Well logging evaluation method suitable for shale gas reservoir geologic model

Abstract

The invention relates to the technical field of logging evaluation of a shale gas reservoir geologic model, and discloses a logging evaluation method suitable for the shale gas reservoir geologic model, which comprises the following steps: collecting and arranging logging data, including natural gamma, resistivity, interval transit time, density and neutron logging curves, based on geological data and logging data of adjacent wells, an initial geological model of the shale gas reservoir is established, a logging curve is preprocessed, including depth correction, environment correction, data smoothing, utilization of response characteristics of the logging curve, identification of cracks and stratification in the shale gas reservoir, and utilization of the logging data to calculate physical property parameters of the shale gas reservoir. According to the logging evaluation method, a plurality of reservoir parameters including the organic carbon content, the thermal maturity, the porosity, the permeability, the water saturation, the fracture development degree and the like can be obtained, and the comprehensive analysis of the parameters can more comprehensively reflect the reservoir characteristics, so that the accuracy of a geologic model is improved.

Description

Technical Field

[0001] The invention relates to the technical field of well logging evaluation of shale gas reservoir geological models, and in particular to a well logging evaluation method suitable for shale gas reservoir geological models. Background Art

[0002] Shale gas reservoir refers to a natural gas reservoir enriched and produced in shale lithology. It has extremely high gas content and rich resource potential. Shale gas reservoirs are mainly shale lithology, and its rock mineral composition is mainly clay minerals and quartz, accompanied by a small amount of feldspar, carbonate minerals and organic matter. Pore structure: The pore structure of shale gas reservoirs is mainly composed of microscopic pores and cracks. Microscopic pores refer to pores with a pore size of less than 0.1 microns, while cracks refer to natural cracks larger than 0.1 mm formed by tectonic action and ground stress. These pores and cracks jointly control the occurrence and migration mechanism of shale gas. Permeability and porosity: Shale gas reservoirs are characterized by low porosity and low permeability. The porosity is usually between 0.1% and 8%, and the permeability is also low, usually below 0.1md.

[0003] Conventional logging methods such as natural gamma, caliper logging, neutron density logging, lithology density logging, acoustic time difference, resistivity logging, etc., although they can obtain some basic physical parameters of the reservoir, have limitations in the evaluation of key parameters such as organic matter content, maturity, and degree of fracture development in shale gas reservoirs, and cannot be accurate. Therefore, a new method is proposed to solve this technical problem. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a logging evaluation method suitable for shale gas reservoir geological models, which has the advantages of improved accuracy and solves the problem of low accuracy of the traditional methods mentioned in the above background technology.

[0006] (II) Technical solution

[0007] In order to achieve the purpose of improving accuracy mentioned in the above background technology, the present invention provides the following technical solution: a well logging evaluation method applicable to a shale gas reservoir geological model, comprising the following steps:

[0008] Step 1: Collect and organize logging data, including natural gamma, resistivity, acoustic time difference, density, and neutron logging curves;

[0009] Step 2: Establish an initial geological model of the shale gas reservoir based on geological data and logging data of adjacent wells;

[0010] Step 3: Preprocess the logging curve, including depth correction, environmental correction, and data smoothing;

[0011] Step 4: Using the response characteristics of the well logging curve, identify the fractures and bedding in the shale gas reservoir;

[0012] Step 5: Calculate the physical property parameters of shale gas reservoir using well logging data;

[0013] Step 6: Compare the calculated reservoir parameters with the initial geological model to verify the accuracy of the model;

[0014] Step 7. Combine the geological model, logging data and reservoir parameters to conduct a comprehensive evaluation of the shale gas reservoir and write a logging evaluation report, recording the evaluation process, results and conclusions in detail.

[0015] Preferably, the data collection and preparation includes data collection and data preparation, specifically:

[0016] Data collection: well logging data collection, geological data collection, and other related data collection;

[0017] Data preparation: data collation and verification, data conversion and formatting, preliminary construction of geological models, data backup and security management.

[0018] Conventional logging data: including natural gamma, resistivity, acoustic time difference, density, neutron and other conventional logging curve data, which are the basis for evaluating the geological characteristics of shale gas reservoirs.

[0019] Special logging data: Collect special logging data such as element capture energy spectrum, dipole acoustic wave, and acoustic-electric imaging as needed. These data can provide more detailed reservoir information, such as mineral composition and fracture development;

[0020] Stratigraphic column: Collect stratigraphic column of the study area to understand basic information such as lithology and thickness of the strata;

[0021] Geological structure map: Collect geological structure maps to understand the geological structure characteristics of the study area, such as folds, faults, etc.;

[0022] Neighboring data: Collect logging data and core analysis data of neighboring wells to provide reference for establishing initial geological models and verifying models;

[0023] Data preparation includes organizing the collected data to ensure the integrity and consistency of the data, converting the logging data into an appropriate format according to the needs of the evaluation method, using the collected geological data and adjacent well data to preliminarily build a geological model of the shale gas reservoir, and backing up the collected data to prevent data loss or damage.

[0024] Preferably, the logging data needs to collect basic geological data of shale gas reservoirs, and based on the collected data, a logging evaluation method suitable for shale gas reservoirs is established. After the logging evaluation method is established and useful geological information is extracted, the initial geological model is constructed. After the initial geological model is established, the model is verified and optimized.

[0025] Basic well information, mud logging data, well logging data and their interpretation results, core data, and fault data intersecting the well trajectory;

[0026] Seismic data: seismic data structural interpretation data and seismic attribute prediction data;

[0027] It also includes a selection logging series: according to the geological characteristics of shale gas reservoirs, select geophysical logging methods that can reflect the characteristics of shale organic matter and gas indications. Conventional logging series include natural gamma, well diameter, neutron density, lithology density, acoustic time difference, and resistivity logging. For special needs, special logging series can also be considered, such as element capture spectrum logging, dipole acoustic logging, and acoustic and electrical imaging logging;

[0028] Data processing and interpretation: Process the logging data, eliminate interference factors, improve data quality, interpret the logging data according to the geological characteristics of shale gas reservoirs, and extract useful geological information;

[0029] Establishing interpretation model: Using well logging data, core data, and mud logging data, an interpretation model of shale gas reservoir parameters is established, which can be used to evaluate key parameters of shale gas reservoirs, such as organic carbon content, organic matter maturity, and effective thickness;

[0030] The establishment of the initial geological model is specifically as follows: establishing a framework geological model, using drilling layer data and seismic interpretation results to establish a geological model that characterizes the geometric morphology of the shale gas reservoir interface, the three-dimensional spatial distribution characteristics and the internal structure;

[0031] Establishing matrix attribute geological model: Based on sample analysis and testing data, well logging data and its interpretation results, and seismic data prediction results, a geological model is established within the framework geological model to characterize the spatial variation characteristics of various continuous or discrete distribution geological attributes within the shale gas reservoir;

[0032] Establish a fracture geological model: Using the interpretation results of natural fractures from well logging and the prediction results of seismic faults and fractures, combined with the constraints of geological models of matrix stress fields, brittle minerals and other properties, establish a geological model that characterizes the spatial distribution of fractures of various causes and their conductivity;

[0033] Fusion and superposition to form a comprehensive geological model: Various geological models are fused and superimposed step by step to form a comprehensive geological model that characterizes the distribution and changes of various geological characteristics, attributes and parameters inside the shale gas reservoir in three-dimensional space.

[0034] Preferably, the logging data is real underground data directly obtained vertically, specifically:

[0035] Step 1: Eliminate abnormal points of the logging curve by corresponding analysis and filtering of the logging curve;

[0036] Step 2: Using logging environment parameters, logging instrument type and model, and wellbore conditions, environmental correction is achieved through the model;

[0037] Step 3: Perform depth correction through correlation comparison to make the logging curves of multiple measurements reach depth uniformity and matching;

[0038] Step 4: When the formation is thin, the amplitude of the logging response is low and its value cannot reflect the true situation of the formation. The logging curve must be corrected for layer thickness;

[0039] Step 5: When the reservoir contains oil and gas, it will affect the natural potential, resistivity, and porosity logging. The influence of oil and gas should be eliminated so that the logging response can reflect the true situation of the formation.

[0040] Step 6: The acoustic wave curve does not match the lithology ideally. Analyze the reasons and eliminate the wellbore factor.

[0041] Preferably, the fracture and bedding identification includes fracture identification and bedding identification, wherein fracture identification includes conventional logging methods, special logging methods and mathematical methods;

[0042] And the conventional logging methods include: dual lateral logging, natural gamma logging, and acoustic time difference logging;

[0043] Special logging methods include: acoustic and electrical imaging logging, dipole acoustic wave logging;

[0044] Mathematical methods include: wavelet multi-scale analysis, BP neural network and grey relation.

[0045] Preferably, the reservoir parameter calculation not only reflects the physical and chemical properties of the reservoir, but also directly affects the subsequent exploration and development strategy, including porosity calculation, permeability calculation, gas saturation calculation, organic carbon content calculation and rock brittleness index calculation.

[0046] Porosity is an important indicator for evaluating reservoir permeability and storage capacity, and can be calculated by using the conventional three-porosity logging method combined with core experimental data for correction, or it can be estimated by dual laterolog response values;

[0047] Permeability estimation: The fitting relationship between porosity and permeability can be analyzed through core experiments, and then the permeability parameters can be estimated using the logging porosity. The nuclear magnetic resonance logging method can also provide relatively accurate permeability parameters.

[0048] Gas saturation calculation includes free gas saturation and adsorbed gas saturation: it can be calculated by conventional logging methods combined with core experimental data, and factors such as formation pressure, formation temperature, total organic carbon content and kerogen type need to be considered. Langmuir isotherm curve data is usually used for calculation;

[0049] The organic carbon content is calculated by using the overlapping relationship between resistivity logging and porosity logging curves and by establishing a regression relationship between radioactive uranium content and total organic carbon content;

[0050] The rock brittleness index is calculated by using array acoustic wave data to obtain the Young's modulus and Poisson's ratio of the rock, and then the rock's brittleness index is calculated. The lower the Poisson's ratio, the more brittle the rock is. When the Young's modulus value increases, the rock will be more brittle. The brittleness index is also calculated through the core mineral measurement results, usually expressed as the ratio of quartz content to total mineral content.

[0051] Preferably, the geological model verification and adjustment is an important step to ensure the accuracy and reliability of the model, which includes the verification of the geological model and the adjustment of the geological model, specifically:

[0052] Verification of geological models includes: Verification based on logging data: using logging curves to compare with geological models to verify whether the rock type, mineral composition, porosity, permeability and other parameters predicted by the model are consistent with the logging data;

[0053] Core testing and comparison: Compare the predictions of the geological model with the core test data;

[0054] Production data verification: Use actual production data from shale gas wells to verify the accuracy of geological models;

[0055] Adjustment of geological model: Adjustment based on verification results: Improve the accuracy and reliability of the model by modifying the model parameter settings, updating geological data, and improving algorithms;

[0056] Iterative optimization: By continuously collecting new logging data, core test data and production data, the model is continuously verified and adjusted to gradually approach the actual geological conditions;

[0057] Multi-data fusion: In the process of adjusting the geological model, data from different sources are integrated to provide more comprehensive geological information support.

[0058] When verifying and adjusting geological models, it is necessary to ensure that the data used is of high quality and consistency. The geological conditions of shale gas reservoirs are usually complex, with problems such as heterogeneity and anisotropy. When verifying and adjusting geological models, the impact of these geological complexities needs to be fully considered to ensure the accuracy and reliability of the model.

[0059] Preferably, the comprehensive evaluation and interpretation, report writing and results presentation include comprehensive evaluation and interpretation, report writing and results presentation, wherein porosity is calculated by density logging, sonic logging and other methods, permeability is evaluated by nuclear magnetic resonance logging and other technologies, and the mineral composition of rocks is analyzed by means of element capture spectrum logging, natural gamma spectrum logging and other means, organic matter content is calculated by using the relationship between logging response characteristics and total organic carbon content, and the gas content of the reservoir is comprehensively evaluated by combining the adsorbed gas content logging evaluation method and the free gas content calculation method;

[0060] Taking into account multiple parameters such as porosity, permeability, mineral composition, organic matter content, and gas stars, a comprehensive evaluation of the reservoir is conducted. Based on the geological model, the geological risks that may be encountered during reservoir development, such as the degree of fracture development and changes in formation pressure, are assessed.

[0061] Compared with the prior art, the present invention provides a well logging evaluation method applicable to shale gas reservoir geological models, which has the following beneficial effects:

[0062] 1. The well logging evaluation method of the present invention can obtain multiple reservoir parameters including organic carbon content, thermal maturity, porosity, permeability, water saturation, fracture development degree, etc. The comprehensive analysis of these parameters can more comprehensively reflect the reservoir characteristics, thereby improving the accuracy of the geological model. The well logging data has an extremely high vertical resolution and can record the stratigraphic information of multiple depth points at equal intervals within a depth of one meter, which enables the geological model to more finely characterize the reservoir characteristics and improve the accuracy and reliability of the model.

[0063] 2. The well logging evaluation method of the present invention has the characteristics of high speed and high efficiency, and can usually evaluate a large number of reservoirs in a short time, which helps to speed up the exploration and development process and shorten the project cycle.

[0064] 3. The well logging evaluation method of the present invention combines knowledge from multiple disciplines such as geophysical well logging technology, geology, and geochemistry, and promotes the cross-integration and technological innovation of these disciplines. With the continuous development and application of well logging evaluation technology, it will promote the improvement of the technical level and international competitiveness of the entire shale gas exploration and development industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figure 1 A well logging evaluation method suitable for a shale gas reservoir geological model, characterized in that it comprises the following steps:

[0068] Step 1: Collect and organize logging data, including natural gamma, resistivity, acoustic time difference, density, and neutron logging curves;

[0069] Step 2: Establish an initial geological model of the shale gas reservoir based on geological data and logging data of adjacent wells;

[0070] Step 3: Preprocess the logging curve, including depth correction, environmental correction, and data smoothing;

[0071] Step 4: Using the response characteristics of the well logging curve, identify the fractures and bedding in the shale gas reservoir;

[0072] Step 5: Calculate the physical property parameters of shale gas reservoir using well logging data;

[0073] Step 6: Compare the calculated reservoir parameters with the initial geological model to verify the accuracy of the model;

[0074] Step 7. Combine the geological model, logging data and reservoir parameters to conduct a comprehensive evaluation of the shale gas reservoir and write a logging evaluation report, recording the evaluation process, results and conclusions in detail.

[0075] Specifically, Figure 1 As shown, the data collection and preparation include data collection and data preparation, specifically:

[0076] Data collection: well logging data collection, geological data collection, and other related data collection;

[0077] Data preparation: data collation and verification, data conversion and formatting, preliminary construction of geological models, data backup and security management.

[0078] Through the above technical solution, conventional logging data: including natural gamma, resistivity, acoustic time difference, density, neutron and other conventional logging curve data, these data are the basis for evaluating the geological characteristics of shale gas reservoirs.

[0079] Special logging data: Collect special logging data such as element capture energy spectrum, dipole acoustic wave, and acoustic-electric imaging as needed. These data can provide more detailed reservoir information, such as mineral composition and fracture development;

[0080] Stratigraphic column: Collect stratigraphic column of the study area to understand basic information such as lithology and thickness of the strata;

[0081] Geological structure map: Collect geological structure maps to understand the geological structure characteristics of the study area, such as folds, faults, etc.;

[0082] Neighboring data: Collect logging data and core analysis data of neighboring wells to provide reference for establishing initial geological models and verifying models;

[0083] Data preparation includes organizing the collected data to ensure the integrity and consistency of the data, converting the logging data into an appropriate format according to the needs of the evaluation method, using the collected geological data and adjacent well data to preliminarily build a geological model of the shale gas reservoir, and backing up the collected data to prevent data loss or damage.

[0084] Specifically, Figure 1 As shown, the logging data needs to collect basic geological data of shale gas reservoirs, and based on the collected data, a logging evaluation method suitable for shale gas reservoirs is established. After the logging evaluation method is established and useful geological information is extracted, the initial geological model is constructed. After the initial geological model is established, the model is verified and optimized.

[0085] Through the above technical solution, the basic information of the well, mud logging data, well logging data and its interpretation results, core data, and fault data intersecting with the well trajectory;

[0086] Seismic data: seismic data structural interpretation data and seismic attribute prediction data;

[0087] It also includes a selection logging series: according to the geological characteristics of shale gas reservoirs, select geophysical logging methods that can reflect the characteristics of shale organic matter and gas indications. Conventional logging series include natural gamma, well diameter, neutron density, lithology density, acoustic time difference, and resistivity logging. For special needs, special logging series can also be considered, such as element capture spectrum logging, dipole acoustic logging, and acoustic and electrical imaging logging;

[0088] Data processing and interpretation: Process the logging data, eliminate interference factors, improve data quality, interpret the logging data according to the geological characteristics of shale gas reservoirs, and extract useful geological information;

[0089] Establishing interpretation model: Using well logging data, core data, and mud logging data, an interpretation model of shale gas reservoir parameters is established, which can be used to evaluate key parameters of shale gas reservoirs, such as organic carbon content, organic matter maturity, and effective thickness;

[0090] The establishment of the initial geological model is specifically as follows: establishing a framework geological model, using drilling layer data and seismic interpretation results to establish a geological model that characterizes the geometric morphology of the shale gas reservoir interface, the three-dimensional spatial distribution characteristics and the internal structure;

[0091] Establishing matrix attribute geological model: Based on sample analysis and testing data, well logging data and its interpretation results, and seismic data prediction results, a geological model is established within the framework geological model to characterize the spatial variation characteristics of various continuous or discrete distribution geological attributes within the shale gas reservoir;

[0092] Establish a fracture geological model: Using the interpretation results of natural fractures from well logging and the prediction results of seismic faults and fractures, combined with the constraints of geological models of matrix stress fields, brittle minerals and other properties, establish a geological model that characterizes the spatial distribution of fractures of various causes and their conductivity;

[0093] Fusion and superposition to form a comprehensive geological model: Various geological models are fused and superimposed step by step to form a comprehensive geological model that characterizes the distribution and changes of various geological characteristics, attributes and parameters inside the shale gas reservoir in three-dimensional space.

[0094] Specifically, Figure 1 As shown, the logging data is the real underground data directly obtained vertically, specifically:

[0095] Step 1: Eliminate abnormal points of the logging curve by corresponding analysis and filtering of the logging curve;

[0096] Step 2: Using logging environment parameters, logging instrument type and model, and wellbore conditions, environmental correction is achieved through the model;

[0097] Step 3: Perform depth correction through correlation comparison to make the logging curves of multiple measurements reach depth uniformity and matching;

[0098] Step 4: When the formation is thin, the amplitude of the logging response is low and its value cannot reflect the true situation of the formation. The logging curve must be corrected for layer thickness;

[0099] Step 5: When the reservoir contains oil and gas, it will affect the natural potential, resistivity, and porosity logging. The influence of oil and gas should be eliminated so that the logging response can reflect the true situation of the formation.

[0100] Step 6: The acoustic wave curve does not match the lithology ideally. Analyze the reasons and eliminate the wellbore factor.

[0101] Specifically, Figure 1 As shown, the fracture and bedding identification includes fracture identification and bedding identification, wherein fracture identification includes conventional logging methods, special logging methods and mathematical methods;

[0102] And the conventional logging methods include: dual lateral logging, natural gamma logging, and acoustic time difference logging;

[0103] Special logging methods include: acoustic and electrical imaging logging, dipole acoustic wave logging;

[0104] Mathematical methods include: wavelet multi-scale analysis, BP neural network and grey relation.

[0105] Specifically, Figure 1 As shown, the reservoir parameter calculation not only reflects the physical and chemical properties of the reservoir, but also directly affects the subsequent exploration and development strategies, including porosity calculation, permeability calculation, gas saturation calculation, organic carbon content calculation and rock brittleness index calculation.

[0106] Through the above technical solution, porosity is an important indicator for evaluating reservoir permeability and storage capacity, and can be calculated by using the conventional three-porosity logging method combined with core experimental data for correction, or it can be estimated by dual laterolog response values;

[0107] Permeability estimation: The fitting relationship between porosity and permeability can be analyzed through core experiments, and then the permeability parameters can be estimated using the logging porosity. The nuclear magnetic resonance logging method can also provide relatively accurate permeability parameters.

[0108] Gas saturation calculation includes free gas saturation and adsorbed gas saturation: it can be calculated by conventional logging methods combined with core experimental data, and factors such as formation pressure, formation temperature, total organic carbon content and kerogen type need to be considered. Langmuir isotherm curve data is usually used for calculation;

[0109] The organic carbon content is calculated by using the overlapping relationship between resistivity logging and porosity logging curves and by establishing a regression relationship between radioactive uranium content and total organic carbon content;

[0110] The rock brittleness index is calculated by using array acoustic wave data to obtain the Young's modulus and Poisson's ratio of the rock, and then the rock's brittleness index is calculated. The lower the Poisson's ratio, the more brittle the rock is. When the Young's modulus value increases, the rock will be more brittle. The brittleness index is also calculated through the core mineral measurement results, usually expressed as the ratio of quartz content to total mineral content.

[0111] Specifically, Figure 1As shown, the geological model verification and adjustment is an important step to ensure the accuracy and reliability of the model, which includes the verification of the geological model and the adjustment of the geological model, specifically:

[0112] Verification of geological models includes: Verification based on logging data: using logging curves to compare with geological models to verify whether the rock type, mineral composition, porosity, permeability and other parameters predicted by the model are consistent with the logging data;

[0113] Core testing and comparison: Compare the predictions of the geological model with the core test data;

[0114] Production data verification: Use actual production data from shale gas wells to verify the accuracy of geological models;

[0115] Adjustment of geological model: Adjustment based on verification results: Improve the accuracy and reliability of the model by modifying the model parameter settings, updating geological data, and improving algorithms;

[0116] Iterative optimization: By continuously collecting new logging data, core test data and production data, the model is continuously verified and adjusted to gradually approach the actual geological conditions;

[0117] Multi-data fusion: In the process of adjusting the geological model, data from different sources are integrated to provide more comprehensive geological information support.

[0118] Through the above technical solution, when verifying and adjusting the geological model, it is necessary to ensure that the data used is of high quality and consistency. The geological conditions of shale gas reservoirs are usually more complex, with problems such as heterogeneity and anisotropy. When verifying and adjusting the geological model, it is necessary to fully consider the impact of these geological complexities to ensure the accuracy and reliability of the model.

[0119] Specifically, Figure 1 As shown, the comprehensive evaluation and interpretation, report writing and results presentation include comprehensive evaluation and interpretation, report writing and results presentation, wherein porosity is calculated by density logging, sonic logging and other methods, permeability is evaluated by nuclear magnetic resonance logging and other technologies, and the mineral composition of rocks is analyzed by element capture spectrum logging, natural gamma spectrum logging and other means, organic matter content is calculated by using the relationship between logging response characteristics and total organic carbon content, and the gas content of the reservoir is comprehensively evaluated by combining the adsorbed gas content logging evaluation method and the free gas content calculation method;

[0120] Taking into account multiple parameters such as porosity, permeability, mineral composition, organic matter content, and gas stars, a comprehensive evaluation of the reservoir is conducted. Based on the geological model, the geological risks that may be encountered during reservoir development, such as the degree of fracture development and changes in formation pressure, are assessed.

[0121] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A well logging evaluation method suitable for a shale gas reservoir geological model, characterized in that: The following steps are involved: Step 1: Collect and organize logging data, including natural gamma, resistivity, acoustic time difference, density, and neutron logging curves; Step 2: Establish an initial geological model of the shale gas reservoir based on geological data and logging data of adjacent wells; Step 3: Preprocess the logging curve, including depth correction, environmental correction, and data smoothing; Step 4: Using the response characteristics of the well logging curve, identify the fractures and bedding in the shale gas reservoir; Step 5: Calculate the physical property parameters of shale gas reservoir using well logging data; Step 6: Compare the calculated reservoir parameters with the initial geological model to verify the accuracy of the model; Step 7. Combine the geological model, logging data and reservoir parameters to conduct a comprehensive evaluation of the shale gas reservoir and write a logging evaluation report, recording the evaluation process, results and conclusions in detail.

2. A well logging evaluation method applicable to a shale gas reservoir geological model according to claim 1, characterized in that: The data collection and preparation include data collection and data preparation, specifically: Data collection: well logging data collection, geological data collection, and other related data collection; Data preparation: data collation and verification, data conversion and formatting, preliminary construction of geological models, data backup and security management.

3. The well logging evaluation method applicable to a shale gas reservoir geological model according to claim 1, characterized in that: The logging data needs to collect basic geological data of shale gas reservoirs. Based on the collected data, a logging evaluation method suitable for shale gas reservoirs is established. After the logging evaluation method is established and useful geological information is extracted, the initial geological model is constructed. After the initial geological model is established, the model is verified and optimized.

4. The well logging evaluation method applicable to a shale gas reservoir geological model according to claim 1, characterized in that: The logging data is the real underground data directly obtained vertically, specifically: Step 1: Eliminate abnormal points of the logging curve by corresponding analysis and filtering of the logging curve; Step 2: Using logging environment parameters, logging instrument type and model, and wellbore conditions, environmental correction is achieved through the model; Step 3: Perform depth correction through correlation comparison to make the logging curves of multiple measurements reach depth uniformity and matching; Step 4: When the formation is thin, the amplitude of the logging response is low and its value cannot reflect the true situation of the formation. The logging curve must be corrected for layer thickness; Step 5: When the reservoir contains oil and gas, it will affect the natural potential, resistivity, and porosity logging. The influence of oil and gas should be eliminated so that the logging response can reflect the true situation of the formation. Step 6: The acoustic wave curve does not match the lithology ideally. Analyze the reasons and eliminate the wellbore factor.

5. The well logging evaluation method applicable to a shale gas reservoir geological model according to claim 1, characterized in that: The fracture and bedding identification includes fracture identification and bedding identification, wherein fracture identification includes conventional logging methods, special logging methods and mathematical methods; And the conventional logging methods include: dual lateral logging, natural gamma logging, and acoustic time difference logging; Special logging methods include: acoustic and electrical imaging logging, dipole acoustic wave logging; Mathematical methods include: wavelet multi-scale analysis, BP neural network and grey relation.

6. The well logging evaluation method applicable to a shale gas reservoir geological model according to claim 1, characterized in that: The reservoir parameter calculation not only reflects the physical and chemical properties of the reservoir, but also directly affects the subsequent exploration and development strategies, including porosity calculation, permeability calculation, gas saturation calculation, organic carbon content calculation and rock brittleness index calculation.

7. The well logging evaluation method applicable to a shale gas reservoir geological model according to claim 1, characterized in that: The geological model verification and adjustment is an important step to ensure the accuracy and reliability of the model, which includes the verification of the geological model and the adjustment of the geological model, specifically: Verification of geological models includes: Verification based on logging data: using logging curves to compare with geological models to verify whether the rock type, mineral composition, porosity, permeability and other parameters predicted by the model are consistent with the logging data; Core testing and comparison: Compare the predictions of the geological model with the core test data; Production data verification: Use actual production data from shale gas wells to verify the accuracy of geological models; Adjustment of geological model: Adjustment based on verification results: Improve the accuracy and reliability of the model by modifying the model parameter settings, updating geological data, and improving algorithms; Iterative optimization: By continuously collecting new logging data, core test data and production data, the model is continuously verified and adjusted to gradually approach the actual geological conditions; Multi-data fusion: In the process of adjusting the geological model, data from different sources are integrated to provide more comprehensive geological information support.

8. The well logging evaluation method applicable to a shale gas reservoir geological model according to claim 1, characterized in that: The said comprehensive evaluation and interpretation, report writing and results presentation include comprehensive evaluation and interpretation, report writing and results presentation, wherein porosity is calculated by density logging, sonic logging and other methods, permeability is evaluated by nuclear magnetic resonance logging and other technologies, and mineral composition of rocks is analyzed by means of element capture spectrum logging, natural gamma spectrum logging and other means, organic matter content is calculated by using the relationship between logging response characteristics and total organic carbon content, and gas content of reservoir is comprehensively evaluated by combining adsorbed gas content logging evaluation method and free gas content calculation method; Taking into account multiple parameters such as porosity, permeability, mineral composition, organic matter content, and gas stars, a comprehensive evaluation of the reservoir is conducted, and based on the geological model, the geological risks that may be encountered during reservoir development, such as the degree of fracture development and changes in formation pressure, are assessed.

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