An intelligent processing method for mud logging data

Abstract

This application relates to the field of data technology and discloses an intelligent processing method for mud logging data. The method includes: collecting data through multi-source mud logging equipment and preprocessing to form a result set; identifying and classifying formation characteristics according to the result set; analyzing oil and gas parameters based on the formation atlas and the result set; establishing an early warning mechanism by identifying risks using the oil and gas evaluation table and the result set; evaluating reservoir physical properties according to the oil and gas evaluation table and the early warning mechanism; integrating the formation atlas, the oil and gas evaluation table and the reservoir report to reconstruct the geological structure and predict the enrichment area. This application realizes the full-process intelligent processing from the acquisition and preprocessing of multi-source heterogeneous mud logging data to formation identification, oil and gas evaluation, drilling risk early warning, reservoir evaluation and oil and gas reservoir description, overcomes problems such as data spatio-temporal mismatch, isolated parameter analysis, strong subjectivity in evaluation, and lagging early warning in traditional methods, and improves the efficiency, accuracy and systematicness of mud logging data processing.

Description

Technical Field

[0001] This application relates to the field of data technology, and particularly to an intelligent processing method for mud logging data. Background Art

[0002] During the exploration and development of oil and gas resources, mud logging data, as an important information carrier directly reflecting underground geological characteristics and oil and gas conditions, plays an irreplaceable role in the discovery, evaluation, and development of oil and gas reservoirs. Traditional mud logging data processing methods mainly rely on manual experience judgment, and simple analysis is carried out through basic mud logging parameters such as drilling fluid gas components, cuttings fluorescence, and lithology description to form formation division and preliminary evaluation of oil and gas layers. With the development of technology, computer-aided processing systems have gradually been introduced into the mud logging field, such as gas logging data processing systems, mud logging comprehensive analysis software, etc. These technical means have improved the data processing efficiency and accuracy, and realized the basic digital and visual processing of mud logging data. In recent years, with the wide application of multi-parameter mud logging instruments, mud logging technology has developed from traditional gas logging to multi-parameter comprehensive mud logging in the direction of gas logging, cuttings fluorescence, gamma, acoustic wave, etc. The data types and acquisition frequencies have increased significantly, providing a data basis for more comprehensive and accurate evaluation of formation and oil and gas conditions.

[0003] However, there are still many deficiencies in the existing mud logging data processing technology: First, in the face of heterogeneous data generated by multi-source mud logging equipment, there is a lack of effective timestamp alignment and depth correction methods, resulting in spatio-temporal mismatch problems between data from different sources; Second, the ability to process noise and outliers during data processing is limited, and the data quality is unstable; Third, formation identification and oil and gas evaluation mainly rely on single parameters or simple threshold judgments, lacking a multi-parameter comprehensive verification mechanism, and the evaluation results are highly subjective; Fourth, the identification of drilling anomalies lags behind, and real-time risk warning cannot be achieved; Fifth, the evaluation of reservoir physical property parameters mostly relies on empirical formulas, without fully considering the differences in regional geological characteristics and reservoir heterogeneity; The description of oil and gas reservoirs lacks systematicness and integrity, and it is difficult to accurately depict and predict from single-well data to regional oil and gas reservoirs. These problems seriously restrict the full play of the value of mud logging data and affect the efficiency and success rate of oil and gas resource exploration and development. Summary of the Invention

[0004] This application provides an intelligent processing method for mud logging data, which is used to realize the full-process intelligent processing from the acquisition and preprocessing of multi-source heterogeneous mud logging data to formation identification, oil and gas evaluation, drilling risk warning, reservoir evaluation, and oil and gas reservoir description, overcome problems such as spatio-temporal mismatch of data, isolated analysis of parameters, strong subjectivity of evaluation, and lag of warning in traditional methods, and improve the efficiency, accuracy, and systematicness of mud logging data processing.

[0005] The present application provides an intelligent processing method for mud logging data. The intelligent processing method for mud logging data includes: collecting and preprocessing drilling process data through multi-source mud logging equipment to form a mud logging data preprocessing result set; identifying and classifying formation characteristics according to the mud logging data preprocessing result set to generate a formation stratification classification map; comprehensively analyzing oil and gas parameters based on the formation stratification classification map and the mud logging data preprocessing result set to form an oil and gas layer evaluation result table; using the oil and gas layer evaluation result table and the mud logging data preprocessing result set to identify and classify the risk levels of drilling anomalies and establish a drilling risk early warning mechanism; intelligently converting and evaluating reservoir physical property parameters according to the oil and gas layer evaluation result table and the drilling risk early warning mechanism to form a comprehensive reservoir evaluation report; integrating the formation stratification classification map, the oil and gas layer evaluation result table, and the comprehensive reservoir evaluation report to reconstruct the geological structure and predict the oil and gas enrichment area, and generate an oil and gas reservoir description result.

[0006] In the technical solution provided by this application, the multi-source logging equipment is used to collect and preprocess the drilling process data to form a logging data preprocessing result set, which realizes the time-depth coordination and unification of different logging data sources, solves the spatio-temporal mismatch problem in traditional logging data processing, and effectively improves the data quality and reliability of subsequent analysis; according to the logging data preprocessing result set, the formation characteristics are identified and classified to generate a formation stratification classification map, and the Poisson change point detection algorithm is applied to realize the accurate identification of the formation interface, overcoming the defect of inaccurate identification of the formation demarcation point by traditional methods; based on the formation stratification classification map and the logging data preprocessing result set, the oil and gas parameters are comprehensively analyzed to form an oil and gas layer evaluation result table, and the normalized oil and gas display index and auxiliary oil and gas identification feature data are weighted and fused by the multi-parameter cross-validation method, avoiding the one-sidedness of single-parameter evaluation and improving the accuracy of oil and gas layer identification; using the oil and gas layer evaluation result table and the logging data preprocessing result set, the drilling anomalies are identified and the risk levels are classified, a drilling risk warning mechanism is established, and the machine learning algorithm is applied to the drilling anomaly pattern recognition, significantly improving the foresight and accuracy of risk warning, and can early warn and prevent drilling accidents; according to the oil and gas layer evaluation result table and the drilling risk warning mechanism, the reservoir physical property parameters are intelligently converted and evaluated to form a comprehensive reservoir evaluation report, the regional conversion function is used to calculate and process the reservoir physical property parameter set, and the parameters are corrected in combination with the evaluation of the reservoir damage degree during the drilling process, realizing the accurate quantitative evaluation of the reservoir physical property parameters; integrating the formation stratification classification map, the oil and gas layer evaluation result table and the comprehensive reservoir evaluation report, the geological structure is reconstructed and the oil and gas enrichment areas are predicted to generate an oil and gas reservoir description result, and the spatial interpolation algorithm is applied to horizontally extend the single-well geological information, realizing the leap from a one-dimensional wellbore model to a three-dimensional oil and gas reservoir description. The application of artificial intelligence algorithms in this solution, such as the application of the Poisson change point detection algorithm in formation interface identification, the application of the multi-parameter cross-validation method in comprehensive oil and gas parameter analysis, the application of the machine learning algorithm in drilling anomaly identification, and the application of the spatial interpolation algorithm in three-dimensional geological modeling, makes the entire logging data processing process get rid of the limitations of traditional empirical judgment and realizes the intelligence, automation and precision of data processing. Brief Description of the Drawings

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0008] Figure 1 It is a schematic diagram of an embodiment of the intelligent logging data processing method in the embodiment of this application;

[0009] Figure 2 This is a schematic flow chart of collecting and preprocessing drilling process data through a multi-source mud logging device in an embodiment of the present application. Specific implementation manners

[0010] An embodiment of the present application provides an intelligent processing method for mud logging data. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Please refer to Figure 1 In an embodiment of the intelligent processing method for mud logging data in the embodiment of the present application, it includes:

[0012] Step S101: Collect and preprocess drilling process data through a multi-source mud logging device to form a mud logging data preprocessing result set;

[0013] Step S102: Identify and classify formation characteristics according to the mud logging data preprocessing result set to generate a formation stratification classification map;

[0014] Step S103: Based on the formation stratification classification map and the mud logging data preprocessing result set, comprehensively analyze oil and gas parameters to form an oil and gas layer evaluation result table;

[0015] Step S104: Use the oil and gas layer evaluation result table and the mud logging data preprocessing result set to identify and classify the risk levels of drilling anomalies, and establish a drilling risk warning mechanism;

[0016] Step S105: According to the oil and gas layer evaluation result table and the drilling risk warning mechanism, perform intelligent conversion and evaluation on reservoir physical property parameters to form a comprehensive reservoir evaluation report;

[0017] Step S106: Integrate the formation stratification classification map, the oil and gas layer evaluation result table and the comprehensive reservoir evaluation report, perform geological structure reconstruction and prediction of oil and gas enrichment areas, and generate an oil and gas reservoir description result.

[0018] It is understandable that the execution entity of this application can be an intelligent processing system for mud logging data, or it can also be a terminal or a server, and no specific limitation is made here. In the embodiments of this application, the server is taken as an example of the execution entity for illustration.

[0019] Specifically, the multi-source mud logging equipment is used to collect and preprocess the drilling process data to form a mud logging data preprocessing result set. The specific implementation process is as follows: The original mud logging parameters are collected through a gas detector, a cuttings fluorescence detector, and a gamma ray detector to obtain data such as the gas components in the drilling fluid, the cuttings fluorescence value, and the natural gamma value, which constitute the original mud logging data stream. At the same time, engineering parameters such as the weight on bit, the drilling rate, the torque, and the pump pressure are obtained from the drilling engineering monitoring system, and combined with the lithology description data in the geological coring analysis, an auxiliary mud logging parameter set is formed. The original mud logging data stream is subjected to timestamp alignment and depth correction processing to eliminate the lag effect and depth deviation, and a time-depth consistent mud logging data is constructed. In an exploration well example, there is a lag deviation of about 15 meters between the methane content data collected by the gas detector and the drilling depth. Through timestamp analysis and comparison, the data points are remapped to the correct depth, realizing the time-depth synchronization of the data. Then, the piecewise cubic spline interpolation method is used to resample the time-depth consistent mud logging data and the auxiliary mud logging parameter set to solve the problem of inconsistent sampling frequencies of data from different sources and generate a unified frequency data set. Then, the wavelet transform filtering technology is used to suppress noise and remove outliers from the unified frequency data set to improve the signal-to-noise ratio of the data and form high-quality mud logging basic data. The wavelet transform filtering technology decomposes the data at multiple scales, identifies and removes high-frequency noise components, and retains the effective information of the data. For the missing segments in the high-quality mud logging basic data, data repair and filling are carried out through the associated parameter mapping method, and a time-depth double-index system is established to form a mud logging data preprocessing result set.

[0020] Based on the pre - processed logging data result set, identify and classify formation characteristics to generate a formation stratification and classification atlas. Specifically: By calculating the coefficient of variation and the first - order derivative of the natural gamma ray curve in the pre - processed logging data result set, identify the curve mutation points to form a candidate set of formation boundaries. The coefficient of variation is the ratio of the standard deviation to the mean value, which can reflect the degree of data fluctuation, and the first - order derivative reflects the curve change rate. Then, combined with the mutation characteristics of the drilling rate curve, screen and verify the candidate set of formation boundaries to determine the preliminary formation boundary depth points and construct a preliminary formation boundary point set. Then, optimize the preliminary formation boundary point set through the Poisson change - point detection algorithm to exclude non - geological boundary points and generate an accurate formation boundary point set. The Poisson change - point detection algorithm identifies abnormal change points in the data sequence through statistical principles and can effectively distinguish geological and non - geological changes. Extract the gas logging component ratio, cuttings fluorescence characteristics, and natural gamma ray values within each formation segment from the pre - processed logging data result set to construct a formation characteristic vector set. Based on the similarity calculation results between the formation characteristic vector set and the regional standard formation data, determine the geological age attribution of each formation segment to form a formation classification result. According to the cuttings morphological characteristics, mineral composition, and gas logging hydrocarbon component ratio, conduct a lithological subdivision of each formation segment in the formation classification result and integrate it to form a formation stratification and classification atlas.

[0021] Based on the formation stratification and classification atlas and the pre - processed logging data result set, conduct a comprehensive analysis of oil and gas parameters to form an oil and gas layer evaluation result table. The specific implementation process is as follows: Extract the total hydrocarbon value, methane / ethane ratio, abnormal gas component content, and cuttings fluorescence intensity parameters from the pre - processed logging data result set to construct a basic index set for oil and gas shows. Then, according to the lithological classification results in the formation stratification and classification atlas, perform standardization processing on the basic index set for oil and gas shows by selecting different evaluation standard thresholds to generate a lithology - matched normalized oil and gas show index. The standardization processing takes into account the background value differences of different lithologies. For example, sandstone and mudstone correspond to different total hydrocarbon background value thresholds. Then, extract the information of drilling rate mutation, drilling pressure change, and mud density anomaly from the engineering parameters in the pre - processed logging data result set, and combine with the acoustic travel - time data to form auxiliary oil and gas identification characteristic data. Through the multi - parameter cross - validation method, weight - fuse the normalized oil and gas show index and the auxiliary oil and gas identification characteristic data to calculate the comprehensive evaluation index value of the oil and gas layer and establish a comprehensive evaluation index curve of the oil and gas layer. The multi - parameter cross - validation method is a data fusion technology based on parameter mutual verification, which can improve the identification accuracy. According to the comprehensive evaluation index curve of the oil and gas layer, combined with the lithology and sedimentary environment information in the formation stratification and classification atlas, identify the location of potential oil and gas layer segments and divide them into grades to form a preliminary division result of the oil and gas layer. Calculate the index integral value and effective thickness of each oil and gas layer segment in the preliminary division result of the oil and gas layer, and combine with the regional production capacity data to conduct a production capacity potential prediction to form an oil and gas layer evaluation result table.

[0022] Using the oil and gas reservoir evaluation result table and the pre - processed logging data result set, identify and classify the risk levels of drilling anomalies, and establish a drilling risk early - warning mechanism. Specifically: By statistically calculating the gas logging parameters, drilling engineering parameters, and mud property parameters in the pre - processed logging data result set, determine the parameter fluctuation range under normal drilling conditions, and establish a drilling parameter reference file. Extract the latest drilling parameters from the pre - processed logging data result set, compare and analyze them with the drilling parameter reference file, calculate the parameter deviation magnitude, and generate an anomaly index matrix. Then, according to the characteristic patterns of key anomaly types such as well kick, lost circulation, stuck pipe, and sudden formation pressure change, construct a drilling anomaly feature library, match and compare the anomaly index matrix with the drilling anomaly feature library to identify the locations of potential drilling risk points. Combining the oil and gas reservoir information in the oil and gas reservoir evaluation result table, conduct a correlation analysis on the drilling risk points, evaluate the abnormal development trend and severity, classify the drilling risks, and form a graded risk signal. By analyzing historical drilling anomaly handling cases, for the graded risk signal, combined with the current drilling environment and reservoir characteristics, generate a targeted treatment suggestion plan. Integrate the graded risk signal and the treatment suggestion plan into the drilling decision - making push system, automatically trigger the early - warning process, and accurately push the early - warning information to the drilling site engineers to establish a drilling risk early - warning mechanism.

[0023] According to the oil and gas reservoir evaluation result table and the drilling risk early - warning mechanism, conduct intelligent conversion and evaluation of reservoir physical property parameters to form a comprehensive reservoir evaluation report. The specific implementation process is as follows: Extract the acoustic time difference, cuttings pore characteristics, and gas logging hydrocarbon component ratio data related to reservoir physical properties from the pre - processed logging data result set, and combine the oil and gas show information in the oil and gas reservoir evaluation result table to construct a reservoir physical property parameter set. Then, perform calculation and processing on the reservoir physical property parameter set through a regional conversion function to convert the logging physical property indexes into reservoir porosity, permeability, and oil - gas saturation values, and generate an initial quantitative reservoir evaluation result. The regional conversion function is a mathematical model established based on regional geological characteristics that can convert logging parameters into reservoir physical property parameters. Combining the anomaly identification information in the drilling risk early - warning mechanism, evaluate the degree of reservoir damage during the drilling process, and correct the initial quantitative reservoir evaluation result to form a reservoir physical property correction table. According to the oil and gas reservoir sections in the oil and gas reservoir evaluation result table and the reservoir physical property correction table, analyze the reservoir heterogeneity factors, identify the locations of preferential flow channels and fluid barrier layers, and construct a reservoir seepage characteristic map. Based on the reservoir seepage characteristic map, calculate and evaluate the vertical continuity and lateral distribution range of the reservoir, determine the effective reservoir thickness and distribution range, and generate reservoir geometric structure data. Integrate the reservoir physical property correction table, the reservoir seepage characteristic map, and the reservoir geometric structure data, conduct a comprehensive reservoir quality rating, and compile a comprehensive reservoir evaluation report according to the reservoir depth, thickness, physical properties, heterogeneity characteristics, and quality grades.

[0024] Integrate the stratigraphic classification atlas, the oil and gas reservoir evaluation result table, and the comprehensive reservoir evaluation report to reconstruct the geological structure and predict the oil and gas enrichment areas, generating the oil and gas reservoir description results. The specific implementation process is as follows: Based on the key information of the strata and reservoirs in the stratigraphic classification atlas, the oil and gas reservoir evaluation result table, and the comprehensive reservoir evaluation report, construct a one-dimensional wellbore geological profile model to display the vertical distribution characteristics of the strata. Then, according to the one-dimensional wellbore geological profile model combined with the regional tectonic background data, perform horizontal extension processing on the single-well geological information through a spatial interpolation algorithm to form a three-dimensional geological framework. The spatial interpolation algorithm generates a continuous spatial distribution data field by performing spatial analysis and prediction on the known point data. Use the reservoir physical property distribution and heterogeneity characteristic data in the comprehensive reservoir evaluation report to finely depict the reservoir structure in the three-dimensional geological framework, generating a fine reservoir structure diagram. By analyzing the oil and gas show information and pressure data in the oil and gas reservoir evaluation result table, determine the position of the oil and gas-water interface, mark the fluid distribution boundary in the fine reservoir structure diagram, and establish a fluid distribution diagram. Based on the reservoir physical property and connectivity data in the oil and gas reservoir evaluation result table and the comprehensive reservoir evaluation report, predict and calculate the oil and gas migration channels and enrichment areas, generating an oil and gas enrichment distribution diagram. Integrate the three-dimensional geological framework, the fine reservoir structure diagram, the fluid distribution diagram, and the oil and gas enrichment distribution diagram, comprehensively evaluate the resource potential and production capacity expectations of the target area, and form the oil and gas reservoir description results.

[0025] In the embodiments of the present application, the multi-source logging equipment is used to collect and preprocess the drilling process data to form a logging data preprocessing result set, realizing the time-depth coordination and unification of different logging data sources, solving the spatio-temporal mismatch problem in traditional logging data processing, and effectively improving the data quality and reliability of subsequent analysis; according to the logging data preprocessing result set, the formation characteristics are identified and classified to generate a formation stratification classification map, and the Poisson change point detection algorithm is applied to realize the accurate identification of the formation interface, overcoming the defect of inaccurate identification of the formation boundary point by traditional methods; based on the formation stratification classification map and the logging data preprocessing result set, the oil and gas parameters are comprehensively analyzed to form an oil and gas layer evaluation result table, and the normalized oil and gas display index and the auxiliary oil and gas identification feature data are weighted and fused by the multi-parameter cross-validation method, avoiding the one-sidedness of single-parameter evaluation and improving the accuracy of oil and gas layer identification; using the oil and gas layer evaluation result table and the logging data preprocessing result set, the drilling anomalies are identified and the risk levels are divided, a drilling risk early warning mechanism is established, and the machine learning algorithm is applied to the drilling anomaly pattern recognition, significantly improving the foresight and accuracy of risk early warning, and the drilling accidents can be early warned and prevented; according to the oil and gas layer evaluation result table and the drilling risk early warning mechanism, the reservoir physical property parameters are intelligently converted and evaluated to form a comprehensive reservoir evaluation report, the regional conversion function is used to calculate and process the reservoir physical property parameter set, and the parameters are corrected in combination with the evaluation of the reservoir damage degree during the drilling process, realizing the accurate quantitative evaluation of the reservoir physical property parameters; integrating the formation stratification classification map, the oil and gas layer evaluation result table and the comprehensive reservoir evaluation report, the geological structure is reconstructed and the oil and gas enrichment areas are predicted to generate an oil and gas reservoir description result, and the spatial interpolation algorithm is applied to horizontally extend the single-well geological information, realizing the leap from the one-dimensional wellbore model to the three-dimensional oil and gas reservoir description, providing a systematic technical basis for the oil and gas resource development decision-making. The application of artificial intelligence algorithms in this solution, such as the application of the Poisson change point detection algorithm in the formation interface identification, the application of the multi-parameter cross-validation method in the comprehensive analysis of oil and gas parameters, the application of the machine learning algorithm in the drilling anomaly identification, and the application of the spatial interpolation algorithm in the three-dimensional geological modeling, enables the entire logging data processing process to get rid of the limitations of traditional empirical judgment and realizes the intelligentization, automation and precision of data processing.

[0026] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0027] Collect the original logging parameters through a gas detector, a cuttings fluorescence detector, and a gamma ray detector to obtain the gas components of the drilling fluid, the cuttings fluorescence value, and the natural gamma value, and generate the original logging data stream; obtain the weight on bit, penetration rate, torque, and pump pressure parameters from the drilling engineering monitoring system, and combine the lithology description data in the geological coring analysis to form an auxiliary logging parameter set; perform timestamp alignment and depth correction processing on the original logging data stream to eliminate the lag effect and depth deviation, and construct the time-depth consistent logging data; perform resampling processing on the time-depth consistent logging data and the auxiliary logging parameter set through the piecewise cubic spline interpolation method to solve the problem of inconsistent sampling frequencies of data from different sources and generate a unified frequency data set; use the wavelet transform filtering technology to suppress noise and remove outliers from the unified frequency data set to improve the signal-to-noise ratio of the data and form high-quality basic logging data; for the missing segments in the high-quality basic logging data, perform data repair and filling through the associated parameter mapping method, establish a time-depth double-index system, and form a logging data preprocessing result set.

[0028] Specifically, as Figure 2 shown, it is a schematic flow chart of collecting and preprocessing the drilling process data through multi-source logging equipment in the embodiment of the present application. Deploy the equipment at the drilling site. The gas detector is mainly installed near the shale shaker and is used to continuously monitor and collect the content data of hydrocarbon gas components such as methane, ethane, and propane in the drilling fluid. The sampling frequency is usually once every 30 seconds; the cuttings fluorescence detector is set at the outlet of the shale shaker, irradiates the cuttings samples returned from the bottom of the well with ultraviolet light, and measures the fluorescence intensity value. The fluorescence intensity reflects the hydrocarbon content in the rock, and the sampling frequency is once every 5 minutes; the gamma ray detector obtains the natural gamma value data by detecting the natural radioactivity of the formation, and the sampling frequency is once every 1 minute. The data collected by these devices are integrated through the on-site acquisition terminal to form the original logging data stream. Each data point in the data stream contains three key information elements: the parameter value, the acquisition timestamp, and the preliminary estimated depth. At the same time, the process of obtaining the weight on bit, penetration rate, torque, and pump pressure parameters from the drilling engineering monitoring system is to obtain the operating parameters of the drilling rig in real time through the drilling parameter acquisition system. The weight on bit represents the axial pressure on the drill bit, and the unit is kilonewton; the penetration rate represents the footage rate of the drill bit, and the unit is meters per hour; the torque represents the torque required for the drill string to rotate, and the unit is kilonewton-meter; the pump pressure represents the outlet pressure of the drilling fluid circulation pump, and the unit is megapascal. The sampling frequency of these engineering parameters is usually once every 5 seconds, which is significantly higher than the sampling frequency of geological parameters. Then, combine the lithology description data in the geological coring analysis, including information such as rock type, color, structure, and mineral composition, and integrate these engineering parameters and lithology data to form an auxiliary logging parameter set to provide support information for subsequent analysis.

[0029] Since it takes a certain amount of time for the gas sample to return from the bottom of the well to the surface, there is an obvious time lag effect in the gas logging data, and the lag time is related to the well depth and the drilling fluid circulation speed. Depth correction processing establishes a time-depth mapping relationship. By calculating the relationship between the drilling fluid circulation time and the well depth, the lag time correction value is obtained. Then, the lag time compensation is performed on each gas logging data point, associating it with the correct well depth, eliminating the depth deviation, and constructing time-depth consistent logging data. For example, when the well depth is 2500 meters and the upward return speed of the drilling fluid is 20 meters per minute, the calculated lag time is 125 minutes, which means that the gas components detected by the surface gas logging instrument actually come from the formation position where the drill bit was located 125 minutes ago.

[0030] The time-depth consistent logging data and the auxiliary logging parameter set are resampled by the piecewise cubic spline interpolation method to solve the problem of inconsistent sampling frequencies of data from different sources. The piecewise cubic spline interpolation method is a method for constructing a smooth curve between data points. It uses cubic polynomials to interpolate between adjacent data points and ensures the continuity of the curve and its first and second derivatives at the data points. The specific operation is to divide all parameters into several intervals according to depth. A cubic spline function is constructed within each interval, and then resampling is performed at a unified depth interval (usually 0.1 meter or 0.25 meter), unifying data from different sources and different sampling frequencies to the same depth points to generate a unified frequency data set. The process of noise suppression and outlier removal for the unified frequency data set using wavelet transform filtering technology involves the frequency domain analysis and processing of data. Wavelet transform filtering decomposes the time series data into different frequency components, separates the trend, periodic changes, and noise components in the data, and then retains the low-frequency and mid-frequency components (containing the main geological information) and removes the high-frequency components (mainly noise). The specific processing steps include: selecting an appropriate wavelet basis function (such as the Daubechies wavelet), performing multi-scale decomposition on the data sequence, setting a threshold to process the high-frequency coefficients, and reconstructing the signal to obtain the denoised data. Outlier removal identifies data points outside the reasonable range through statistical analysis. For example, using the 3-fold standard deviation criterion (data points deviating from the mean by more than 3 times the standard deviation are considered outliers), marking them and replacing them with reasonable values, thereby improving the signal-to-noise ratio of the data and forming high-quality logging basic data.

[0031] For the missing segments in high-quality mud logging basic data, data repair and filling through the associated parameter mapping method is an important means to solve the problem of incomplete data. The associated parameter mapping method is based on the correlation principle between parameters and estimates the missing values by establishing the mapping relationship between parameters. Calculate the correlation coefficient matrix between parameters, identify other parameters highly correlated with the missing parameter, then establish a regression model to describe the relationship between parameters, and use the known parameter values to estimate the missing parameter values. For example, when the acoustic travel time data is missing, the highly correlated density data can be used for estimation to achieve data repair and filling. Establish a time-depth double-index system, associate each data point with the corresponding acquisition time and well depth information at the same time, form a complete set of preprocessed mud logging data results, and provide a reliable data basis for subsequent analysis.

[0032] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0033] By calculating the coefficient of variation and the first derivative of the natural gamma curve in the preprocessed mud logging data results set, identify the curve mutation points to form a candidate set of formation boundaries; combine the mutation characteristics of the drilling rate curve to screen and verify the candidate set of formation boundaries, determine the preliminary formation boundary depth points, and construct a preliminary set of formation boundary points; optimize the preliminary set of formation boundary points through the Poisson change point detection algorithm, exclude non-geological boundary points, and generate an accurate set of formation boundary points; extract the gas logging component ratios, cuttings fluorescence characteristics, and natural gamma values within each formation segment from the preprocessed mud logging data results set to construct a formation feature vector set; based on the similarity calculation results between the formation feature vector set and the regional standard formation data, determine the geological age attribution of each formation segment to form a formation classification result; according to the cutting morphology characteristics, mineral composition, and gas logging hydrocarbon component ratios, conduct lithological subdivision of each formation segment in the formation classification result, and integrate to form a formation stratification classification map.

[0034] Specifically, the original mud logging parameters are collected by a gas logging instrument, a cuttings fluorescence detector, and a gamma ray detector. The gas logging instrument is installed near the shale shaker and continuously collects the content data of hydrocarbon gas components such as methane, ethane, and propane in the drilling fluid, with a sampling frequency of once every 30 seconds; the cuttings fluorescence detector is located at the outlet of the shale shaker, irradiates the returned cuttings samples with ultraviolet light, measures and records the fluorescence intensity values, with a sampling frequency of once every 5 minutes; the gamma ray detector obtains the natural gamma value by measuring the natural radioactivity of the formation, with a sampling frequency of once every 1 minute. The data collected by these three devices are integrated through a data acquisition terminal to form an original mud logging data stream containing parameter values, acquisition timestamps, and preliminary depth estimates.

[0035] At the same time, obtain engineering parameters such as weight on bit (WOB), rate of penetration (ROP), torque, and pump pressure from the drilling engineering monitoring system. The weight on bit refers to the axial pressure (kN) exerted on the drill bit, the rate of penetration refers to the footage rate of the drill bit (m / h), the torque refers to the torque required for the drill string to rotate (kN·m), and the pump pressure refers to the outlet pressure of the drilling fluid circulation pump (MPa). The sampling frequency of these parameters is as high as once every 5 seconds. Then, combine the lithology description data in geological core analysis, such as information on rock type, color, structure, and mineral composition, to integrate the engineering parameters and lithology data to form an auxiliary mud logging parameter set. Since it takes time for the drilling fluid to carry gas and cuttings back to the surface from the bottom of the well, there is a time lag between the gas logging data and the cuttings data relative to the actual drilling depth. Establish a time-depth mapping model by calculating the relationship between the drilling fluid circulation time and the well depth, perform time compensation and depth correction for each data point, accurately correspond the data collected on the surface to its true source depth, and construct time-depth consistent mud logging data. For example, at a well depth of 2500 meters, if the upward flow rate of the drilling fluid is 20 m / min, the lag time is approximately 125 minutes, which means that the gas data collected on the surface actually reflects the formation characteristics at the position where the drill bit was located 125 minutes ago.

[0036] Resample the time-depth consistent mud logging data and the auxiliary mud logging parameter set by the piecewise cubic spline interpolation method to solve the problem of inconsistent sampling frequencies of data from different sources. The piecewise cubic spline interpolation method is a mathematical interpolation method that uses cubic polynomials to construct a smooth curve between adjacent data points, ensuring the continuity of the curve and its first and second derivatives at the data points. Specifically, divide the data of different parameters into several intervals according to depth, establish a cubic spline function within each interval, and then resample all parameters at a unified depth interval (usually 0.1 m or 0.25 m), unify the sampling points of different data sources to the same depth sequence, and generate a dataset with a unified frequency. Use wavelet transform filtering technology to suppress noise and remove outliers from the dataset with a unified frequency. Wavelet transform filtering is a signal processing technology that can decompose time series data into components of different frequencies, distinguish the trends, periodic variations, and noise parts in the signal. In practical applications, select a wavelet basis function suitable for the characteristics of geological data (such as Daubechies wavelet), decompose the data at multiple scales, set a threshold to process the high-frequency coefficients (mainly containing noise), retain the low-frequency and medium-frequency components (containing the main geological information), and reconstruct the signal through inverse transformation to obtain the denoised data. Outlier removal uses statistical methods, such as marking points that deviate from the data mean by more than three standard deviations as outliers and replacing them with reasonable values, thereby improving the signal-to-noise ratio of the data and forming high-quality basic mud logging data.

[0037] For the missing segments in high-quality mud logging basic data, data repair and filling are carried out through the associated parameter mapping method. The associated parameter mapping method is based on the correlation principle between parameters, calculates the correlation coefficient matrix between parameters, identifies other parameters highly correlated with the missing parameter, then establishes a regression model to describe the quantitative relationship between parameters, and estimates the missing parameter value using known parameters. A time-depth double-index system is established to associate each data point with the corresponding acquisition time and well depth information simultaneously, forming a complete set of preprocessed mud logging data results.

[0038] In the process of identifying curve mutation points by calculating the coefficient of variation and the first derivative of the natural gamma curve in the preprocessed mud logging data results set, the following formulas are involved:

[0039]

[0040] Among them, represents the coefficient of variation of the natural gamma curve, represents the standard deviation of the natural gamma value, represents the average value of the natural gamma value. The coefficient of variation can reflect the relative fluctuation degree of data and helps to identify the change characteristics between different strata.

[0041]

[0042] Among them, represents the first derivative of the natural gamma curve, represents the depth where the gamma value is located, represents the gamma value at depth h + Δh, represents the depth interval. The first derivative reflects the rate of change of the gamma value. When the derivative value becomes significantly larger, it indicates that there may be a formation interface.

[0043] In the process of determining the geological age attribution of each formation segment based on the similarity calculation results between the formation feature vector set and the regional standard formation data, the following similarity calculation formula is used:

[0044]

[0045] Among them, represents the feature vector of the i-th formation segment and the j-th regional standard formation feature vector of the cosine similarity, represents the dot product of the two vectors, and respectively represent the Euclidean norms of the two vectors. The cosine similarity value ranges from -1 to 1, and the value closer to 1 indicates that the two formation features are more similar.

[0046]

[0047] Among them, represents the feature vector of the i-th formation segment and the feature vector of the j-th regional standard formation The Euclidean distance of, represents the feature vector The k-th component value of, represents the feature vector The k-th component value of, n represents the dimension of the feature vector. The smaller the Euclidean distance, the more similar the two formation features are.

[0048] Extract the morphological feature information from the cuttings samples collected at the mud logging site. The morphological features of cuttings refer to the appearance, size, shape, and surface structure of cuttings, which are obtained through visual observation and microscopic analysis. The morphological features include particle size (coarse sand, fine sand, silt, etc.), particle shape (angular, sub-angular, sub-rounded, rounded, etc.), surface structure (smooth, rough, porous, etc.), and color (gray, brown, black, etc.). These morphological features directly reflect the basic genetic types and sedimentary environment characteristics of rocks. Analyze the mineral composition of cuttings, and determine the main mineral composition in the rocks of each stratigraphic section through methods such as thin section identification of cuttings, X-ray diffraction analysis, or identification of mineral fluorescence characteristics. Mineral composition analysis includes the content and distribution characteristics of main rock-forming minerals (such as quartz, feldspar, clay minerals, carbonate minerals, etc.), as well as the types and contents of accessory minerals (such as mica, heavy minerals, etc.). Different lithology types have characteristic mineral combinations. For example, sandstone is mainly composed of quartz and feldspar, mudstone is rich in clay minerals, and limestone is mainly composed of calcite. At the same time, comprehensive analysis is carried out in combination with the gas logging hydrocarbon component ratio data. The gas logging hydrocarbon component ratio refers to the ratio relationship of the contents of hydrocarbon gases with different carbon numbers such as methane, ethane, and propane. These ratios are closely related to lithology and organic matter characteristics. Commonly used ratios include methane / ethane ratio, methane / total hydrocarbon ratio, wet gas ratio, etc. These ratios show certain regular differences in different lithology types and can be used as auxiliary criteria for fine lithology division. Based on the above three types of data, multi-feature comprehensive identification is used to conduct fine lithology division for each stratigraphic section. Establish a regional lithology feature standard library, which contains typical feature combinations of morphological features, mineral composition, and gas logging ratios of different lithology types; then match the cuttings features obtained during the actual drilling process with the standard library to identify the most similar lithology type; for cases where multiple features are crossed or inconsistent, use a weight decision algorithm to assign weights according to the reliability and indicative significance of each feature, and comprehensively judge the lithology attribution. Through this multi-feature comprehensive identification method, the preliminary stratigraphic classification results are refined into more detailed lithology units. For example, the rough "sandstone section" is refined into "quartz sandstone", "feldspar sandstone", "lithic sandstone", etc.; the "carbonate rock section" is refined into "limestone", "dolomite", "marl", etc. This refinement not only considers the rock type but also includes characteristic information such as sedimentary structure and genetic type. Integrate the fine lithology division results with the previous stratigraphic boundary point set and geological age attribution information to form a complete stratigraphic classification map. This map takes depth as the vertical axis and simultaneously shows the stratigraphic boundary depth, geological age, lithology type, and lithology change characteristics, intuitively displaying the vertical distribution and change law of the formation penetrated by the wellbore, and providing a basic geological framework for subsequent oil and gas evaluation, reservoir analysis, and geological structure reconstruction.

[0049] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0050] Extract the total hydrocarbon value, methane / ethane ratio, abnormal gas component content, and cuttings fluorescence intensity parameters from the preprocessed logging data result set to construct a basic index set for hydrocarbon shows; according to the lithology classification results in the stratigraphic layering and classification atlas, perform standardization processing on the basic index set for hydrocarbon shows by selecting different evaluation standard thresholds to generate a lithology-matched normalized hydrocarbon show index; extract information on sudden changes in drilling speed, changes in drilling pressure, and abnormal mud density from the engineering parameters in the preprocessed logging data result set, and combine with acoustic transit time data to form auxiliary hydrocarbon identification characteristic data; use the multi-parameter cross-validation method to perform weighted fusion of the normalized hydrocarbon show index and the auxiliary hydrocarbon identification characteristic data, calculate the comprehensive evaluation index value of hydrocarbon-bearing layers, and establish a comprehensive evaluation index curve for hydrocarbon-bearing layers; according to the comprehensive evaluation index curve for hydrocarbon-bearing layers, combine with the lithology and sedimentary environment information in the stratigraphic layering and classification atlas to identify the positions of potential hydrocarbon-bearing layer segments and classify them to form a preliminary result of hydrocarbon-bearing layer division; calculate the index integral value and effective thickness of each hydrocarbon-bearing layer segment in the preliminary result of hydrocarbon-bearing layer division, and combine with regional production capacity data to predict production capacity potential to form an evaluation result table for hydrocarbon-bearing layers.

[0051] Specifically, extract the total hydrocarbon value, methane / ethane ratio, abnormal gas component content, and cuttings fluorescence intensity parameters from the preprocessed logging data result set to construct a basic index set for hydrocarbon shows. The total hydrocarbon value refers to the total content of all hydrocarbons detected by a gas detector, with the unit of parts per million (ppm); the methane / ethane ratio is the ratio of methane content to ethane content, a dimensionless number, reflecting the maturity and source of hydrocarbon gases; the abnormal gas component content refers to the content of non-hydrocarbon gases such as hydrogen, carbon dioxide, and hydrogen sulfide, with the same unit of ppm; the cuttings fluorescence intensity parameter refers to the fluorescence intensity value generated by cuttings under ultraviolet light irradiation, with the unit of relative fluorescence unit (RFU). These parameters are directly extracted from the corresponding data columns in the preprocessed logging data result set and integrated according to the depth sequence to form a basic index set for hydrocarbon shows.

[0052] According to the lithology classification results in the stratigraphic classification atlas, different evaluation standard thresholds are selected for the basic index set of hydrocarbon shows for standardization processing to generate a normalized hydrocarbon show index matching the lithology. Since the background values of different lithologies (such as sandstone, mudstone, carbonate rock, etc.) vary greatly, it is necessary to set different evaluation criteria for different lithologies. The standardization process uses the interval mapping method, sets specific background values and anomaly value thresholds for each lithology type, maps the original parameter values into the 0-1 interval, and generates a dimensionless normalized hydrocarbon show index. For example, the background value of total hydrocarbons in sandstone is 500 ppm and the anomaly value is 5000 ppm, while the background value of total hydrocarbons in mudstone is 800 ppm and the anomaly value is 8000 ppm. The original total hydrocarbon value is converted into a normalized index value between 0 and 1 through linear mapping. Information on sudden changes in drilling rate, changes in drilling pressure, and abnormal mud density is extracted from the engineering parameters in the preprocessed logging data set, and combined with the acoustic transit time data to form auxiliary hydrocarbon identification characteristic data. A sudden change in drilling rate refers to a significant change in the drilling rate within a short distance, with the unit of (meter / hour) / meter; a change in drilling pressure refers to the fluctuation range of the drilling pressure during stable drilling, with the unit of kN / m; the abnormal mud density information refers to the percentage change of the mud density relative to the reference value; the acoustic transit time data refers to the propagation time of sound waves through the rock formation, with the unit of microseconds / foot. These parameters can reflect the physical properties of the reservoir and fluid properties from the side and provide an auxiliary basis for hydrocarbon layer identification.

[0053] The normalized hydrocarbon show index and the auxiliary hydrocarbon identification characteristic data are weighted and fused through the multi-parameter cross-validation method to calculate the comprehensive evaluation index value of the hydrocarbon layer and establish a comprehensive evaluation index curve of the hydrocarbon layer. The multi-parameter cross-validation method is a data fusion technology based on mutual verification between parameters, which improves the recognition accuracy by examining the co-variation characteristics of multiple parameters. In the implementation process, the correlation matrix between parameters is calculated to determine the mutual support relationship between parameters, and then the initial weights are set according to the reliability and sensitivity of the parameters. Then, the weights are dynamically adjusted in a cross-validation manner so that the comprehensive evaluation index after multi-parameter fusion can reflect the characteristics of the hydrocarbon layer to the greatest extent. Finally, the parameters are weighted and summed according to the determined weights to obtain the comprehensive evaluation index value reflecting the hydrocarbon potential at each depth point, and a continuous comprehensive evaluation index curve of the hydrocarbon layer is constructed.

[0054] Based on the comprehensive evaluation index curve of the oil and gas reservoir and combining the lithology and sedimentary environment information in the stratigraphic classification atlas, identify the location of potential oil and gas reservoir sections and classify them into grades to form the preliminary division result of the oil and gas reservoir. Set the threshold of the comprehensive evaluation index, and mark the continuous paragraphs exceeding the threshold as potential oil and gas reservoirs; then screen them in combination with lithology conditions (such as porous sandstone, carbonate rock reservoirs, etc.) and sedimentary environment characteristics (such as delta front, shallow marine shelf, etc.), and exclude the high-index paragraphs that do not meet the reservoir conditions; divide the identified oil and gas reservoir sections into four grades of excellent, good, medium, and poor according to the level of the comprehensive evaluation index to form the preliminary division result of the oil and gas reservoir. Calculate the index integral value and effective thickness of each oil and gas reservoir section in the preliminary division result of the oil and gas reservoir, and combine the regional production capacity data to predict the production capacity potential to form the oil and gas reservoir evaluation result table. The index integral value is a comprehensive index to measure the quality of the oil and gas reservoir, representing the intensity and scale of the enrichment degree of the oil and gas reservoir. Its calculation formula is as follows:

[0055]

[0056] Among them, represents the index integral value of the oil and gas reservoir section, and represent the top and bottom depths of the oil and gas reservoir respectively, represents the weight coefficient at depth z, which is related to lithology and porosity, represents the comprehensive evaluation index value at depth z, represents the physical property adjustment coefficient at depth z, which is related to permeability and saturation, and dz represents the depth integration microelement.

[0057] The calculation of the effective thickness of the oil and gas reservoir takes into account the limitation of the index threshold, and the formula is as follows:

[0058]

[0059] Among them, represents the effective thickness of the oil and gas reservoir, represents the thickness of the kth depth segment, is the step function, when is greater than the threshold it takes the value of 1, otherwise it is 0, is the effectiveness coefficient related to porosity, is the flow capacity coefficient related to water saturation, and N represents the total number of depth segments in the oil and gas reservoir. Furthermore, collect and organize the production capacity database of the developed oil and gas wells in the region, including production data such as daily oil production, daily gas production, and water cut of each well, as well as corresponding reservoir characteristic parameters, such as effective thickness, average porosity, average permeability, oil and gas saturation, etc. These data are sourced from the test data, production history records, and reservoir evaluation results of the existing wells in the region, forming a basic dataset containing the corresponding relationship between production capacity and reservoir characteristics. Conduct statistical analysis and processing on the data to identify the correlation between production capacity and each reservoir parameter. Through correlation analysis, determine the key parameters that have the most significant impact on production capacity. For example, effective thickness, permeability, and oil and gas saturation are usually positively correlated with production capacity, while water saturation is negatively correlated with production capacity. Then, use statistical or machine learning methods such as multiple regression analysis and neural networks to construct a production capacity prediction model, which can predict the potential production capacity level based on reservoir characteristic parameters. Subsequently, input the characteristic parameters of each oil and gas layer segment in the preliminary division result of the target well's oil and gas layer into the production capacity prediction model to calculate production capacity indicators such as the expected daily oil production and daily gas production of each oil and gas layer segment. Consider key factors such as the comprehensive evaluation index of the oil and gas layer, effective thickness, and formation pressure during the prediction process, and calibrate in combination with the regional production capacity distribution law to ensure the rationality and reliability of the prediction results. Conduct uncertainty analysis on the prediction results to evaluate the confidence interval and risk level of the predicted values. Usually, use probability statistics methods to calculate the production capacity prediction range under different confidence levels, such as P10 (optimistic prediction), P50 (median prediction), and P90 (conservative prediction) values, so as to comprehensively reflect the uncertainty characteristics of production capacity prediction. Integrate the preliminary division result of the oil and gas layer with the production capacity potential prediction result to compile an oil and gas layer evaluation result table. The content of the result table includes information such as the depth range, thickness, lithological characteristics, oil and gas show intensity, comprehensive evaluation index, quality grade, predicted production capacity parameters and confidence interval, and development suggestions of the oil and gas layer.

[0060] = In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0061] By performing statistical calculations on the gas logging parameters, drilling engineering parameters, and mud performance parameters in the preprocessed logging data result set, determining the parameter fluctuation range under normal drilling conditions, and establishing a drilling parameter reference file; extracting the latest drilling parameters from the preprocessed logging data result set, comparing and analyzing them with the drilling parameter reference file, calculating the parameter deviation magnitude, and generating an anomaly index matrix; constructing a drilling anomaly feature library according to the characteristic patterns of key anomaly types such as well kick, lost circulation, stuck pipe, and sudden formation pressure change, matching and comparing the anomaly index matrix with the drilling anomaly feature library to identify the locations of potential drilling risk points; combining the reservoir information in the reservoir evaluation result table, conducting a correlation analysis on the drilling risk points, evaluating the abnormal development trend and severity, classifying the drilling risks, and forming hierarchical risk signals; by analyzing historical drilling anomaly handling cases, generating targeted treatment suggestion plans for the hierarchical risk signals in combination with the current drilling environment and reservoir characteristics; integrating the hierarchical risk signals and treatment suggestion plans into the drilling decision-making push system, automatically triggering the warning process, accurately pushing warning information to the drilling site engineers, and establishing a drilling risk warning mechanism.

[0062] Specifically, by performing statistical calculations on the gas logging parameters, drilling engineering parameters, and mud performance parameters in the preprocessed logging data result set, determining the parameter fluctuation range under normal drilling conditions, and establishing a drilling parameter reference file. The gas logging parameters include the contents and ratios of hydrocarbon gases such as methane, ethane, and propane; the drilling engineering parameters include physical quantities reflecting the drilling state such as drilling speed, drilling pressure, torque, and rotational speed; the mud performance parameters include indicators reflecting the state of the circulation system such as mud density, viscosity, shear force, and water loss. Conduct statistical analysis on the data of these parameters during the historical stable drilling stage, calculate statistical quantities such as the average value, standard deviation, and coefficient of variation of each parameter, determine the normal fluctuation range of each parameter, and form a drilling parameter reference file as a reference benchmark for anomaly identification. Extract the latest drilling parameters from the preprocessed logging data result set, compare and analyze them with the drilling parameter reference file, calculate the parameter deviation magnitude, and generate an anomaly index matrix. Specifically, extract the real-time parameters of the current drilling state in the form of a sliding time window (usually 10 - 30 minutes), compare these parameters with the normal range of the corresponding parameters in the reference file, and calculate the deviation magnitude of each parameter. The deviation magnitude is usually calculated using a standardization method, that is, (measured value - reference value) / standard deviation, indicating the degree to which the current parameter deviates from the normal range. Organize the deviation magnitudes of all parameters into a matrix form, with each row representing a time point and each column representing a parameter, forming an anomaly index matrix to intuitively reflect the change trend and abnormal state of each parameter over time.

[0063] According to the characteristic patterns of key abnormal types such as well kick, lost circulation, stuck pipe, and sudden formation pressure change, a drilling anomaly feature library is constructed. The anomaly degree index matrix is matched and compared with the drilling anomaly feature library to identify the locations of potential drilling risk points. A well kick refers to the uncontrolled entry of formation fluids into the wellbore, usually characterized by an increase in mud volume, a decrease in mud density, an increase in the total hydrocarbon value of gas logging, etc.; lost circulation refers to the entry of drilling fluid into the formation, characterized by a decrease in mud return volume and a drop in pump pressure; stuck pipe refers to the obstruction of the drill string in the wellbore, manifested as an increase in torque, fluctuations in weight on bit, and a decrease in drilling speed; sudden formation pressure change is manifested as phenomena such as changes in drilling speed, sudden increase in gas content, and abnormal weight on bit. For these typical abnormal types, their characteristic patterns are extracted through expert experience and machine learning methods to construct a drilling anomaly feature library containing feature vectors of different abnormal types. Then, pattern recognition algorithms (such as support vector machines, neural networks, etc.) are used to calculate the similarity and perform matching comparison between the real-time generated anomaly degree index matrix and the patterns in the feature library. When the similarity exceeds the set threshold, the locations of potential drilling risk points are identified.

[0064] Combined with the reservoir information in the reservoir evaluation result table, a correlation analysis is carried out on the drilling risk points to evaluate the abnormal development trend and severity, classify the drilling risks, and form a graded risk signal. The reservoir information includes key data such as formation, lithology, porosity and permeability characteristics, fluid type, and pressure system. This information is of great significance for understanding the background and causes of drilling anomalies. During the correlation analysis process, the identified risk points are compared with the reservoirs encountered in terms of spatial location to analyze their correlation. At the same time, the change rate, duration, and influence range of the abnormal indicators are considered to comprehensively evaluate the development trend and severity of the anomaly. According to the evaluation results, the drilling risks are divided into four levels: emergency (red), severe (orange), moderate (yellow), and minor (blue), forming a graded risk signal to provide an intuitive reference for subsequent decision-making. By analyzing historical drilling anomaly treatment cases, targeted treatment suggestion plans are generated for the graded risk signals in combination with the current drilling environment and reservoir characteristics. Historical drilling anomaly treatment cases are valuable experience resources, including the treatment methods and effect evaluations of various drilling anomalies under different regions and formation conditions. During the implementation process, a case database is constructed to record key information such as abnormal type, severity, geological background, treatment method, and effect. Then, based on similar case retrieval technology, historical cases similar to the current situation are searched from the case database. Next, in combination with the current drilling environment (such as well depth, temperature, pressure, etc.) and reservoir characteristics (such as lithology, porosity, pressure system, etc.), the historical treatment methods are adjusted adaptively to form specific treatment suggestions for the current risk, including parameter adjustment plans, emergency operation steps, and prevention and control measures, etc.

[0065] Integrate the hierarchical risk signals and treatment suggestion schemes into the drilling decision-making push system, automatically trigger the early warning process, accurately push the early warning information to the drilling site engineers, and establish a drilling risk early warning mechanism. The drilling decision-making push system is a platform integrating data analysis, risk assessment, and information distribution functions, which can receive hierarchical risk signals and treatment suggestions in real time and automatically trigger the corresponding-level early warning process according to the risk level. Set differentiated response strategies for different risk levels: for emergency risks, immediately push the early warning information to the mobile terminals of all key decision-makers and initiate the emergency response process; for serious risks, push the early warning to the drilling engineers and geological engineers, requiring close monitoring and preparation of response measures; for medium and minor risks, push reminder information and suggest strengthening parameter monitoring. The content of the early warning information includes key information such as risk type, location, level, development trend, possible causes, and treatment suggestions.

[0066] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0067] Extract the acoustic time difference, cuttings pore characteristics, and gas logging hydrocarbon component ratio data related to reservoir physical properties from the preprocessed mud logging data result set, and combine the oil and gas show information in the oil and gas reservoir evaluation result table to construct a reservoir physical property parameter set; perform calculation and processing on the reservoir physical property parameter set through the regional conversion function, convert the mud logging physical property indicators into reservoir porosity, permeability, and oil and gas saturation values, and generate the initial result of quantitative reservoir evaluation; combine the abnormal identification information in the drilling risk early warning mechanism, evaluate the degree of reservoir damage during the drilling process, and correct the initial result of quantitative reservoir evaluation to form a reservoir physical property correction table; analyze the reservoir heterogeneity factors based on the oil and gas reservoir intervals in the oil and gas reservoir evaluation result table and the reservoir physical property correction table, identify the positions of the dominant seepage channels and fluid barrier layers, and construct a reservoir seepage characteristic map; based on the reservoir seepage characteristic map, calculate and evaluate the vertical continuity and horizontal distribution range of the reservoir, determine the effective reservoir thickness and distribution range, and generate reservoir geometric structure data; integrate the reservoir physical property correction table, the reservoir seepage characteristic map, and the reservoir geometric structure data, conduct a comprehensive evaluation of the reservoir quality, and compile a comprehensive reservoir evaluation report according to the reservoir depth, thickness, physical properties, heterogeneity characteristics, and quality level.

[0068] Specifically, extract the acoustic time difference, cuttings pore characteristics, and gas logging hydrocarbon component ratio data related to reservoir physical properties from the preprocessed logging data result set. Combine these with the oil and gas show information in the oil and gas reservoir evaluation result table to construct a reservoir physical property parameter set. The acoustic time difference refers to the propagation time of sound waves through rocks, with the unit of microseconds per foot, reflecting the compaction degree and porosity characteristics of rocks; the cuttings pore characteristics include the pore morphology, size, and distribution characteristics of cuttings, which are obtained through microscopic observation and image analysis; the gas logging hydrocarbon component ratio data refers to the content ratio of hydrocarbon gases with different carbon numbers (such as methane, ethane, propane, etc.), reflecting the source and maturity of hydrocarbons. Deeply match and integrate these parameters extracted from the preprocessed logging data result set with the oil and gas show information (such as total hydrocarbon content, gas logging anomaly amplitude, cuttings fluorescence characteristics, etc.) in the oil and gas reservoir evaluation result table to construct a reservoir physical property parameter set containing various physical property-related indicators. Calculate and process the reservoir physical property parameter set through a regional conversion function to convert the logging physical property indicators into reservoir porosity, permeability, and hydrocarbon saturation values, generating the initial result of quantitative reservoir evaluation. The regional conversion function is a mathematical model established based on the geological characteristics of a specific region and statistical analysis of drilled well data, which can convert the indirect physical property indicators obtained from logging into key parameters directly reflecting reservoir quality. In practical applications, select the applicable conversion function according to different lithology types. For example, for sandstone reservoirs, calculate the porosity using the time-distance average equation based on the acoustic time difference, while for carbonate reservoirs, use a modified empirical equation. Permeability is usually estimated from parameters such as porosity, cuttings grain size, and sorting through empirical formulas, and hydrocarbon saturation is mainly evaluated based on the gas logging hydrocarbon component ratio and cuttings fluorescence characteristics. Through the calculation and processing of these regional conversion functions, convert various logging physical property indicators into quantitative reservoir parameters, including the porosity (percentage), permeability (millidarcy), and hydrocarbon saturation (percentage) at each depth point, forming the initial result of quantitative reservoir evaluation.

[0069] Combine the anomaly identification information in the drilling risk warning mechanism to evaluate the degree of reservoir damage during the drilling process and correct the initial result of quantitative reservoir evaluation to form a reservoir physical property correction table. Various abnormal conditions during the drilling process (such as lost circulation, well kick, drilling fluid invasion, etc.) cause the measured values of reservoir physical property parameters to deviate from the true values, and corresponding corrections are required. Extract the anomaly identification information from the drilling risk warning mechanism, including the anomaly type, occurrence depth, duration, and severity, etc.; then establish a damage assessment model according to the influence characteristics of different anomaly types on the reservoir to quantify the influence degree of various anomalies on the measurement of reservoir porosity, permeability, and saturation; consider these influencing factors in the initial result of quantitative reservoir evaluation and perform parameter correction to form a reservoir physical property correction table closer to the true formation conditions.

[0070] According to the reservoir intervals in the oil and gas reservoir evaluation results table and the reservoir physical property correction table, analyze the reservoir heterogeneity factors, identify the positions of the dominant seepage channels and fluid barrier layers, and construct a reservoir seepage characteristics map. Reservoir heterogeneity refers to the variation characteristics of physical property parameters in the spatial distribution of the reservoir, including vertical and horizontal heterogeneity. During the analysis process, identify the lithological changes within the oil and gas reservoir, including factors affecting fluid flow such as shale interlayers and tight bands; then, by comparing the permeability differences of each interval, identify the intervals with abnormally high permeability (dominant seepage channels) and the intervals with abnormally low permeability (fluid barrier layers); comprehensively consider the lithological changes, fracture development degree, and physical property distribution characteristics to construct a reservoir seepage characteristics map reflecting the internal fluid flow path and blocking positions of the reservoir, providing a basis for the design of the reservoir development plan.

[0071] Based on the reservoir seepage characteristics map, calculate and evaluate the vertical continuity and horizontal distribution range of the reservoir, determine the effective reservoir thickness and distribution range, and generate reservoir geometric structure data. The evaluation of vertical continuity mainly examines the connectivity of the reservoir in the vertical direction, and it is necessary to identify and exclude the influence of the barrier layer and calculate the thickness of the continuous reservoir interval; the evaluation of the horizontal distribution range combines the regional geological understanding and sedimentary environment analysis to infer the extension law and boundary position of the reservoir in the horizontal direction. By synthesizing these analysis results, determine the thickness (considering the lower limit requirements of physical properties, such as porosity > 8%, permeability > 1 millidarcy, etc.) and distribution range of the effective reservoir in each interval, and generate reservoir geometric structure data containing the three-dimensional spatial information of the reservoir.

[0072] Integrate the reservoir physical property correction table, the reservoir seepage characteristics map, and the reservoir geometric structure data, conduct a comprehensive evaluation of the reservoir quality, and prepare a comprehensive reservoir evaluation report according to the reservoir depth, thickness, physical properties, heterogeneity characteristics, and quality grades. The comprehensive evaluation of reservoir quality is a comprehensive assessment of the development value of the reservoir, considering multiple factors, including reservoir depth (affecting development costs), effective thickness (affecting production capacity scale), physical property parameters (affecting production efficiency), and heterogeneity characteristics (affecting development difficulty and recovery rate). According to the comprehensive scoring results, divide the reservoir into four grades: high-quality, good, medium, and poor, and give detailed characteristic descriptions and development suggestions for each grade of reservoir intervals. Finally, systematically organize all the evaluation results and compile them into a comprehensive reservoir evaluation report containing key information such as reservoir location, geometric characteristics, physical property distribution, flow characteristics, and quality grades.

[0073] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0074] Based on the key information of strata and reservoirs in the stratigraphic classification atlas, oil and gas reservoir evaluation result table, and comprehensive reservoir evaluation report, a one-dimensional geological profile model of the wellbore is constructed to display the vertical distribution characteristics of strata. According to the one-dimensional geological profile model of the wellbore and combined with regional tectonic background data, the single-well geological information is horizontally extended through a spatial interpolation algorithm to form a three-dimensional geological framework. Using the reservoir physical property distribution and heterogeneity characteristic data in the comprehensive reservoir evaluation report, the reservoir structure in the three-dimensional geological framework is finely characterized to generate a fine reservoir structure map. By analyzing the oil and gas show information and pressure data in the oil and gas reservoir evaluation result table, the position of the oil-gas-water interface is determined, and the fluid distribution boundary is marked on the fine reservoir structure map to establish a fluid distribution map. Based on the reservoir physical property and connectivity data in the oil and gas reservoir evaluation result table and the comprehensive reservoir evaluation report, the oil and gas migration channels and enrichment areas are predicted and calculated to generate an oil and gas enrichment distribution map. Integrate the three-dimensional geological framework, fine reservoir structure map, fluid distribution map, and oil and gas enrichment distribution map to comprehensively evaluate the resource potential and production capacity expectation of the target area and form an oil and gas reservoir description result.

[0075] Specifically, the realization process of geological structure reconstruction and prediction of oil and gas enrichment areas is based on the key information of strata and reservoirs in the stratigraphic classification atlas, oil and gas reservoir evaluation result table, and comprehensive reservoir evaluation report, and a one-dimensional geological profile model of the wellbore is constructed to display the vertical distribution characteristics of strata. Specifically, extract the formation interface depth, lithology distribution, and geological age information from the stratigraphic classification atlas; extract the oil and gas reservoir position, thickness, and quality grade from the oil and gas reservoir evaluation result table; extract the distribution of reservoir physical property parameters from the comprehensive reservoir evaluation report. Then integrate this information in depth order to generate a one-dimensional geological profile model along the wellbore direction, intuitively displaying each formation unit and its characteristics passed through by the wellbore, providing basic data for subsequent three-dimensional modeling. According to the one-dimensional geological profile model of the wellbore and combined with regional tectonic background data, the single-well geological information is horizontally extended through a spatial interpolation algorithm to form a three-dimensional geological framework. The spatial interpolation algorithm is a mathematical method for extending known point data to a spatial area, and commonly used ones include Kriging interpolation method, inverse distance weighted method, etc. During the implementation process, establish a preliminary geological conceptual model in combination with regional tectonic background data (such as tectonic morphology, fault distribution, sedimentary facies belt, etc.); then use the key formation interface points in the one-dimensional geological profile model of the wellbore as known data points; then adopt a suitable spatial interpolation algorithm and perform constrained interpolation calculations considering geological laws to horizontally extend the single-well formation and structure information to form a three-dimensional geological framework model containing main formation interfaces and tectonic characteristics.

[0076] Using the reservoir physical property distribution and heterogeneity characteristic data in the comprehensive reservoir evaluation report, the reservoir structure in the 3D geological framework is finely characterized to generate a fine reservoir structure map. Extract the lithology distribution, porosity, permeability variation characteristics and heterogeneity description of the reservoir from the comprehensive reservoir evaluation report; then fill these reservoir physical property data into the geological framework according to the 3D spatial coordinate relationship; then use geostatistical methods to simulate the spatial distribution of the physical property data, consider the lithology change law and physical property gradual change characteristics, and finely characterize the reservoir structure; generate a fine reservoir structure map reflecting the internal structure and physical property distribution of the reservoir, providing a reservoir basis for subsequent fluid distribution analysis. By analyzing the oil and gas show information and pressure data in the oil and gas layer evaluation result table, determine the position of the oil-gas-water interface, mark the fluid distribution boundary in the fine reservoir structure map, and establish a fluid distribution map. The oil-gas-water interface is the interface between oil and gas and formation water in the oil and gas reservoir, and is a key parameter determining the scope of the oil and gas reservoir. In the determination process, extract the pressure data at different depths from the oil and gas layer evaluation result table and calculate the fluid pressure gradient; then determine the possible position of the oil-gas-water interface according to the pressure gradient change characteristics and oil and gas show information; then consider the reservoir heterogeneity and capillary force effect to correct the interface; mark the determined interface position on the fine reservoir structure map, divide the oil area, gas area and water area, and establish a 3D fluid distribution map.

[0077] Based on the reservoir physical property and connectivity data in the oil and gas layer evaluation result table and the comprehensive reservoir evaluation report, predict and calculate the oil and gas migration channels and enrichment areas to generate an oil and gas enrichment distribution map. The oil and gas migration channel refers to the migration path of oil and gas from the source rock to the reservoir, and the enrichment area refers to the area where oil and gas preferentially accumulate in the reservoir. During the prediction process, analyze the permeability distribution and connectivity characteristics of the reservoir to identify possible dominant migration channels; then combine the structural position and the oil-gas-water interface relationship to determine the structural high points and the dominant areas where oil and gas may accumulate; then use the principles of fluid mechanics, consider gravity differentiation and buoyancy effects, and calculate the possible distribution law of oil and gas in the reservoir; generate an oil and gas enrichment distribution map reflecting the oil and gas enrichment degree and distribution range.

[0078] Integrate the 3D geological framework, fine reservoir structure map, fluid distribution map and oil and gas enrichment distribution map, comprehensively evaluate the resource potential and production capacity expectation of the target area, and form the oil and gas reservoir description results. During the integration process, overlay various maps according to a unified coordinate system to form a complete oil and gas reservoir spatial structure model; then calculate the oil and gas resource volume in the area based on the reservoir volume method; then predict the potential production capacity level according to the reservoir physical properties and fluid characteristics; systematically organize all information to generate comprehensive oil and gas reservoir description results including geological characteristics, reservoir properties, fluid distribution and resource evaluation, providing a scientific basis for oil and gas field development decision-making.

[0079] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for intelligent processing of logging data, characterized in that: include: The drilling process data is collected and preprocessed through multi-source logging equipment to form a logging data preprocessing result set; According to the logging data preprocessing result set, the formation characteristics are identified and classified to generate a formation stratification classification map; including calculating the coefficient of variation and the first-order derivative of the natural gamma curve in the logging data preprocessing result set, identifying the curve mutation points, and forming a formation boundary candidate point set; combining the mutation characteristics of the drilling rate curve, screening and verifying the formation boundary candidate point set, determining the preliminary formation boundary depth point, and constructing a preliminary formation boundary point set; optimizing the preliminary formation boundary point set through the Poisson change point detection algorithm, excluding non-geological boundary points, and generating an accurate formation boundary point set; extracting the gas measurement component ratio, drill cuttings fluorescence characteristics and natural gamma value in each formation segment from the logging data preprocessing result set, and constructing a formation feature vector set; Based on the similarity calculation results of the stratigraphic feature vector set and the regional standard stratigraphic data, the geological age attribution of each stratigraphic segment is determined to form a stratigraphic classification result; according to the drill cuttings morphological characteristics, mineral composition and gas logging hydrocarbon component ratio, the lithological segment of each stratigraphic segment in the stratigraphic classification result is subdivided, including using multi-feature comprehensive identification to subdivide the lithological segment of each stratigraphic segment, establishing a regional lithological feature standard library, matching the drill cuttings characteristics obtained in the actual drilling process with the standard library, identifying the most similar lithological type, using a weighted decision algorithm, assigning weights according to the reliability and indicative significance of each feature, comprehensively judging the lithological attribution, and integrating to form a stratigraphic stratification classification map; Based on the stratigraphic classification atlas and the logging data preprocessing result set, a comprehensive analysis of oil and gas parameters is performed to form an oil and gas layer evaluation result table; Using the oil and gas layer evaluation results table and the logging data preprocessing result set, abnormal drilling conditions are identified and risk levels are classified, and a drilling risk early warning mechanism is established; According to the oil and gas layer evaluation results table and drilling risk early warning mechanism, the reservoir physical property parameters are intelligently converted and evaluated to form a comprehensive reservoir evaluation report; The stratigraphic classification map, oil and gas layer evaluation results table and comprehensive reservoir evaluation report are integrated to carry out geological structure reconstruction and oil and gas enrichment area prediction, and generate oil and gas reservoir description results.

2. The method for intelligent processing of logging data according to claim 1, characterized in that: The drilling process data is collected and preprocessed by multi-source logging equipment to form a logging data preprocessing result set, including: The original logging parameters are collected through gas detectors, drill cuttings fluorescence detectors and gamma ray detectors to obtain the gas components of drilling fluid, drill cuttings fluorescence values ​​and natural gamma values, and generate the original logging data stream; Obtain drilling pressure, drilling speed, torque and pump pressure parameters from the drilling engineering monitoring system, and combine them with the lithology description data from geological coring analysis to form an auxiliary logging parameter set; Performing time stamp alignment and depth correction processing on the original logging data stream to eliminate hysteresis effect and depth deviation, and constructing time-depth consistent logging data; The time-depth consistent logging data and the auxiliary logging parameter set are resampled by using a piecewise cubic spline interpolation method to solve the problem of inconsistent sampling frequencies of data from different sources and generate a unified frequency data set; The wavelet transform filtering technology is used to suppress noise and remove abnormal points from the unified frequency data set, thereby improving the data signal-to-noise ratio and forming high-quality logging basic data; For the missing segments in the high-quality logging basic data, the data is repaired and filled through the associated parameter mapping method, and a time-depth dual index system is established to form a logging data preprocessing result set.

3. The method for intelligent processing of logging data according to claim 2, characterized in that: Based on the stratigraphic classification atlas and the logging data preprocessing result set, the oil and gas parameters are comprehensively analyzed to form an oil and gas layer evaluation result table, including: Extracting total hydrocarbon value, methane / ethane ratio, abnormal gas component content and drill cuttings fluorescence intensity parameters from the logging data preprocessing result set to construct a basic index set for oil and gas display; According to the lithology classification result in the stratigraphic stratification classification atlas, different evaluation standard thresholds are selected for the oil and gas display basic index set for standardization processing to generate a lithology-matched normalized oil and gas display index; Extracting the information of drilling speed mutation, drilling pressure change and mud density abnormality from the engineering parameters in the logging data preprocessing result set, and combining with the acoustic wave time difference data to form auxiliary oil and gas identification feature data; The normalized oil and gas display index and the auxiliary oil and gas identification feature data are weighted and fused by a multi-parameter interactive verification method to calculate the comprehensive evaluation index value of the oil and gas layer and establish a comprehensive evaluation index curve of the oil and gas layer; According to the comprehensive evaluation index curve of the oil and gas layer, combined with the lithology and sedimentary environment information in the stratigraphic classification atlas, the potential oil and gas layer segments are identified and graded to form a preliminary oil and gas layer classification result; The index integral value and effective thickness of each oil and gas layer segment in the preliminary oil and gas layer division result are calculated, and the production capacity potential is predicted in combination with the regional production capacity data to form an oil and gas layer evaluation result table.

4. The method for intelligent processing of logging data according to claim 3, characterized in that: The method of using the oil and gas layer evaluation result table and the logging data preprocessing result set to identify abnormal drilling conditions and classify risk levels, and establish a drilling risk early warning mechanism, includes: By performing statistical calculations on the gas logging parameters, drilling engineering parameters and mud performance parameters in the logging data preprocessing result set, the parameter fluctuation range under normal drilling conditions is determined, and a drilling parameter benchmark file is established; Extract the latest drilling parameters from the logging data preprocessing result set, compare and analyze with the drilling parameter benchmark file, calculate the parameter deviation value, and generate an abnormality index matrix; According to the characteristic patterns of key abnormal types such as kick, lost circulation, stuck pipe and formation pressure mutation, a drilling abnormality feature library is constructed, and the abnormality index matrix is ​​matched and compared with the drilling abnormality feature library to identify the potential drilling risk point locations; Combined with the oil and gas layer information in the oil and gas layer evaluation results table, the drilling risk points are analyzed for correlation, the abnormal development trend and severity are evaluated, the drilling risks are graded, and a graded risk signal is formed; By analyzing historical drilling anomaly handling cases, targeted treatment suggestions are generated for the graded risk signals, combined with the current drilling environment and oil and gas layer characteristics; The graded risk signals and treatment suggestions are integrated into the drilling decision push system to automatically trigger the early warning process, accurately push early warning information to drilling site engineers, and establish a drilling risk early warning mechanism.

5. The method for intelligent processing of logging data according to claim 4, characterized in that: According to the oil and gas layer evaluation results table and the drilling risk early warning mechanism, the reservoir physical property parameters are intelligently converted and evaluated to form a comprehensive reservoir evaluation report, including: Extracting acoustic wave time difference, cuttings pore characteristics and gas logging hydrocarbon component ratio data related to reservoir physical properties from the logging data preprocessing result set, and combining with the oil and gas display information in the oil and gas layer evaluation result table to construct a reservoir physical property parameter set; The reservoir physical property parameter set is calculated and processed by a regional conversion function, and the logging physical property index is converted into reservoir porosity, permeability and oil and gas saturation values ​​to generate an initial result of quantitative reservoir evaluation; Combined with the abnormal identification information in the drilling risk early warning mechanism, the damage degree of the drilling process to the reservoir is evaluated, and the initial result of the quantitative reservoir evaluation is corrected to form a reservoir property correction table; According to the oil and gas layer segments in the oil and gas layer evaluation results table and the reservoir physical property correction table, the reservoir heterogeneity factors are analyzed, the positions of the dominant seepage channels and fluid barrier layers are identified, and a reservoir seepage characteristic diagram is constructed; Based on the reservoir seepage characteristic diagram, the vertical continuity and lateral distribution range of the reservoir are calculated and evaluated to determine the effective reservoir thickness and distribution range, and generate reservoir geometry data; The reservoir physical property correction table, reservoir seepage characteristic diagram and reservoir geometric structure data are integrated to conduct a comprehensive reservoir quality rating, and a comprehensive reservoir evaluation report is compiled according to reservoir depth, thickness, physical properties, heterogeneity characteristics and quality grade.

6. The method for intelligent processing of logging data according to claim 5, characterized in that: The above-mentioned integration of the stratigraphic classification atlas, oil and gas layer evaluation results table and comprehensive reservoir evaluation report, the reconstruction of geological structure and prediction of oil and gas enrichment areas, and the generation of oil and gas reservoir description results include: Based on the stratigraphic classification atlas, the oil and gas layer evaluation results table and the stratigraphic and reservoir key information in the comprehensive reservoir evaluation report, a one-dimensional geological profile model of the wellbore is constructed to display the vertical distribution characteristics of the stratigraphic formation; According to the one-dimensional geological profile model of the wellbore combined with the regional structural background data, the geological information of the single well is horizontally extended through a spatial interpolation algorithm to form a three-dimensional geological skeleton; Using the reservoir physical property distribution and heterogeneity characteristic data in the comprehensive reservoir evaluation report, the reservoir structure in the three-dimensional geological framework is finely depicted to generate a reservoir fine structure map; By analyzing the oil and gas display information and pressure data in the oil and gas layer evaluation results table, the oil and gas-water interface position is determined, the fluid distribution boundary is calibrated in the reservoir fine structure map, and a fluid distribution map is established; Based on the reservoir physical properties and connectivity data in the oil and gas layer evaluation results table and the comprehensive reservoir evaluation report, the oil and gas migration channels and enrichment areas are predicted and calculated to generate an oil and gas enrichment distribution map; By integrating the three-dimensional geological framework, reservoir fine structure map, fluid distribution map and oil and gas enrichment distribution map, the resource potential and production capacity expectation of the target area are comprehensively evaluated to form oil and gas reservoir description results.

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