Method and device for comprehensive evaluation and analysis of shale geological conditions based on mathematical model

Abstract

The present invention discloses a method and device for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, belonging to the technical field of shale gas exploration. By reflecting multi-level parameters of shale geological conditions, a hierarchical list construction and an influence list construction are carried out, so that shale gas explorers can use computers to conduct comprehensive exploration of shale; by constructing and providing a mathematical model adapted to the multi-level parameters, a characterization value is obtained, and the shale geological conditions are comprehensively evaluated and analyzed, which is more intelligent; the multi-parameters are hierarchically integrated and influentially integrated to construct a set of methods for comprehensively evaluating and analyzing shale geological conditions based on multi-level parameters, which avoids the need to select multiple different models for evaluation and analysis for different parameters in the prior art, avoids the tediousness of selecting a calculation model, reduces the workload of exploration and testing personnel, and facilitates unified digital comprehensive analysis of shale geological conditions.

Description

Technical Field

[0001] The present application relates to the technical field of shale gas exploration, and in particular to a method, device, equipment and storage medium for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model. Background Art

[0002] Gas content is an important indicator of shale gas geological evaluation, which directly affects the evaluation of whether the shale gas block has industrial mining value. The occurrence state of shale gas includes three parts: free, adsorbed and dissolved. There are many factors affecting gas content, including shale factors (such as shale organic matter type, TOC, thermal evolution degree, fractures, porosity, mineral composition, thickness, etc.) and external factors (such as shale burial depth, temperature and pressure, humidity, etc.). At present, the method of experimental determination of adsorbed gas and (or) total gas content is usually used to evaluate the gas content of shale at home and abroad. However, the use of experimental methods to determine the gas content of shale has the following problems: ① Adsorbed gas is an important component of shale, but it cannot fully represent the quality of shale gas content; ② The experimental research cost is high and the cycle is long; ③ To carry out evaluation within the basin, a certain number of experimental samples with relatively uniform distribution over a large area are required, which is limited in many aspects and is not conducive to the evaluation of shale gas content. Although there are many methods to characterize gas content, the evaluation indicators and standards for shale gas content are still being explored, and there is currently no method for evaluating the gas content of shale horizontal wells.

[0003] This application designs a mathematical model that is more suitable for comprehensive evaluation and analysis of shale geological conditions based on the levels of multi-level parameters and the influence of the parameters on the comprehensive evaluation and analysis of shale geological conditions. The multi-level parameters are comprehensively evaluated through the mathematical model to solve the problem that the conditions targeted in the comprehensive evaluation and analysis of shale geological conditions in the prior art are not comprehensive enough and are relatively lacking. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a method, device, equipment and storage medium for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model to solve the problems existing in the prior art.

[0005] In order to solve the above technical problems, the present application provides a method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model, which adopts the following technical solutions: including:

[0006] Based on human-computer interaction, obtain multi-level parameters and the correlation between various parameters for evaluating and analyzing shale geological conditions;

[0007] Constructing a multi-level list of the multi-level parameters according to the correlation between the parameters, and storing the multi-level list in a preset parameter database;

[0008] Based on human-computer interaction, the measured values ​​corresponding to the multi-level parameters are obtained, and the measured values ​​are transferred into the preset parameter database, and a one-to-one correspondence relationship is established with each list in the parameter database;

[0009] A mathematical model is constructed, and the measured values ​​of the elements in the multi-level list corresponding to the elements in the multi-level list are input into the mathematical model as input parameters, and an output result is obtained as a characterization value for comprehensive evaluation and analysis of shale geological conditions.

[0010] Furthermore, the multi-level parameters and the correlation between the parameters used to evaluate and analyze shale geological conditions based on human-computer interaction include:

[0011] Provide an operation interface for researchers to input parameters and correlations;

[0012] A heartbeat reporting mechanism is adopted to obtain the multi-level parameter information and correlation information between the parameters input by the researchers for evaluating and analyzing shale geological conditions, and to report the parameter information and correlation information to the list construction module respectively.

[0013] Furthermore, the multi-level parameters used to evaluate and analyze shale geological conditions include at least:

[0014] Primary parameters, secondary parameters and tertiary parameters,

[0015] The first-level parameters include at least: the sedimentary type, structural style, source rock characteristics, brittle mineral content, reservoir characteristics and preservation conditions of the shale;

[0016] The secondary parameters include at least: the lithology of the shale, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type, graptolite zone development, felsic mineral content, carbonate mineral content, porosity, permeability, fault conditions, roof and floor conditions, self-sealing, fracture development and burial depth;

[0017] The three-level parameters include at least: organic pores, inorganic pores, microcracks, fault properties, fault scale, fault development, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, porosity and adsorption performance of the shale.

[0018] Furthermore, the correlation between the various parameters is specifically as follows:

[0019] The source rock characteristics in the first-level parameters include the lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type and graptolite zone development in the second-level parameters;

[0020] The brittle mineral content in the primary parameter includes the felsic mineral content and carbonate mineral content in the secondary parameter;

[0021] The reservoir characteristics in the primary parameters include porosity and permeability in the secondary parameters;

[0022] The preservation conditions in the first-level parameters include the fault conditions, roof and floor conditions, self-sealing, fracture development and burial depth in the second-level parameters;

[0023] The porosity in the secondary parameter includes the organic pores, inorganic pores and microcracks in the tertiary parameter;

[0024] The fault conditions in the second-level parameters include the fault properties, fault scale and fault development in the third-level parameters;

[0025] The roof and floor conditions in the secondary parameters include the roof and floor lithology, roof and floor thickness and roof and floor conductivity in the third-level parameters;

[0026] The self-sealing property in the secondary parameter includes the thickness, porosity and adsorption performance in the tertiary parameter.

[0027] Furthermore, the multi-level parameters are constructed into a multi-level list according to the correlation between the parameters, specifically:

[0028] Use the first-level parameters as the elements in the first-level list to construct a first-level list;

[0029] The lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type and graptolite zone development in the secondary parameters are used as elements in the first and second level lists to construct the first and second level lists;

[0030] The content of felsic minerals and the content of carbonate minerals in the secondary parameters are used as elements in the second secondary list to construct a second secondary list;

[0031] The porosity and permeability in the secondary parameters are used as elements in the third secondary list to construct the third secondary list;

[0032] The fault conditions, roof and floor conditions, self-sealing, fracture development and burial depth in the second-level parameters are used as elements in the fourth second-level list to construct the fourth second-level list;

[0033] The organic pores, inorganic pores and microcracks in the three-level parameters are used as elements in the first three-level list to construct the first three-level list;

[0034] The fault properties, fault scale and fault development conditions in the three-level parameters are used as the elements in the second and third-level lists to construct the second and third-level lists;

[0035] The top and bottom plate lithology, top and bottom plate thickness and top and bottom plate conductivity in the three-level parameters are used as elements in the third-level list to construct the third-level list;

[0036] The thickness, porosity and adsorption performance of the three-level parameters are used as elements in the fourth three-level list to construct the fourth three-level list.

[0037] Furthermore, constructing a multi-level list of the multi-level parameters according to the correlation between the parameters further includes:

[0038] Based on the influence of each multi-level parameter on the evaluation and analysis of shale geological conditions, an influence list is constructed, and the influence list includes: a positive correlation evaluation list, a negative correlation evaluation list and an intermediate evaluation list.

[0039] Among them, the greater the measured value corresponding to the element in the positive correlation evaluation list is than the preset first threshold, the better the shale geological conditions are; the smaller the measured value corresponding to the element in the negative correlation evaluation list is than the preset second threshold, the better the shale geological conditions are; the closer the measured value corresponding to the element in the intermediate evaluation list is to the preset third threshold, the better the shale geological conditions are.

[0040] Among them, the elements in the positive correlation evaluation list include brittle mineral content, reservoir characteristics, preservation conditions, effective shale thickness, organic matter abundance, graptolite zone development, felsic mineral content, porosity, permeability, roof and floor conditions, self-sealing, burial depth, organic pores, inorganic pores, microcracks, roof and floor lithology, roof and floor thickness, thickness, pore roar and adsorption performance;

[0041] The elements in the negative correlation evaluation list include the development of cracks and the conductivity of the top and bottom plates;

[0042] The elements in the intermediate evaluation list include sedimentary type, structural style, source rock characteristics, lithofacies, degree of thermal evolution, organic matter type, carbonate mineral content, fault conditions, fault properties and fault scale.

[0043] Furthermore, the measured values ​​corresponding to the multi-level parameters include:

[0044] The mathematical type value used to represent the multi-level parameter obtained based on the existing measurement method shall at least include:

[0045] Mathematical type values ​​corresponding to the primary parameters without corresponding secondary parameters, wherein the primary parameters of the shale without corresponding secondary parameters include sedimentary type and structural style;

[0046] Mathematical type values ​​corresponding to the secondary parameters without corresponding third-level parameters, wherein the secondary parameters of the shale without corresponding third-level parameters include lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type, graptolite zone development, felsic mineral content, carbonate mineral content, permeability, fracture development and mathematical type values ​​corresponding to burial depth;

[0047] The three-level parameters of the shale include organic pores, inorganic pores, microcracks, fault properties, fault scale, fault development, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, porosity and adsorption properties.

[0048] Furthermore, the construction of the mathematical model specifically includes:

[0049] Based on the preset first algorithm formula: A shale sample screening model is constructed, wherein the shale sample screening model is used to screen out suitable shale samples, wherein z represents the number of shale samples to be analyzed, x represents the number of samples to be analyzed, 1 to x z Indicates the measured values ​​corresponding to any parameter of the same type in each of the z multi-level parameters of the shale to be analyzed;

[0050] Based on the preset second algorithm formula: A shale positive correlation characterization model is constructed, wherein the shale positive correlation characterization model is used to obtain a first characterization value, wherein x represents the measured value of any element in the positive correlation evaluation list corresponding to any of the shale samples to be analyzed, and x l Indicates the preset first threshold, x b to x a , that is [x b , x a ] represents the allowable error value range between the measured value of any element in the positive correlation evaluation list and the preset first threshold value;

[0051] Based on the preset third algorithm formula: A shale negative correlation characterization model is constructed, wherein the shale negative correlation characterization model is used to obtain a second characterization value, wherein x represents the measured value of any element in the negative correlation evaluation list corresponding to any of the shale samples to be analyzed, and x m Indicates the preset second threshold, x c to x d , that is [x c , x d] represents the allowable error value range between the measured value of any element in the negative correlation evaluation list and the preset second threshold value;

[0052] Based on the preset fourth algorithm formula: Constructing a shale intermediate characterization model, wherein the shale intermediate characterization model is used to obtain a third characterization value, wherein x represents the measured value of any element in the intermediate evaluation list corresponding to any of the shale samples to be analyzed, and x n Indicates the preset third threshold, x e to x f ,Right now Indicates the allowable error range between the measured value of any element in the intermediate evaluation list and the preset third threshold value;

[0053] Based on the preset fifth algorithm formula: A comprehensive shale evaluation and analysis model is constructed, and the comprehensive shale evaluation and analysis model is used to obtain a final characterization value based on the first characterization value, the second characterization value and the third characterization value, wherein z represents the number of shale samples to be analyzed, i represents the number of elements in the positive correlation evaluation list corresponding to any of the shale samples to be analyzed, j represents the number of elements in the negative correlation evaluation list corresponding to any of the shale samples to be analyzed, and k represents the number of elements in the intermediate evaluation list corresponding to any of the shale samples to be analyzed.

[0054] Furthermore, the measured values ​​of the elements in the multi-level list corresponding to the elements in the multi-level list are input into the mathematical model as input parameters, and the output results are obtained as the characterization values ​​for comprehensive evaluation and analysis of shale geological conditions. The specific steps are as follows:

[0055] Step 301-1, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a third-level parameter;

[0056] Step 302-1, if it is a third-level parameter, obtain a third-level list corresponding to the third-level parameter, and simultaneously obtain all elements in the third-level list and the measured values ​​corresponding to all the elements;

[0057] Step 303, transferring the measured values ​​corresponding to all the elements to the shale sample screening model to screen out suitable shale samples;

[0058] Step 304, after selecting suitable shale samples based on the shale sample screening model, identifying an influence list corresponding to the elements input into the mathematical model;

[0059] Step 305-1, if the influence list corresponding to the element in the mathematical model is a positive correlation evaluation list, the element is transferred to the shale positive correlation characterization model to obtain a first characterization value;

[0060] Step 305-2: if the influence list corresponding to the element in the mathematical model is a negative correlation evaluation list, the element is transferred to the shale negative correlation characterization model to obtain a second characterization value;

[0061] Step 305-3: if the influence list corresponding to the element in the mathematical model is an intermediate evaluation list, the element is transferred to the shale intermediate characterization model to obtain a third characterization value;

[0062] Step 306, after obtaining the first characterization value, the second characterization value and the third characterization value, the first characterization value, the second characterization value and the third characterization value are transmitted to the shale comprehensive evaluation and analysis model, and a final characterization value is obtained as a mathematical type value of the secondary parameter corresponding to all elements in the three-level list;

[0063] Step 301-2, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a second-level parameter without a corresponding third-level parameter;

[0064] Step 302-2, if it is a level 2 parameter without a corresponding level 3 parameter, then obtain a level 2 list corresponding to the level 2 parameter without a corresponding level 3 parameter, and simultaneously obtain all elements in the level 2 list and the measured values ​​corresponding to all elements;

[0065] Step 307, passing all elements in the secondary list obtained in step 302-2 and the measured values ​​corresponding to all the elements into the mathematical model, and repeatedly executing steps 303 to 306 to obtain the final characterization value as the mathematical type value of the primary parameter corresponding to all the elements in the secondary list;

[0066] Step 301-3, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a primary parameter without a corresponding secondary parameter;

[0067] Step 302-3, if it is a primary parameter without a corresponding secondary parameter, obtain a primary list corresponding to the primary parameter without a corresponding secondary parameter, and simultaneously obtain all elements in the primary list and the measured values ​​corresponding to all the elements;

[0068] Step 308, all the elements in the first-level list obtained in step 302-3 and the measured values ​​corresponding to all the elements are passed into the mathematical model, and steps 303 to 306 are repeated to obtain the final characterization value as the mathematical type value corresponding to all the elements in the first-level list, that is, the characterization value for comprehensive evaluation and analysis of the shale geological conditions.

[0069] Furthermore, when the shale geological conditions are comprehensively evaluated and analyzed through the mathematical model, if a secondary parameter without a corresponding third-level parameter or a first-level parameter without a corresponding second-level parameter is preferentially obtained, and the mathematical measurement value corresponding to the secondary parameter with the corresponding third-level parameter or the first-level parameter with the corresponding second-level parameter has not been determined through the mathematical model or has not been input through human-computer interaction, then step 307 or step 308 is terminated first, and the program waits until the mathematical measurement value corresponding to the secondary parameter with the corresponding third-level parameter or the first-level parameter with the corresponding second-level parameter is determined through the mathematical model or input through human-computer interaction, and step 307 or step 308 is executed again, wherein the secondary parameter with the corresponding third-level parameter includes: porosity, fault condition, roof and floor condition and self-sealing property, and the first-level parameter with the corresponding secondary parameter includes: source rock characteristics, brittle mineral content, reservoir characteristics and preservation conditions.

[0070] In order to solve the above technical problems, the embodiment of the present application further provides a device for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, including:

[0071] A parameter input module is used to obtain multi-level parameters and correlations between parameters for evaluating and analyzing shale geological conditions based on human-computer interaction; and obtain measured values ​​corresponding to the multi-level parameters based on human-computer interaction;

[0072] A database construction module is used to construct a multi-level list of the multi-level parameters according to the correlation between the various parameters, and store the multi-level list in a preset parameter database; transfer the measured value to the preset parameter database, and establish a one-to-one correspondence with each list in the parameter database;

[0073] The mathematical model construction and evaluation and analysis module is used to construct a mathematical model, and input the measured values ​​of the elements in the multi-level list and the elements in the multi-level list in a one-to-one correspondence into the mathematical model as input parameters, and obtain the output result as a characterization value for comprehensive evaluation and analysis of shale geological conditions.

[0074] In order to solve the above technical problems, an embodiment of the present application also provides a computer device, which adopts the technical solution described below: it includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of a method for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model proposed in an embodiment of the present application when executing the computer program.

[0075] In order to solve the above technical problems, an embodiment of the present application also provides a non-volatile computer-readable storage medium, which adopts the following technical solution: a computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the steps of a method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model proposed in an embodiment of the present application are implemented.

[0076] Compared with the prior art, this application has the following beneficial effects:

[0077] The embodiments of the present application disclose a method, device, equipment and storage medium for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model. By constructing a hierarchical list and an influence list based on multi-level parameters reflecting the shale geological conditions, it is convenient for shale gas explorers to conduct comprehensive exploration of shale in combination with computers. By constructing and providing a mathematical model compatible with the multi-level parameters, characterization values ​​are obtained to conduct comprehensive evaluation and analysis of shale geological conditions, which is more intelligent and reduces the manual calculation amount of shale gas explorers to a certain extent. Multi-parameters are hierarchically integrated and influentially integrated to construct a method for comprehensive evaluation and analysis of shale geological conditions based on multi-level parameters, avoiding the need to select multiple different models for evaluation and analysis for different parameters in the prior art, avoiding the cumbersomeness of the calculation model and reducing the workload of explorers, and facilitating a unified digital comprehensive analysis of shale geological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0079] Figure 2 This is a flow chart of an embodiment of the method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model described in the embodiments of the present application;

[0080] Figure 3 , Figure 4 and Figure 5 This is a flowchart of an embodiment of the mathematical model performing program execution after input parameters are passed in the embodiment of the present application;

[0081] Figure 6a and Figure 6b A logical schematic diagram of an embodiment of a method for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model described in an embodiment of the present application;

[0082] Figure 7 This is a schematic structural diagram of an embodiment of a device for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model as described in the embodiments of the present application;

[0083] Figure 8 It is a structural diagram of an embodiment of a computer device in the embodiments of the present application. DETAILED DESCRIPTION

[0084] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0085] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0086] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0087] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0088] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0089] It should be noted that the method for comprehensive evaluation and analysis of shale geological conditions based on mathematical models provided in the embodiments of the present application is generally executed by a server / terminal device, and accordingly, the device for comprehensive evaluation and analysis of shale geological conditions based on mathematical models is generally set in the server / terminal device.

[0090] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0091] Continue to refer Figure 2 , the figure shows a flow chart of an embodiment of the method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model of the present application, the method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model comprises the following steps:

[0092] Step 201 : obtaining multi-level parameters and correlations between parameters for evaluating and analyzing shale geological conditions based on human-computer interaction.

[0093] In an embodiment of the present application, the method of obtaining multi-level parameters for evaluating and analyzing shale geological conditions and the correlation between each parameter based on human-computer interaction includes: providing an operation interface for researchers to input parameters and correlations; using a heartbeat reporting mechanism to obtain the multi-level parameter information for evaluating and analyzing shale geological conditions and the correlation information between each parameter input by the researchers, and reporting the parameter information and correlation information to a list construction module respectively.

[0094] In an embodiment of the present application, the multi-level parameters for evaluating and analyzing shale geological conditions include at least: primary parameters, secondary parameters and tertiary parameters, wherein the primary parameters include at least: the sedimentary type, structural style, source rock characteristics, brittle mineral content, reservoir characteristics and preservation conditions of the shale; the secondary parameters include at least: the lithology, effective mudstone thickness, organic matter abundance, thermal evolution degree, organic matter type, graptolite belt development, felsic mineral content, carbonate mineral content, porosity, permeability, fault conditions, roof and floor conditions, self-sealing, fracture development and burial depth of the shale; the tertiary parameters include at least: organic pores, inorganic pores, microcracks, fault properties, fault scale, fault development, roof and floor lithology, roof and floor thickness, roof and floor conductivity, thickness, porosity and adsorption performance of the shale.

[0095] In the embodiment of the present application, the correlation between the various parameters is specifically as follows: the source rock characteristics in the first-level parameters include the lithology, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type and graptolite belt development in the second-level parameters; the brittle mineral content in the first-level parameters includes the felsic mineral content and carbonate mineral content in the second-level parameters; the reservoir characteristics in the first-level parameters include the porosity and permeability in the second-level parameters; the preservation conditions in the first-level parameters include the fault conditions, top and bottom plate conditions, self-sealing, fracture development and burial depth in the second-level parameters; the porosity in the second-level parameters includes the organic pores, inorganic pores and microcracks in the third-level parameters; the fault conditions in the second-level parameters include the fault properties, fault scale and fault development in the third-level parameters; the top and bottom plate conditions in the second-level parameters include the top and bottom plate lithology, top and bottom plate thickness and top and bottom plate conductivity in the third-level parameters; the self-sealing in the second-level parameters includes the thickness, porosity and adsorption performance in the third-level parameters.

[0096] Step 202: construct a multi-level list of the multi-level parameters according to the correlation between the various parameters, and store the multi-level list in a preset parameter database.

[0097] In the embodiment of the present application, the multi-level parameters are constructed into a multi-level list according to the correlation between the parameters, specifically: the first-level parameters are used as the elements in the first-level list to construct the first-level list; the lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type and graptolite belt development in the second-level parameters are used as the elements in the first and second-level lists to construct the first and second-level lists; the felsic mineral content and carbonate mineral content in the second-level parameters are used as the elements in the second and second-level lists to construct the second and second-level lists; the porosity and permeability in the second-level parameters are used as the elements in the third and second-level lists to construct the third and second-level lists; the faults in the second-level parameters are used as the elements in the third and second-level lists to construct the third and second-level lists. The fourth-level list is constructed by taking the organic pores, inorganic pores and microcracks in the third-level parameters as the elements in the first-level list to construct the first-level list; the fault properties, fault scale and fault development in the third-level parameters are taken as the elements in the second-level list to construct the second-level list; the top and bottom plate lithology, top and bottom plate thickness and top and bottom plate conductivity in the third-level parameters are taken as the elements in the third-level list to construct the third-level list; the thickness, porosity and adsorption performance in the third-level parameters are taken as the elements in the fourth-level list to construct the fourth-level list.

[0098] In an embodiment of the present application, the multi-level parameters are constructed into a multi-level list according to the correlation between the parameters, and further includes: constructing an influence list based on the influence of each multi-level parameter on the evaluation and analysis of shale geological conditions, wherein the influence list includes: a positive correlation evaluation list, a negative correlation evaluation list and an intermediate evaluation list, wherein the greater the measured value corresponding to the element in the positive correlation evaluation list is than the preset first threshold, the better the shale geological conditions are; the smaller the measured value corresponding to the element in the negative correlation evaluation list is than the preset second threshold, the better the shale geological conditions are; the closer the measured value corresponding to the element in the intermediate evaluation list is to the preset third threshold, the better the shale geological conditions are. The better the geological conditions, the better the geological conditions. The elements in the positive correlation evaluation list include brittle mineral content, reservoir characteristics, preservation conditions, effective shale thickness, organic matter abundance, graptolite belt development, felsic mineral content, porosity, permeability, roof and floor conditions, self-sealing, burial depth, organic pores, inorganic pores, microcracks, roof and floor lithology, roof and floor thickness, thickness, porosity and adsorption performance; the elements in the negative correlation evaluation list include fracture development and roof and floor conductivity; the elements in the intermediate evaluation list include sedimentary type, structural style, source rock characteristics, lithofacies, thermal evolution degree, organic matter type, carbonate mineral content, fault conditions, fault properties and fault scale.

[0099] Step 203, based on human-computer interaction, obtain the measured values ​​corresponding to the multi-level parameters respectively, and transfer the measured values ​​to the preset parameter database, and establish a one-to-one correspondence with each list in the parameter database.

[0100] In an embodiment of the present application, the measured values ​​corresponding to the multi-level parameters include: mathematical type values ​​obtained based on the existing measurement method for representing the multi-level parameters, and the mathematical type values ​​obtained based on the existing measurement method for representing the multi-level parameters should at least include: mathematical type values ​​corresponding to the primary parameters without corresponding secondary parameters, and the primary parameters of the shale without corresponding secondary parameters include sedimentary type and structural style; mathematical type values ​​corresponding to the secondary parameters without corresponding tertiary parameters, and the secondary parameters of the shale without corresponding tertiary parameters include lithology, effective mudstone thickness, organic matter abundance, thermal evolution degree, organic matter type, graptolite belt development, felsic mineral content, carbonate mineral content, permeability, fracture development and burial depth; mathematical type values ​​corresponding to organic pores, inorganic pores, microcracks, fault properties, fault scale, fault development, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, porosity and adsorption performance in the tertiary parameters of the shale.

[0101] Step 204, construct a mathematical model, and input the measured values ​​of the elements in the multi-level list and the elements in the multi-level list in one-to-one correspondence into the mathematical model as input parameters, and obtain output results as characterization values ​​for comprehensive evaluation and analysis of shale geological conditions.

[0102] In the embodiment of the present application, the construction of the mathematical model specifically includes:

[0103] Based on the preset first algorithm formula: A shale sample screening model is constructed, wherein the shale sample screening model is used to screen out suitable shale samples, wherein z represents the number of shale samples to be analyzed, x represents the number of samples to be analyzed, 1 to x z Indicates the measured values ​​corresponding to any parameter of the same type in each of the z multi-level parameters of the shale to be analyzed;

[0104] Based on the preset second algorithm formula: A shale positive correlation characterization model is constructed, wherein the shale positive correlation characterization model is used to obtain a first characterization value, wherein x represents the measured value of any element in the positive correlation evaluation list corresponding to any of the shale samples to be analyzed, and x l Indicates the preset first threshold, x b to x a , that is [x b , x a ] represents the allowable error value range between the measured value of any element in the positive correlation evaluation list and the preset first threshold value;

[0105] Based on the preset third algorithm formula: A shale negative correlation characterization model is constructed, wherein the shale negative correlation characterization model is used to obtain a second characterization value, wherein x represents the measured value of any element in the negative correlation evaluation list corresponding to any of the shale samples to be analyzed, and x m Indicates the preset second threshold, x c to x d , that is, [x c , x d ] represents the allowable error value range between the measured value of any element in the negative correlation evaluation list and the preset second threshold value;

[0106] Based on the preset fourth algorithm formula: Constructing a shale intermediate characterization model, wherein the shale intermediate characterization model is used to obtain a third characterization value, wherein x represents the measured value of any element in the intermediate evaluation list corresponding to any of the shale samples to be analyzed, and x n Indicates the preset third threshold, x e to x f ,Right now Indicates the allowable error range between the measured value of any element in the intermediate evaluation list and the preset third threshold value;

[0107] Based on the preset fifth algorithm formula: A comprehensive shale evaluation and analysis model is constructed, and the comprehensive shale evaluation and analysis model is used to obtain a final characterization value based on the first characterization value, the second characterization value and the third characterization value, wherein z represents the number of shale samples to be analyzed, i represents the number of elements in the positive correlation evaluation list corresponding to any of the shale samples to be analyzed, j represents the number of elements in the negative correlation evaluation list corresponding to any of the shale samples to be analyzed, and k represents the number of elements in the intermediate evaluation list corresponding to any of the shale samples to be analyzed.

[0108] Explanation: In specific use, assuming that the geological conditions (gas-bearing properties) of shale within a certain (A) geographical range are measured, first, multiple shale samples within the (A) geographical range are selected, and the values ​​of the tertiary parameters, secondary parameters without corresponding tertiary parameters, and primary parameters without corresponding secondary parameters corresponding to the shale samples are measured, and reference thresholds are set for each tertiary parameter, secondary parameter without corresponding tertiary parameter, and primary parameter without corresponding secondary parameter, respectively. Then, the shale sample screening model is used to screen out suitable samples, and the tertiary parameters, secondary parameters with corresponding tertiary parameters, and primary parameters with corresponding secondary parameters in the suitable samples are measured. The primary parameters are mathematically measured by the shale positive correlation evaluation model, the shale negative correlation evaluation model and the shale intermediate evaluation model, and then the characterization value for comprehensive evaluation and analysis of the shale geological conditions is obtained by the shale sample comprehensive evaluation and analysis model, and the characterization value is used as a reference value. Secondly, after the reference value is determined, when a new shale sample is measured, it is only necessary to compare the characterization value of the new shale sample measured by this method with the reference value, and determine the geological conditions corresponding to the new shale sample according to the comparison result, and judge the gas storage or gas containing capacity of the shale in area A through the geological conditions.

[0109] In addition, in an embodiment of the present application, it may also include: taking the final characterization value obtained based on the first characterization value, the second characterization value and the third characterization value as a reference value, that is, a characterization threshold, taking a new shale sample as a test sample, and obtaining a new characterization value through steps 201 to 204. If the new characterization value is greater than the characterization threshold, then in terms of gas storage performance, the geological conditions of the new shale sample are better than the geological conditions of the shale sample corresponding to the characterization threshold; conversely, in terms of gas storage performance, the geological conditions of the new shale sample are worse than the geological conditions of the shale sample corresponding to the characterization threshold; if the new characterization value is equal to the characterization threshold, then in terms of gas storage performance, the geological conditions of the new shale sample are basically the same as the geological conditions of the shale sample corresponding to the characterization threshold.

[0110] In the embodiments of this application, specific reference is made to Figure 3 , Figure 4 and Figure 5 , Figure 3 , Figure 4 and Figure 5 The flowchart of an embodiment of the present application is jointly shown, in which the mathematical model performs program execution after the input parameters are passed in, and the measured values ​​of the elements in the multi-level list and the elements in the multi-level list corresponding to each other are input into the mathematical model as input parameters, and the output result is obtained as a characterization value for comprehensive evaluation and analysis of shale geological conditions, which specifically includes the steps of:

[0111] Step 301-1, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a third-level parameter;

[0112] Step 302-1, if it is a third-level parameter, obtain a third-level list corresponding to the third-level parameter, and simultaneously obtain all elements in the third-level list and the measured values ​​corresponding to all the elements;

[0113] Step 303, transferring the measured values ​​corresponding to all the elements to the shale sample screening model to screen out suitable shale samples;

[0114] Step 304, after selecting suitable shale samples based on the shale sample screening model, identifying an influence list corresponding to the elements input into the mathematical model;

[0115] Step 305-1, if the influence list corresponding to the element in the mathematical model is a positive correlation evaluation list, the element is transferred to the shale positive correlation characterization model to obtain a first characterization value;

[0116] Step 305-2: if the influence list corresponding to the element in the mathematical model is a negative correlation evaluation list, the element is transferred to the shale negative correlation characterization model to obtain a second characterization value;

[0117] Step 305-3: if the influence list corresponding to the element in the mathematical model is an intermediate evaluation list, the element is transferred to the shale intermediate characterization model to obtain a third characterization value;

[0118] Step 306, after obtaining the first characterization value, the second characterization value and the third characterization value, the first characterization value, the second characterization value and the third characterization value are transmitted to the shale comprehensive evaluation and analysis model, and a final characterization value is obtained as a mathematical type value of the secondary parameter corresponding to all elements in the three-level list;

[0119] Step 301-2, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a second-level parameter without a corresponding third-level parameter;

[0120] Step 302-2, if it is a level 2 parameter without a corresponding level 3 parameter, then obtain a level 2 list corresponding to the level 2 parameter without a corresponding level 3 parameter, and simultaneously obtain all elements in the level 2 list and the measured values ​​corresponding to all elements;

[0121] Step 307, passing all elements in the secondary list obtained in step 302-2 and the measured values ​​corresponding to all the elements into the mathematical model, and repeatedly executing steps 303 to 306 to obtain the final characterization value as the mathematical type value of the primary parameter corresponding to all the elements in the secondary list;

[0122] Step 301-3, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a primary parameter without a corresponding secondary parameter;

[0123] Step 302-3, if it is a primary parameter without a corresponding secondary parameter, obtain a primary list corresponding to the primary parameter without a corresponding secondary parameter, and simultaneously obtain all elements in the primary list and the measured values ​​corresponding to all the elements;

[0124] Step 308, all the elements in the first-level list obtained in step 302-3 and the measured values ​​corresponding to all the elements are passed into the mathematical model, and steps 303 to 306 are repeated to obtain the final characterization value as the mathematical type value corresponding to all the elements in the first-level list, that is, the characterization value for comprehensive evaluation and analysis of the shale geological conditions.

[0125] In an embodiment of the present application, when the shale geological conditions are comprehensively evaluated and analyzed through the mathematical model, if a secondary parameter without a corresponding third-level parameter or a first-level parameter without a corresponding second-level parameter is preferentially obtained, and the mathematical measurement value corresponding to the secondary parameter with the corresponding third-level parameter or the first-level parameter with the corresponding second-level parameter has not been determined through the mathematical model or has not been input through human-computer interaction, then step 307 or step 308 is terminated first, and the program waits until the mathematical measurement value corresponding to the secondary parameter with the corresponding third-level parameter or the first-level parameter with the corresponding second-level parameter is determined through the mathematical model or input through human-computer interaction, and step 307 or step 308 is executed again, wherein the secondary parameter with the corresponding third-level parameter includes: porosity, fault condition, roof and floor condition and self-sealing property, and the first-level parameter with the corresponding secondary parameter includes: source rock characteristics, brittle mineral content, reservoir characteristics and preservation conditions.

[0126] Continue to refer Figure 6a and Figure 6b , Figure 6a and Figure 6b The following are a logical diagram of an embodiment of a method for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model described in an embodiment of the present application.

[0127] in, Figure 6a The execution logic diagram shown in the figure shows that: the first parameter, the second parameter and the third parameter input by the user through human-computer interaction are obtained, and a hierarchical list is constructed for the first parameter, the second parameter and the third parameter based on the correlation input by the user. At the same time, the second parameter without the corresponding third-level parameter, the first parameter without the corresponding second-level parameter, the second parameter with the corresponding third-level parameter and the first parameter with the corresponding second-level parameter are determined. Based on the influence of the first parameter, the second parameter and the third parameter on the evaluation and analysis of shale geological conditions, a classification list is constructed for the first parameter, the second parameter and the third parameter, and a positive correlation evaluation list, a negative correlation evaluation list and a negative correlation evaluation list are constructed. A mathematical model for evaluating and analyzing shale geological conditions is constructed by using a related evaluation list and an intermediate evaluation list, wherein the mathematical model includes: a shale sample screening model, which is used to screen shale samples and select suitable shale samples. For the suitable shale samples, based on the positive correlation evaluation list, the negative correlation evaluation list and the intermediate evaluation list, a shale positive correlation evaluation model, a shale negative correlation evaluation model or a shale intermediate evaluation model is selected for the first-level parameters, the second-level parameters and the third-level parameters to perform measurement value evaluation. Based on the mathematical model, a characterization value for comprehensive evaluation and analysis of the shale geological conditions is finally obtained;

[0128] Figure 6bThe execution logic diagram shown in shows that: based on the multi-level list, it is identified whether the parameter corresponding to the element input into the mathematical model is a third-level parameter, a second-level parameter without a corresponding third-level parameter, or a first-level parameter without a corresponding second-level parameter; if it is a third-level parameter, a second-level parameter without a corresponding third-level parameter, or a first-level parameter without a corresponding second-level parameter, then the third-level list corresponding to the third-level parameter is obtained, and all elements in the third-level list and the measured values ​​corresponding to all elements are obtained at the same time; if it is a second-level parameter without a corresponding third-level parameter, then the second-level list corresponding to the second-level parameter without a corresponding third-level parameter is obtained, and all elements in the second-level list and the measured values ​​corresponding to all elements are obtained at the same time; if it is a first-level parameter without a corresponding second-level parameter parameter, then obtain the first-level list corresponding to the first-level parameter without corresponding second-level parameter, and at the same time obtain all elements in the first-level list and the measured values ​​corresponding to all elements; pass the measured values ​​corresponding to all elements to the shale sample screening model to screen out suitable shale samples; after screening out suitable shale samples based on the shale sample screening model, identify the influence list corresponding to the element input into the mathematical model; if the influence list corresponding to the element in the mathematical model is a positive correlation evaluation list, then pass the element to the shale positive correlation characterization model to obtain the first characterization value; if the influence list corresponding to the element in the mathematical model is a negative correlation evaluation list, then pass the element to the shale negative correlation evaluation model to obtain the first characterization value. the related characterization model is used to obtain the second characterization value; if the influence list corresponding to the element in the mathematical model is an intermediate evaluation list, the element is passed into the shale intermediate characterization model to obtain the third characterization value; after obtaining the first characterization value, the second characterization value and the third characterization value, the first characterization value, the second characterization value and the third characterization value are passed into the shale comprehensive evaluation and analysis model to obtain the final characterization value, which are respectively used as the mathematical type values ​​of the secondary parameters corresponding to all elements in the three-level list, the mathematical type values ​​of the primary parameters corresponding to all elements in the secondary list and the characterization value for comprehensive evaluation and analysis of the shale geological conditions, and the shale geological conditions are comprehensively evaluated and analyzed by the mathematical model. During the evaluation and analysis, if a secondary parameter without a corresponding tertiary parameter or a primary parameter without a corresponding secondary parameter is obtained first, and the mathematical measurement value corresponding to the secondary parameter with the corresponding tertiary parameter or the primary parameter with the corresponding secondary parameter has not been determined through the mathematical model or has not been input through human-computer interaction, the step is terminated first, and the program waits until the mathematical measurement value corresponding to the secondary parameter with the corresponding tertiary parameter or the primary parameter with the corresponding secondary parameter is determined through the mathematical model or input through human-computer interaction, and the step is executed again to obtain the mathematical type values ​​of the primary parameters corresponding to all elements in the secondary list and the characterization values ​​for comprehensive evaluation and analysis of the shale geological conditions.

[0129] The method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model described in the embodiments of the present application can construct a hierarchical list and an influence list through multi-level parameters reflecting the shale geological conditions, so as to facilitate shale gas explorers to conduct comprehensive exploration of shale in combination with computers; by constructing and providing a mathematical model compatible with the multi-level parameters, characterization values ​​are obtained, and the shale geological conditions are comprehensively evaluated and analyzed, which is more intelligent; multi-parameters are hierarchically integrated and influentially integrated to construct a method for comprehensive evaluation and analysis of shale geological conditions based on multi-level parameters, thereby avoiding the need to select multiple different models for evaluation and analysis for different parameters multiple times in the prior art, avoiding the tediousness of selecting calculation models and reducing the workload of exploration and testing personnel, and facilitating a unified digital comprehensive analysis of shale geological conditions.

[0130] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0131] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0132] Further references Figure 7 , as a response to the above Figure 2 The present application provides an embodiment of a device for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0133] like Figure 7As shown, the device 7 for comprehensively evaluating and analyzing shale geological conditions based on mathematical models in this embodiment includes: a parameter input module 701, a database construction module 702, and a mathematical model construction, evaluation and analysis module 703. Among them:

[0134] The parameter input module 701 is used to obtain the multi-level parameters and the correlation between the parameters for evaluating and analyzing the shale geological conditions based on human-computer interaction; and obtain the measured values ​​corresponding to the multi-level parameters based on human-computer interaction;

[0135] The database construction module 702 is used to construct a multi-level list of the multi-level parameters according to the correlation between the parameters, and store the multi-level list in a preset parameter database; transfer the measured value to the preset parameter database, and establish a one-to-one correspondence with each list in the parameter database;

[0136] The mathematical model construction and evaluation and analysis module 703 is used to construct a mathematical model, and input the measured values ​​of the elements in the multi-level list and the elements in the multi-level list in a one-to-one correspondence into the mathematical model as input parameters, and obtain the output result as a characterization value for comprehensive evaluation and analysis of shale geological conditions.

[0137] The device for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model described in the embodiment of the present application constructs a hierarchical list and an influence list by reflecting multi-level parameters of shale geological conditions, so as to facilitate shale gas explorers to conduct comprehensive exploration of shale in combination with computers; by constructing and providing a mathematical model compatible with the multi-level parameters, characterization values ​​are obtained, and the shale geological conditions are comprehensively evaluated and analyzed, which is more intelligent; the multi-parameters are hierarchically integrated and influentially integrated to construct a method for comprehensively evaluating and analyzing shale geological conditions based on multi-level parameters, thereby avoiding the need to select multiple different models for evaluation and analysis for different parameters multiple times in the prior art, avoiding the tediousness of selecting calculation models and reducing the workload of exploration and testing personnel, and facilitating a unified digital comprehensive analysis of shale geological conditions.

[0138] To solve the above technical problems, the present application also provides a computer device, for details, please refer to Figure 8 , Figure 8 This is a basic structural block diagram of the computer device in this embodiment.

[0139] The computer device 8 includes a memory 8a, a processor 8b, and a network interface 8c that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 8 with components 8a-8c, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), digital processors (DSP), embedded devices, etc.

[0140] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0141] The memory 8a includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 8a can be an internal storage unit of the computer device 8, such as a hard disk or memory of the computer device 8. In other embodiments, the memory 8a can also be an external storage device of the computer device 8, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 8. Of course, the memory 8a can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 8a is generally used to store the operating system and various application software installed on the computer device 8, such as the program code of the method for comprehensive evaluation and analysis of shale geological conditions based on mathematical models, etc. In addition, the memory 8a can also be used to temporarily store various types of data that have been output or are to be output.

[0142] The processor 8b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 8b is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is used to run the program code or process data stored in the memory 8a, such as running the program code of the method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model.

[0143] The network interface 8c may include a wireless network interface or a wired network interface. The network interface 8c is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0144] The present application also provides another embodiment, namely, providing a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a program for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model, and the program for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model can be executed by at least one processor, so that the at least one processor performs the steps of the method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model as described above.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

Claims

1. A method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model, characterized in that: The steps include: Based on human-computer interaction, obtain multi-level parameters and the correlation between various parameters for evaluating and analyzing shale geological conditions; Constructing a multi-level list of the multi-level parameters according to the correlation between the parameters, and storing the multi-level list in a preset parameter database; Based on human-computer interaction, the measured values ​​corresponding to the multi-level parameters are obtained, and the measured values ​​are transferred into the preset parameter database, and a one-to-one correspondence relationship is established with each list in the parameter database; Constructing a mathematical model, and inputting the measured values ​​of the elements in the multi-level list corresponding to the elements in the multi-level list into the mathematical model as input parameters, and obtaining output results as characterization values ​​for comprehensive evaluation and analysis of shale geological conditions; The construction of the mathematical model specifically includes: Based on the preset first algorithm formula: A shale sample screening model is constructed, wherein the shale sample screening model is used to screen out suitable shale samples, wherein z represents the number of shale samples to be analyzed, and x1 to x z Indicates the measured values ​​corresponding to any parameter of the same type in each of the z multi-level parameters of the shale to be analyzed; Based on the preset second algorithm formula: A shale positive correlation characterization model is constructed, wherein the shale positive correlation characterization model is used to obtain a first characterization value, wherein x represents the measured value of any element in the positive correlation evaluation list corresponding to any of the shale samples to be analyzed, and x l Indicates the preset first threshold, x b to x a , that is [x b , x a ] represents the allowable error value range between the measured value of any element in the positive correlation evaluation list and the preset first threshold value; Based on the preset third algorithm formula: A shale negative correlation characterization model is constructed, wherein the shale negative correlation characterization model is used to obtain a second characterization value, wherein x represents the measured value of any element in the negative correlation evaluation list corresponding to any of the shale samples to be analyzed, and x m Indicates the preset second threshold, x c to x d , that is [x c , x d ] represents the allowable error value range between the measured value of any element in the negative correlation evaluation list and the preset second threshold value; Based on the preset fourth algorithm formula: Constructing a shale intermediate characterization model, wherein the shale intermediate characterization model is used to obtain a third characterization value, wherein x represents the measured value of any element in the intermediate evaluation list corresponding to any of the shale samples to be analyzed, and x n Indicates the preset third threshold, x e to x f , that is [x e , x f ] represents the allowable error value range between the measured value of any element in the intermediate evaluation list and the preset third threshold value; Based on the preset fifth algorithm formula: A comprehensive shale evaluation and analysis model is constructed, and the comprehensive shale evaluation and analysis model is used to obtain a final characterization value based on the first characterization value, the second characterization value and the third characterization value, wherein z represents the number of shale samples to be analyzed, i represents the number of elements in the positive correlation evaluation list corresponding to any of the shale samples to be analyzed, j represents the number of elements in the negative correlation evaluation list corresponding to any of the shale samples to be analyzed, and k represents the number of elements in the intermediate evaluation list corresponding to any of the shale samples to be analyzed.

2. The method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model according to claim 1, characterized in that: The multi-level parameters used to evaluate and analyze shale geological conditions include at least: Primary parameters, secondary parameters and tertiary parameters, The first-level parameters include at least: the sedimentary type, structural style, source rock characteristics, brittle mineral content, reservoir characteristics and preservation conditions of the shale; The secondary parameters include at least: the lithology of the shale, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type, graptolite zone development, felsic mineral content, carbonate mineral content, porosity, permeability, fault conditions, roof and floor conditions, self-sealing, fracture development and burial depth; The three-level parameters include at least: organic pores, inorganic pores, microcracks, fault properties, fault scale, fault development, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, porosity and adsorption performance of the shale.

3. The method for comprehensive evaluation and analysis of shale geological conditions based on mathematical models according to claim 2, characterized in that: The correlation between the various parameters is specifically: The source rock characteristics in the first-level parameters include the lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type and graptolite zone development in the second-level parameters; The brittle mineral content in the primary parameter includes the felsic mineral content and carbonate mineral content in the secondary parameter; The reservoir characteristics in the primary parameters include porosity and permeability in the secondary parameters; The preservation conditions in the first-level parameters include the fault conditions, roof and floor conditions, self-sealing, fracture development and burial depth in the second-level parameters; The porosity in the secondary parameter includes the organic pores, inorganic pores and microcracks in the tertiary parameter; The fault conditions in the second-level parameters include the fault properties, fault scale and fault development in the third-level parameters; The roof and floor conditions in the secondary parameters include the roof and floor lithology, roof and floor thickness and roof and floor conductivity in the third-level parameters; The self-sealing property in the secondary parameter includes the thickness, porosity and adsorption performance in the tertiary parameter.

4. The method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model according to claim 3, characterized in that: The multi-level parameters are constructed into a multi-level list according to the correlation between the parameters, specifically: Use the first-level parameters as the elements in the first-level list to construct a first-level list; The lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type and graptolite zone development in the secondary parameters are used as elements in the first and second level lists to construct the first and second level lists; The content of felsic minerals and the content of carbonate minerals in the secondary parameters are used as elements in the second secondary list to construct a second secondary list; The porosity and permeability in the secondary parameters are used as elements in the third secondary list to construct the third secondary list; The fault conditions, roof and floor conditions, self-sealing, fracture development and burial depth in the second-level parameters are used as elements in the fourth second-level list to construct the fourth second-level list; The organic pores, inorganic pores and microcracks in the three-level parameters are used as elements in the first three-level list to construct the first three-level list; The fault properties, fault scale and fault development conditions in the three-level parameters are used as the elements in the second and third-level lists to construct the second and third-level lists; The top and bottom plate lithology, top and bottom plate thickness and top and bottom plate conductivity in the three-level parameters are used as elements in the third-level list to construct the third-level list; The thickness, porosity and adsorption performance of the three-level parameters are used as elements in the fourth three-level list to construct the fourth three-level list.

5. The method for comprehensive evaluation and analysis of shale geological conditions based on mathematical models according to claim 1, characterized in that: The step of constructing a multi-level list of the multi-level parameters according to the correlation between the parameters further includes: Based on the influence of each multi-level parameter on the evaluation and analysis of shale geological conditions, an influence list is constructed, and the influence list includes: a positive correlation evaluation list, a negative correlation evaluation list and an intermediate evaluation list. Among them, the greater the measured value corresponding to the element in the positive correlation evaluation list is than the preset first threshold, the better the shale geological conditions are; the smaller the measured value corresponding to the element in the negative correlation evaluation list is than the preset second threshold, the better the shale geological conditions are; the closer the measured value corresponding to the element in the intermediate evaluation list is to the preset third threshold, the better the shale geological conditions are. Among them, the elements in the positive correlation evaluation list include brittle mineral content, reservoir characteristics, preservation conditions, effective shale thickness, organic matter abundance, graptolite zone development, felsic mineral content, porosity, permeability, roof and floor conditions, self-sealing, burial depth, organic pores, inorganic pores, microcracks, roof and floor lithology, roof and floor thickness, thickness, pore roar and adsorption performance; The elements in the negative correlation evaluation list include the development of cracks and the conductivity of the top and bottom plates; The elements in the intermediate evaluation list include sedimentary type, structural style, source rock characteristics, lithofacies, degree of thermal evolution, organic matter type, carbonate mineral content, fault conditions, fault properties and fault scale.

6. The method for comprehensive evaluation and analysis of shale geological conditions based on mathematical models according to claim 2, characterized in that: The measured values ​​corresponding to the multi-level parameters respectively include: The mathematical type value used to represent the multi-level parameter obtained based on the existing measurement method shall at least include: Mathematical type values ​​corresponding to the primary parameters without corresponding secondary parameters, wherein the primary parameters of the shale without corresponding secondary parameters include sedimentary type and structural style; Mathematical type values ​​corresponding to the secondary parameters without corresponding third-level parameters, wherein the secondary parameters of the shale without corresponding third-level parameters include lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type, graptolite zone development, felsic mineral content, carbonate mineral content, permeability, fracture development and mathematical type values ​​corresponding to burial depth; The three-level parameters of the shale include organic pores, inorganic pores, microcracks, fault properties, fault scale, fault development, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, porosity and adsorption properties.

7. The method for comprehensive evaluation and analysis of shale geological conditions based on mathematical models according to claim 1, characterized in that: The measured values ​​of the elements in the multi-level list corresponding to the elements in the multi-level list are input into the mathematical model as input parameters, and the output results are obtained as the characterization values ​​for comprehensive evaluation and analysis of shale geological conditions. The specific steps are as follows: Step 301-1, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a third-level parameter; Step 302-1, if it is a third-level parameter, obtain a third-level list corresponding to the third-level parameter, and simultaneously obtain all elements in the third-level list and the measured values ​​corresponding to all the elements; Step 303, transferring the measured values ​​corresponding to all the elements to the shale sample screening model to screen out suitable shale samples; Step 304, after selecting suitable shale samples based on the shale sample screening model, identifying an influence list corresponding to the elements input into the mathematical model; Step 305-1, if the influence list corresponding to the element in the mathematical model is a positive correlation evaluation list, the element is transferred to the shale positive correlation characterization model to obtain a first characterization value; Step 305-2: if the influence list corresponding to the element in the mathematical model is a negative correlation evaluation list, the element is transferred to the shale negative correlation characterization model to obtain a second characterization value; Step 305-3: if the influence list corresponding to the element in the mathematical model is an intermediate evaluation list, the element is transferred to the shale intermediate characterization model to obtain a third characterization value; Step 306, after obtaining the first characterization value, the second characterization value and the third characterization value, the first characterization value, the second characterization value and the third characterization value are transmitted to the shale comprehensive evaluation and analysis model, and a final characterization value is obtained as a mathematical type value of the secondary parameter corresponding to all elements in the three-level list; Step 301-2, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a second-level parameter without a corresponding third-level parameter; Step 302-2, if it is a level 2 parameter without a corresponding level 3 parameter, then obtain a level 2 list corresponding to the level 2 parameter without a corresponding level 3 parameter, and simultaneously obtain all elements in the level 2 list and the measured values ​​corresponding to all elements; Step 307, passing all elements in the secondary list obtained in step 302-2 and the measured values ​​corresponding to all the elements into the mathematical model, and repeatedly executing steps 303 to 306 to obtain the final characterization value as the mathematical type value of the primary parameter corresponding to all the elements in the secondary list; Step 301-3, based on the multi-level list, identifying whether the parameter corresponding to the element input into the mathematical model is a primary parameter without a corresponding secondary parameter; Step 302-3, if it is a primary parameter without a corresponding secondary parameter, obtain a primary list corresponding to the primary parameter without a corresponding secondary parameter, and simultaneously obtain all elements in the primary list and the measured values ​​corresponding to all the elements; Step 308, all the elements in the first-level list obtained in step 302-3 and the measured values ​​corresponding to all the elements are passed into the mathematical model, and steps 303 to 306 are repeated to obtain the final characterization value as the mathematical type value corresponding to all the elements in the first-level list, that is, the characterization value for comprehensive evaluation and analysis of the shale geological conditions.

8. The method for comprehensive evaluation and analysis of shale geological conditions based on mathematical models according to claim 7, characterized in that: When the shale geological conditions are comprehensively evaluated and analyzed by the mathematical model, if a secondary parameter without a corresponding tertiary parameter or a primary parameter without a corresponding secondary parameter is preferentially obtained, and the mathematical measurement value corresponding to the secondary parameter with a corresponding tertiary parameter or the primary parameter with a corresponding secondary parameter has not been determined by the mathematical model or has not been input through human-computer interaction, then step 307 or step 308 is terminated first, and the program waits until the mathematical measurement value corresponding to the secondary parameter with a corresponding tertiary parameter or the primary parameter with a corresponding secondary parameter is determined by the mathematical model or input through human-computer interaction, and step 307 or step 308 is executed again, wherein the secondary parameter with a corresponding tertiary parameter includes: porosity, fault condition, roof and floor condition and self-sealing property, and the primary parameter with a corresponding secondary parameter includes: source rock characteristics, brittle mineral content, reservoir characteristics and preservation conditions.

9. A device for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, implemented based on the method for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model as described in claim 1, characterized in that: include: Parameter input module, used to obtain multi-level parameters and correlations between parameters for evaluating and analyzing shale geological conditions based on human-computer interaction; Acquire the measured values ​​corresponding to the multi-level parameters respectively based on human-computer interaction; A database construction module is used to construct a multi-level list of the multi-level parameters according to the correlation between the various parameters, and store the multi-level list in a preset parameter database; transfer the measured value to the preset parameter database, and establish a one-to-one correspondence with each list in the parameter database; The mathematical model construction and evaluation and analysis module is used to construct a mathematical model, and input the measured values ​​of the elements in the multi-level list and the elements in the multi-level list in a one-to-one correspondence into the mathematical model as input parameters, and obtain the output result as a characterization value for comprehensive evaluation and analysis of shale geological conditions.

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