can significantly improve the reliability of favorable coal reservoir prediction, provide important technical support for key links such as well location optimization deployment and fracturing design, comprehensively and finely depict the spatial distribution law of the gas content inside the coal reservoirs, and is conducive to realizing the prediction of sweet spots in coal reservoirs.

[0095] For better illustration of Figure 1 each step in, Example 1 is proposed in this embodiment for introduction.

[0096] Example 1, as Figure 2 shown, this embodiment takes the coal seam in a certain oilfield area as the research object for modeling, which specifically includes the following steps:

[0097] Step 1: Collect and organize various types of basic data, mainly including regional geological reports, stratigraphic division and correlation data, core descriptions, basic well data, and seismic data, etc. These rich data together constitute an important basis for coal reservoir modeling.

[0098] Step 2: Data preprocessing is a crucial important step to improve the modeling accuracy and analysis reliability. In this embodiment, a number of systematic preprocessing methods are adopted for well logging data and seismic data. These include but are not limited to: (a) performing data cleaning to remove outliers and duplicate values in the data; (b) using denoising algorithms for effective noise reduction; (c) performing normalization processing on the data. These preprocessing methods significantly improve the data quality and provide a reliable basis for subsequent analysis and modeling.

[0099] Step 3: By calibrating the macroscopic coal rock types observed from core samples with different well logging curves (where the well logging data mainly includes gamma, density, neutron, acoustic wave, resistivity), analyze the relationship between macroscopic coal rock types and well logging responses, and finally establish a quantitative well logging discrimination criterion to identify different macroscopic coal rock types. Interpret the macroscopic coal rock types of single wells in the study area according to the macroscopic coal rock type standard established based on well logging, as Figure 3 shown.

[0100] Step 4: Reveal the water body changes during the coal accumulation period according to well logging curves and the combination of macroscopic coal rock types. Based on detailed geochemical characteristic parameters (including reflectance, mineral composition analysis, etc.), draw the coal seam formation curve, and identify the sedimentary cycle units and secondary stratigraphic interfaces in the study area. Conduct fine stratigraphic correlation under the control of the cycle, as Figure 3 shown, to provide a basis for the next step of establishing a geological model.

[0101] Step 5: Set up a skeleton grid model with appropriate model grid size and direction according to the well pattern density and geological body scale in the study area. Specifically, it includes: (a) Under the condition of ensuring calculation efficiency, evaluate the accuracy and stability of the model by adjusting the grid size, and finally determine the grid size that meets the actual needs; (b) The grid direction should consider the main tectonic trend and the provenance direction to better reflect the actual geological characteristics. Finally, based on the skeleton grid model, establish a stratigraphic model and a fault model using drilling data and seismic interpretation data, as Figure 4 shown. In addition, for horizontal well stratigraphic modeling, the multi-branch horizontal well data can be fully utilized to correct the tectonic surface based on the forward gamma ray while drilling. The principle is to improve the accuracy and reliability of the stratigraphic model by changing the tectonic surface to make the synthetic gamma ray curve of adjacent vertical wells fit the gamma ray curve of the horizontal well.

[0102] Step 6: Conduct statistical analysis on the macroscopic coal rock types of individual wells, calculate the thickness values of the macroscopic coal rock types in different individual wells and the proportions they account for at the same vertical grid depth, so as to determine the overall distribution characteristics of the macroscopic coal rock types in the study area at the regional scale and the longitudinal spatial distribution law. On the basis of the statistical analysis, using the data of the macroscopic coal rock types on the ground, under the constraint of the seismic conversion probability volume, a three-dimensional model of the macroscopic coal rock types is constructed by using the method of geostatistical co-simulation, as Figure 5 shown

[0103] Step 7: Based on the relationship between gas content and macroscopic coal rock types, under the constraint of the macroscopic coal rock type model, using the gas content data measured on the ground as hard constraint data, a gas-bearing property model of the coal reservoir is constructed by using the method of facies-controlled simulation, as Figure 6 shown. This model can quantitatively reflect the spatial distribution characteristics of the gas content in the coal reservoir, and significantly improve the recognition accuracy of high gas content areas. By analyzing this gas-bearing property model, the well location deployment can be optimized, and a scientific basis can be provided for the fracturing design.

[0104] As Figure 7 shown, this embodiment proposes a coal reservoir geological modeling device based on macroscopic coal rock types, including:

[0105] A preprocessing unit 701, configured to preprocess the logging data and seismic data in the study area to obtain processed logging data and processed seismic data;

[0106] A standard establishment unit 702, configured to establish a classification standard for macroscopic coal rock types based on the macroscopic coal rock types observed from core samples and the processed logging data;

[0107] A formation correlation unit 703, configured to perform fine formation correlation based on the macroscopic coal rock types and the processed logging data to obtain a fine formation framework;

[0108] A structural modeling unit 704, configured to perform formation modeling and fault modeling according to the fine formation framework and the processed seismic data to obtain a structural model of the study area;

[0109] An individual well macroscopic coal rock type determination unit 705, configured to determine the macroscopic coal rock types of individual wells in the study area based on the macroscopic coal rock type classification standard;

[0110] A feature determination unit 706, configured to determine the distribution characteristics of macroscopic coal rock types based on the macroscopic coal rock types of individual wells;

[0111] A macroscopic coal rock type modeling unit 707, configured to construct a three-dimensional macroscopic coal rock type model of the study area based on the macroscopic coal rock types of individual wells, the distribution characteristics of macroscopic coal rock types, and the structural model;

[0112] The gas-bearing property modeling unit 708 is used to construct a gas-bearing property model of the study area according to the three-dimensional macroscopic coal petrographic type model and the relationship between the macroscopic coal petrographic type and the gas-bearing property; the gas-bearing property model is used to quantitatively reflect the spatial distribution characteristics of the gas content in the coal reservoir in the study area.

[0113] Optionally, the standard establishment unit 702 is further used for:

[0114] Based on the core observation data of the study area, the macroscopic coal petrographic type is finely divided, and the core data and the processed logging data are matched through depth attribution correction; multiple multi-dimensional intersection charts of key logging parameters and the macroscopic coal petrographic type are constructed to determine the processed logging data corresponding to different categories of macroscopic coal petrographic types; statistical methods are used to determine the distribution intervals of each macroscopic coal petrographic type in multiple key logging parameters, and the critical thresholds for distinguishing different macroscopic coal petrographic types are determined; a discrimination function or a machine learning classification model is established based on multi-parameter characteristics to establish a classification standard for the macroscopic coal petrographic type; among them, the classification standard for the macroscopic coal petrographic type includes the logging parameter value intervals corresponding to multiple macroscopic coal petrographic types, and each logging parameter value interval includes the value intervals of multiple key logging parameters.

[0115] Optionally, the structural modeling unit 704 is further used for:

[0116] Determine the model grid size and direction according to the well pattern density and the geological body scale of the study area, and establish a skeleton grid model of the study area according to the model grid size and direction;

[0117] On the basis of the skeleton grid model, a fault model is constructed based on the processed seismic data, and a formation model is generated by layer conversion of interpolation of well logging data under the constraint of the seismic trend;

[0118] If there are horizontal wells in the study area, make full use of the multi-branch horizontal well data to correct the structural plane of the formation model by forward gamma logging while drilling to obtain a corrected formation model, and then determine the corrected formation model and the fault model as a whole as the structural model;

[0119] If there are no horizontal wells in the study area, directly determine the fault model and the formation model as a whole as the structural model.

[0120] Optionally, the stratigraphic correlation unit 703 is further used for:

[0121] Reveal the water body change during the coal-accumulating period according to the processed logging data and the macroscopic coal petrographic type combination, draw a coal seam formation curve based on the geochemical characteristic parameters, identify the sedimentary cycle unit and the secondary layer interface in the study area, and conduct fine stratigraphic correlation under the control of the cycle to obtain a fine stratigraphic framework.

[0122] Optionally, the single - well macroscopic coal - rock type determination unit 705 is further configured to:

[0123] Based on the macroscopic coal - rock type classification standard, conduct macroscopic coal - rock division on the single wells in the study area to obtain the single - well macroscopic coal - rock types;

[0124] Optionally, the feature determination unit 706 is further configured to:

[0125] Conduct statistical analysis on the single - well macroscopic coal - rock types, calculate the thickness values of the macroscopic coal - rock types in different single wells and the proportions of the macroscopic coal - rock types at the same vertical grid depth, so as to determine the overall distribution characteristics and longitudinal spatial distribution laws of the macroscopic coal - rock types in the study area at the regional scale, and take them as the distribution characteristics of the macroscopic coal - rock types in the study area as a whole.

[0126] Optionally, the macroscopic coal - rock type modeling unit 707 is further configured to:

[0127] Map the single - well macroscopic coal - rock types to the geological space defined by the structural model through grid coarsening and retain the vertical resolution; combine the processed well - logging data, processed seismic data and the distribution characteristics of the macroscopic coal - rock types, and use geological statistical algorithms to generate an initial three - dimensional macroscopic coal - rock type model under the constraint of the structural model; conduct reliability tests on the initial three - dimensional macroscopic coal - rock type model through comparison with the actual observation data of new drilled wells or blind wells and statistical characteristic parameter matching degree analysis. When it is determined that the reliability test passes, determine the initial three - dimensional macroscopic coal - rock type model as the three - dimensional macroscopic coal - rock type model.

[0128] Optionally, the macroscopic coal - rock types include: bright coal, semi - bright coal, semi - dull coal and dull coal. The gas - bearing property modeling unit 708 is further configured to:

[0129] Based on core description and gas - bearing property test analysis, determine the first relationship between the macroscopic coal - rock type and the gas - bearing property. The first relationship is that the gas - bearing properties of bright coal and semi - bright coal are higher than those of semi - dull coal and dull coal; based on rock physics analysis, determine the second relationship between the macroscopic coal - rock type and the gas - bearing property. The second relationship is that the well - developed pore - fracture structures of bright coal and semi - bright coal are conducive to gas adsorption; based on production dynamic data analysis, determine the third relationship between the macroscopic coal - rock type and the gas - bearing property. The third relationship is that the well production capacity of dull coal and semi - dull coal is significantly lower than that of bright coal and semi - bright coal;

[0130] Based on the first relationship, second relationship and third relationship between the macroscopic coal - rock type and the gas - bearing property, under the constraint of the three - dimensional macroscopic coal - rock type model, use the measured gas content data on the well as hard - constraint data and adopt a facies - controlled simulation method to construct the gas - bearing property model.

[0131] The coal reservoir geological modeling device based on macroscopic coal petrographic types proposed in this embodiment can preprocess well logging data and seismic data in the study area to obtain processed well logging data and processed seismic data. Based on the macroscopic coal petrographic types observed from core samples and the processed well logging data, calibration is carried out to establish a classification standard for macroscopic coal petrographic types and divide the macroscopic coal petrographic types of a single well. Moreover, based on the macroscopic coal petrographic types of a single well and the processed well logging data, fine stratigraphic correlation is carried out to obtain a fine stratigraphic framework. According to the fine stratigraphic framework and the processed seismic data, stratigraphic modeling and fault modeling are carried out to obtain the structural model of the study area. Based on the macroscopic coal petrographic types of a single well, the distribution characteristics of macroscopic coal petrographic types in the study area are determined, and based on the macroscopic coal petrographic types of a single well, the distribution characteristics of macroscopic coal petrographic types, and the structural model, a three-dimensional macroscopic coal petrographic type model of the study area is constructed. According to the three-dimensional macroscopic coal petrographic type model and according to the relationship between macroscopic coal petrographic types and gas-bearing properties, a gas-bearing property model of the study area is constructed; the gas-bearing property model is used to quantitatively reflect the spatial distribution characteristics of the gas content in the coal reservoir in the study area. This embodiment provides a scientific basis for deeply revealing the internal spatial heterogeneity of the coal reservoir, can significantly improve the reliability of favorable coal reservoir prediction, provides important technical support in key links such as well location optimization and fracturing design, comprehensively and finely depicts the spatial distribution law of the gas content inside the coal reservoir, and is conducive to realizing the prediction of sweet spots in the coal reservoir.

[0132] The coal reservoir geological modeling device based on macroscopic coal petrographic types in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0133] The embodiment of the present invention also provides a computer device having the above Figure 7 shown coal reservoir geological modeling device based on macroscopic coal petrographic types.

[0134] Please refer to Figure 8, A schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. The computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 8 Taking one processor 10 as an example in

[0135] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0136] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0137] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function. The data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include a high-speed random access memory and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0138] The memory 20 can include a volatile memory, such as a random access memory. The memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive. The memory 20 can also include a combination of the above types of memories.

[0139] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0140] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A geological modeling method for coal reservoirs based on macroscopic coal petrological types, characterized in that, Comprising: Preprocessing the logging data and seismic data in the study area to obtain processed logging data and processed seismic data; Based on the macroscopic coal petrographic types observed from core samples and the processed logging data, establishing a classification standard for macroscopic coal petrographic types; and, conducting fine stratigraphic correlation based on the macroscopic coal petrographic types and the processed logging data to obtain a fine stratigraphic framework; Performing stratigraphic modeling and fault modeling according to the fine stratigraphic framework and the processed seismic data to obtain the structural model of the study area; Determining the single-well macroscopic coal petrographic types in the study area based on the classification standard for macroscopic coal petrographic types, determining the distribution characteristics of macroscopic coal petrographic types based on the single-well macroscopic coal petrographic types, and constructing a three-dimensional macroscopic coal petrographic type model of the study area based on the single-well macroscopic coal petrographic types, the distribution characteristics of macroscopic coal petrographic types, and the structural model; Constructing a gas-bearing property model of the study area according to the three-dimensional macroscopic coal petrographic type model and according to the relationship between macroscopic coal petrographic types and gas-bearing properties; the gas-bearing property model is used to quantitatively reflect the spatial distribution characteristics of gas content in the coal reservoirs in the study area.

2. The method according to claim 1, characterized in that The establishing a classification standard for macroscopic coal petrographic types based on the macroscopic coal petrographic types observed from core samples and the processed logging data includes: Making a fine division of macroscopic coal petrographic types based on the core observation data of the study area, and matching the core data with the processed logging data through depth attribution correction; constructing multi-dimensional cross-plot plates of multiple key logging parameters and macroscopic coal petrographic types to determine the processed logging data corresponding to different categories of macroscopic coal petrographic types; using statistical methods to determine the distribution intervals of each macroscopic coal petrographic type in multiple key logging parameters, and determining the critical thresholds for distinguishing different macroscopic coal petrographic types; comprehensively establishing a discrimination function or a machine learning classification model based on multi-parameter characteristics to establish the classification standard for macroscopic coal petrographic types; wherein, the classification standard for macroscopic coal petrographic types includes the logging parameter value intervals corresponding to various macroscopic coal petrographic types, and each logging parameter value interval includes the value intervals of multiple key logging parameters.

3. The method according to claim 1, wherein The performing stratigraphic modeling and fault modeling according to the fine stratigraphic framework and the processed seismic data to obtain the structural model of the study area includes: Determining the model grid size and direction according to the well pattern density and geological body scale of the study area, and establishing a skeleton grid model of the study area according to the model grid size and direction; On the basis of the skeleton grid model, constructing the fault model based on the processed seismic data, and generating the stratigraphic model by means of layer conversion of interpolation of well logging data under the constraint of seismic trend; If there are horizontal wells in the study area, making full use of multi-branch horizontal well data to correct the structural layer of the stratigraphic model by forward gamma logging while drilling to obtain a corrected stratigraphic model, and then determining the corrected stratigraphic model and the fault model as a whole as the structural model; If there are no horizontal wells in the study area, directly determining the fault model and the stratigraphic model as a whole as the structural model.

4. The method according to claim 1, wherein The fine stratigraphic comparison based on the macro coal and rock types and the processed well logging data to obtain a fine stratigraphic framework includes: According to the processed logging data and the combination of macro coal rock types, the changes in water bodies during the coal accumulation period are revealed, the coal seam formation curve is drawn based on the geochemical characteristic parameters, and the sedimentary cycle units and secondary layer interfaces in the study area are identified. Under the control of the cycle, fine stratigraphic comparison is carried out to obtain the fine stratigraphic framework.

5. The method according to claim 1, wherein The determining of the macroscopic coal and rock type of a single well in the study area based on the macroscopic coal and rock type classification standard includes: Based on the macro coal rock type classification standard, macro coal rock classification is performed on the single well in the study area to obtain the macro coal rock type of the single well; The determining of the macroscopic coal and rock type distribution characteristics based on the macroscopic coal and rock type of the single well includes: Statistical analysis was performed on the macroscopic coal and rock types of the single wells, and the thickness values of the macroscopic coal and rock types in different single wells and the proportion of the macroscopic coal and rock types at the same vertical grid depth were calculated to determine the overall distribution characteristics and longitudinal spatial distribution patterns of the macroscopic coal and rock types in the study area at the regional scale, and the overall distribution characteristics of the macroscopic coal and rock types in the study area were used as the overall distribution characteristics.

6. The method according to claim 5, wherein The constructing of a three-dimensional macroscopic coal and rock type model of the study area based on the macroscopic coal and rock type of the single well, the macroscopic coal and rock type distribution characteristics, and the structural model includes: The single well macroscopic coal and rock type is mapped to the geological space defined by the structural model by means of grid coarsening, while retaining the vertical resolution; an initial three-dimensional macroscopic coal and rock type model is generated under the constraints of the structural model by using a geological statistical algorithm in combination with the processed well logging data, the processed seismic data and the distribution characteristics of the macroscopic coal and rock type; the initial three-dimensional macroscopic coal and rock type model is reliability tested by comparing it with actual observation data of newly drilled wells or blind wells and by using a statistical characteristic parameter matching analysis method; and when it is determined that the reliability test passes, the initial three-dimensional macroscopic coal and rock type model is determined as the three-dimensional macroscopic coal and rock type model.

7. The method according to claim 1, characterized in that, Macro coal rock types include: bright coal, semi-bright coal, semi-dark coal and dark coal; based on the three-dimensional macro coal rock type model and the relationship between macro coal rock type and gas content, the gas content model of the study area is constructed, including: Based on core description and gas content test analysis, a first relationship between macroscopic coal rock type and gas content was determined, showing that bright coal and semi-bright coal have higher gas content than semi-dark coal and dark coal. Based on petrophysical analysis, a second relationship between macroscopic coal rock type and gas content was determined, showing that the development of pore and fracture structures in bright coal and semi-bright coal is conducive to gas adsorption. Based on production dynamic data analysis, a third relationship between macroscopic coal rock type and gas content was determined, showing that the productivity of dark coal and semi-dark coal wells is significantly lower than that of bright coal and semi-bright coal wells. Based on the first, second and third relationships between macroscopic coal rock types and gas content, under the constraints of the three-dimensional macroscopic coal rock type model, the gas content data measured on the well is used as hard constraint data, and the gas content model is constructed using a phase-controlled simulation method.

8. A geological modeling method for coal reservoirs based on macroscopic coal petrological types, characterized in that Including: A preprocessing unit for preprocessing well logging data and seismic data in the study area to obtain processed well logging data and processed seismic data; A standard establishment unit for establishing a classification standard for macroscopic coal petrographic types based on the macroscopic coal petrographic types observed from core samples and the processed well logging data; A stratigraphic correlation unit for performing fine stratigraphic correlation based on the macroscopic coal petrographic types and the processed well logging data to obtain a fine stratigraphic framework; A structural modeling unit for performing stratigraphic modeling and fault modeling based on the fine stratigraphic framework and the processed seismic data to obtain a structural model of the study area; A single-well macroscopic coal petrographic type determination unit for determining the single-well macroscopic coal petrographic types in the study area based on the classification standard for macroscopic coal petrographic types; A feature determination unit for determining the distribution characteristics of macroscopic coal petrographic types based on the single-well macroscopic coal petrographic types; A macroscopic coal petrographic type modeling unit for constructing a three-dimensional macroscopic coal petrographic type model of the study area based on the single-well macroscopic coal petrographic types, the distribution characteristics of macroscopic coal petrographic types, and the structural model; A gas-bearing property modeling unit for constructing a gas-bearing property model of the study area according to the three-dimensional macroscopic coal petrographic type model and the relationship between macroscopic coal petrographic types and gas-bearing properties; the gas-bearing property model is used to quantitatively reflect the spatial distribution characteristics of the gas content in the coal reservoirs in the study area.

9. A computer device, characterized in that, Including: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method for geological modeling of coal reservoirs based on macroscopic coal petrographic types according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the method for geological modeling of coal reservoirs based on macroscopic coal petrographic types according to any one of claims 1 to 7.

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