Real-time detection and logging comprehensive evaluation method for hydrogen and helium while drilling of helium-containing natural gas reservoir

Abstract

The invention relates to the technical field of oil and gas field exploration and development, in particular to a while-drilling hydrogen and helium real-time detection and logging comprehensive evaluation method for a helium-containing natural gas reservoir. According to the technical scheme, the while-drilling hydrogen and helium real-time detection and logging comprehensive evaluation method for the helium-containing natural gas reservoir comprises the working process of the while-drilling hydrogen and helium real-time detection and logging comprehensive evaluation method; according to the invention, a high-precision hydrogen helium detection mass spectrometer and logging equipment are integrated, through mechanical installation optimization, automatic calibration and pre-drilling simulation test, the detection precision and data timeliness are significantly improved, a helium concentration vertical profile can be obtained in real time, lithologic changes and helium enrichment horizon can be dynamically identified, and the reliability of the system is improved. An instant basis is provided for well drilling parameter adjustment and gas testing interval selection, and the decision-making period is effectively shortened.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field exploration and development technology, and in particular to a method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of well logging in helium-containing natural gas reservoirs. Background Technology

[0002] Helium is a strategic and scarce resource with irreplaceable value in aerospace, nuclear industry and medical fields. my country is highly dependent on imports for helium resources and urgently needs to explore and develop it independently. The Weihe Basin, as a typical helium-rich tectonic basin, has huge potential for helium resources.

[0003] Existing real-time detection and logging-based evaluation methods for hydrogen and helium during drilling mostly rely on chromatographic gas analysis. These methods have long detection cycles, poor real-time performance, and difficulty in capturing the vertical dynamic changes in helium content. This results in a lag in formation lithology identification and helium-bearing strata determination, affecting the timeliness of drilling decisions.

[0004] Existing methods for real-time hydrogen and helium detection and well logging evaluation during drilling largely rely on gas analysis using chromatography. These methods suffer from long detection cycles, poor real-time performance, and difficulty in capturing the vertical dynamic changes in helium content. This leads to delays in formation lithology identification and helium-bearing layer determination, impacting the timeliness of drilling decisions. This solution integrates a high-precision hydrogen and helium detection mass spectrometer with logging equipment. Through optimized mechanical installation, automatic calibration, and pre-drilling simulation testing, it achieves real-time online detection of helium content. The mass spectrometer has a detection limit of 1 ppm. Combined with a mounting bracket designed using 3D modeling software and a laser alignment instrument, it ensures that the equipment's coaxiality error is ≤0.2 mm, significantly improving detection accuracy and data timeliness. It can acquire vertical profiles of helium concentration in real time, dynamically identify lithological changes and helium-enriched layers, providing immediate evidence for drilling parameter adjustments and gas testing layer selection, effectively shortening the decision-making cycle. Summary of the Invention

[0005] To overcome the shortcomings of existing real-time detection and logging evaluation methods for hydrogen and helium while drilling, which rely heavily on gas analysis using chromatography, resulting in long detection cycles, poor real-time performance, and difficulty in capturing vertical dynamic changes in helium content, leading to lags in formation lithology identification and helium occurrence location determination, and affecting the timeliness of drilling decisions.

[0006] The technical solution of this invention is: a method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-containing natural gas reservoirs, comprising the following steps: S11: Integrates a high-precision hydrogen-helium detection mass spectrometer and logging equipment, and completes mechanical installation, automatic calibration and pre-drilling simulation testing; S12: Integrate multi-source logging data to construct a formation parameter inversion model, and perform wellbore trajectory correction, lithology identification, and dynamic determination of fluid properties; S13: Develop a multi-sensor fusion algorithm, establish a feature library of interfering gases, and correct cross-interference of methane, carbon dioxide, etc. on helium detection in real time; S14: Based on the DS evidence theory, a dynamic classification model for helium-bearing reservoirs is constructed by fusing logging curves and detection data, and a reservoir evaluation index is output. S15: Build a dynamic monitoring platform that integrates 3D geological modeling, intelligent alarm and remote expert support functions to assist on-site decision-making; S16: Develop equipment calibration specifications, establish real-time data quality monitoring indicators, and ensure data reliability through cross-validation and anomaly detection; S17: Adjust drilling parameters based on real-time monitoring data, optimize the selection of gas testing zones, and dynamically optimize geological steering and fracturing schemes; S18: Verify the accuracy of the production capacity prediction model through gas testing, assess economic indicators and environmental impact, and complete the technology applicability evaluation; S19: Develop enterprise / industry standards, build an output database, conduct technical training and external services, and promote industrial application.

[0007] Preferably, the integration and pre-commissioning of the drilling monitoring equipment includes the following steps: S21: Select a quadrupole mass spectrometer with a detection limit of 1ppm helium, and configure a dedicated injection port to connect with the drilling fluid circulation system. Test whether the instrument's temperature resistance reaches 150℃ and its shock resistance meets the API RP17B standard. S22: The installation bracket is designed using 3D modeling software, and the coaxiality error between the mass spectrometer and the drill collar is adjusted to ≤0.2mm using a laser alignment instrument to complete the electrical wiring for explosion-proof certification; S23: Deploy a hydrogen-helium dual-element sensor, a gamma-ray detector, and a resistivity array in the drill string assembly. The sensors are arranged at equal intervals of 0.5m and data is synchronously acquired via a CAN bus. S24: Configure a temperature compensation module to establish a sensor drift correction model in an environment of -20℃ to 120℃, and store the error correction coefficients in non-volatile memory; S25: Integrates a three-point automatic calibration device, develops a PLC control program to achieve daily timed calibration, and uploads the calibration data to the ground system after CRC verification; S26: Configure a dynamic diagnostic algorithm to trigger an automatic cleaning program when baseline drift exceeds 5%, using ultrapure nitrogen to purge the gas path system at a flow rate of 1L / min; S27: Establish a data transmission channel from the well to the surface based on the WITSML standard, configure the MQTT protocol to achieve low-latency transmission, and set QoS=2 to ensure the reliable arrival of critical data packets; S28: Develop a data packet parsing module to convert raw binary data into logging curves conforming to the DLIS format, and synchronously record timestamps and well depth information; S29: Designed for intrinsic safety according to GB 3836.1-2010 standard, and tested for air tightness at 1.5 times the rated pressure to determine whether the exhaust fan automatically starts when the methane concentration reaches 5% VOL; S210: Equipped with dual-circuit uninterruptible power supply, the switching time in case of main power failure is ≤20ms, and the backup battery has the power capacity to meet 72 hours of continuous operation. S211: Construct a simulated drilling fluid circulation system in the laboratory, inject helium standard samples of known concentration, and verify the linear response range of the mass spectrometer; S212: Conduct full-process pressure testing to simulate a 100MPa high-pressure environment downhole and check whether the equipment insulation resistance change rate exceeds 10%. S213: Monitoring software designed based on the Qt framework, which displays hydrogen and helium concentration curves, gamma values, and resistivity parameters in real time, and integrates alarm threshold setting function.

[0008] Preferably, the following steps are included when performing dynamic wellbore environment modeling: S31: Time alignment of mass spectrometry data, gamma curves, and resistivity curves during drilling, and uniform sampling intervals to 0.1m well depth using cubic spline interpolation; S32: Perform outlier detection, using the isolated forest algorithm to identify and remove noisy data points exceeding the mean ±3σ, retaining ≥95% of valid data. S33: Combining logging-while-drilling data, the true wellbore trajectory is calculated using the minimum curvature method to correct the azimuth error of the logging curve caused by wellbore curvature. S34: Develop a wellbore environment compensation model, input mud density and well temperature gradient parameters, and output wellbore environment correction coefficients for formation resistivity and sonic transit time. S35: The wavelet transform algorithm is applied to decompose the natural gamma curve into multiple scales, and the location of the formation interface is determined by the modulus maxima detection method. The interface identification accuracy is ≤0.3m. S36: Construct a lithological-electrical cross-plot and verify the sandstone and mudstone interface identification results by combining the neutron-density curve overlay method; S37: Establish a porosity inversion model based on the Monte Carlo algorithm, input sonic transit time and density logging data, and output the porosity distribution probability density function; S38: Develop a permeability neural network prediction model, with porosity and clay content as input layers, 8 neurons in the hidden layer, and output the logarithmic mean of permeability; S39: Construct a PVT phase state analysis model, input formation pressure and temperature data, calculate the phase envelope of the methane-helium mixture, and determine whether the fluid is in a liquid, gas, or critical state; S310: An oil, gas and water identification model is established using Fisher discriminant analysis, with resistivity, neutron porosity and spontaneous potential selected as discriminant factors. S311: Select a known helium-containing layer as the validation set, calculate the root mean square error (RMSE) between the model predictions and the measured values, and require RMSE < 5%; S312: The particle swarm optimization algorithm is used to adjust the model hyperparameters, with the number of iterations set to 200, and the learning factors c1=1.5 and c2=1.7, until the model converges; S313: Develop a wellbore pressure-temperature coupling model, input drilling fluid circulation pressure and geothermal gradient data, and calculate the influence coefficient of wellbore fluid phase change on the detection signal in real time; S314: Configure a dynamic compensation algorithm to automatically adjust the helium concentration detection threshold when the wellbore pressure change rate exceeds 0.5 MPa / min.

[0009] Preferably, the real-time identification and correction of interfering gases includes the following steps: S41: Collect standard gas samples of methane, carbon dioxide, and nitrogen, and establish an infrared spectral feature database; S42: Develop a support vector machine classification model, input the position and intensity parameters of the infrared absorption peak of the gas, and output the gas type identification result; S43: Integrates electrochemical sensors, catalytic combustion sensors, and thermal conductivity sensors, and achieves multi-sensor data fusion through Kalman filtering; S44: Set data fusion weighting coefficients: mass spectrometer data weight 0.6, electrochemical sensor 0.2, catalytic combustion sensor 0.2, to ensure helium detection specificity ≥95%; S45: Construct a methane-helium cross-interference matrix, establish an interference coefficient model through partial least squares regression, and input CH4 concentration to predict the correction amount of He concentration; S46: Develop a dynamic baseline correction program that activates a baseline drift compensation algorithm when a sudden increase in CO2 concentration is detected, with a compensation period of 30 seconds. S47: Set quality control indicators: helium detection signal noise ratio >20dB, methane interference suppression ratio >30dB; S48: Develop an automatic alarm system that triggers a sensor cleaning procedure and records abnormal events when SNR < 15dB or ISR < 25dB. S49: Construct a helium solubility model, input formation pressure and temperature data, calculate the solubility coefficient of helium in methane, and correct the ratio of free gas to dissolved gas; S410: Apply the equation of state to calculate the compressibility factor of the mixed gas, correct the volumetric flow meter measurement value, and ensure that the calculation error of helium volumetric flow rate is <3%; S411: Performs moving average filtering to suppress random noise while preserving trend information; S412: Develop a data compression algorithm that uses wavelet transform for 8-level decomposition, retains the first 3 levels of detail coefficients, and achieves a compression ratio of 4:1. S413: Inject a mixed gas into a simulated wellbore environment in the laboratory to verify whether the measurement error of the calibrated helium concentration is ≤5%; S414: Conduct a 72-hour continuous stability test and record the standard deviation of the helium concentration measurement value every hour.

[0010] As a preferred method, the following steps are included when constructing a multi-source data fusion and dynamic evaluation model: S51: Construct a hierarchical data fusion architecture, including a data layer, a feature layer, and a decision layer. Select DS evidence theory as the feature layer fusion algorithm and consider the confidence level of well logging curves when defining the basic probability allocation function. S52: Develop a dynamic weight adjustment model, input real-time detected helium concentration, gamma value, and resistivity data, and output the weight coefficients of each parameter through a fuzzy logic controller; S53: Set weight adjustment rules: When the helium concentration is >100ppm, the weight coefficient ω_He = 0.7; when the gamma value is <50API, the weight coefficient ω_GR = 0.6. S54: Construct a random forest classification model, with input features including helium concentration, porosity, permeability, and gas saturation, and output reservoir category; S55: Set the model hyperparameters: Number of trees = 100, maximum depth = 15, minimum number of sample splits = 2, number of cross-validation folds = 5; S56: Develop a multiple linear regression model, input reservoir thickness, porosity, permeability and helium concentration, and output daily helium production per well; S57: The LASSO regularization method is introduced to optimize the regression coefficients. The regularization parameter λ=0.1 is set, and the optimal λ value is selected through 10-fold cross-validation. S58: Configure an online learning module to trigger incremental model updates when newly drilled formation data accumulates to 10 layers, with an update cycle of <2 hours; S59: Develop a model version control function to save model parameters after each update and support rollback to historical versions; S510: Define reservoir evaluation index = 0.3 × helium concentration + 0.25 × porosity + 0.2 × permeability + 0.15 × gas saturation + 0.1 × reservoir thickness; S511: Set the evaluation threshold: When REI > 0.8, it is Class I reservoir; when 0.6 < REI ≤ 0.8, it is Class II; when 0.4 < REI ≤ 0.6, it is Class III; when REI ≤ 0.4, it is an ineffective reservoir; S512: Develop a 3D geological modeling module, map the reservoir classification results to the range of 50m around the wellbore, and generate a 3D histogram to display the REI distribution of each interval; S513: Configure an interactive query function to support filtering data by well depth and reservoir category and export an evaluation report.

[0011] Preferably, when constructing a real-time data visualization and decision support system, it includes the following steps: S61: Build a Web monitoring platform based on the Python Dash framework, integrating the real-time curve of helium concentration, gamma-resistivity cross-plot, and reservoir classification heat map; S62: Configure a 3D geological model visualization module to support dynamic interactive display of wellbore trajectory and reservoir distribution; S63: Set multi-level alarm thresholds: When the helium concentration > 150 ppm, it triggers a first-level alarm; when 100 - 150 ppm, it is a second-level alarm, and a text message is pushed to the on-site engineer synchronously; S64: Develop an alarm suppression algorithm, which triggers an alarm only when 3 consecutive sampling points exceed the threshold, to avoid a false alarm rate > 5%; S65: Design a timeline control to support playing back historical data by minute / hour / day, and synchronously display the cuttings description and drilling time parameters corresponding to the well depth; S66: Develop a data annotation tool to allow users to manually mark abnormal intervals, and store the annotation information in the MongoDB database; S67: Build an inter-well data comparison engine to support loading the logging curves of 5 wells simultaneously, and eliminate the range difference through a dynamic normalization algorithm; S68: Develop a reservoir comparison matrix to display the similarity of reservoir parameters of different wells with a heat map of the correlation coefficient matrix; S69: Build a decision tree model based on the CART algorithm, with input parameters including helium concentration, porosity, and permeability, and output the proposed gas testing intervals; S610: Visualize the decision tree rules to generate an easy-to-understand flow chart and mark the key decision nodes; S611: Integrate WebRTC technology to achieve audio and video communication, and on-site engineers can share the screen with experts in the rear in real time and mark the logging curves; S612: Develop a screen sharing encryption module to encrypt the transmitted data using the AES-256 algorithm; S613: Build a numerical simulation model, input reservoir parameters and wellbore parameters, and output the predicted curve of daily helium production per well.

[0012] As a preferred approach, the quality control and data verification system includes the following steps: S71: Develop quarterly calibration specifications for mass spectrometers, use national secondary standard materials for three-point calibration, record calibration data to a blockchain storage platform, and develop an automatic calibration certificate generation tool that includes calibration curves, error analysis, and equipment status codes. S72: Set data quality indicators, including completeness, consistency and reasonableness; develop a data quality dashboard; use red / yellow / green to indicate the quality status of each parameter; and automatically generate quality reports. S73: Deploy the isolated forest algorithm to detect outlier data points, and trigger an automatic repair program when 5 consecutive outliers are detected; S74: Develop a missing value imputation algorithm based on KNN, and select the median of 5 adjacent normal measurement points as the imputation value; S75: Establish cross-validation rules for logging curves: When the difference between sonic transit time and porosity calculated by density logging is >3%, trigger the manual verification process; S76: Develop an automatic comparison tool to perform correlation analysis between the helium concentration detected during drilling and the core analysis results; S77: Build a device failure prediction model. Input vibration, temperature and current parameters, and output device health index. When HI<70, maintenance is recommended. S78: Develop a preventative maintenance plan generator to automatically recommend maintenance cycles based on equipment usage time and environmental parameters; S79: Uses Git version control system to manage logging data, and automatically generates commit records for each data modification; S710: Configures data rollback function, supporting restoration to any historical version; S711: Develop an audit interface compliant with ISO / IEC 17025 standards, allowing third-party organizations to remotely access raw data, process logs, and calibrate certificates; S712: Generate an audit report template, which includes a data integrity statement, a process compliance checklist, and anomaly handling records.

[0013] As a preferred method, the following steps are included when performing field applications and dynamic parameter optimization: S81: Based on regional geological data, three deployment target points were selected in the "Erhua area" of the Weihe River Basin, and the well depth trajectory was designed to avoid faults; S82: Develop a drilling fluid program, select a low-solids polymer drilling fluid, and test whether helium gas detection is not contaminated by mud. S83: Develop drilling parameter optimization algorithms, input formation hardness and helium concentration data, and output recommended drilling pressure; S84: When a sudden increase in helium concentration is detected, the drilling fluid discharge rate is automatically adjusted to suppress wellbore collapse; S85: Construct a formation pressure prediction model, input DC index and sonic transit time data, and output formation fracture pressure gradient; S86: Develop a well control decision support system that automatically suggests adjusting the drilling fluid density when the bottom hole pressure approaches the formation fracture pressure; S87: Integrates logging-while-drilling data to correct formation interface prediction models in real time, enabling horizontal well landing accuracy to be controlled within 0.5m; S88: Develop a geological guidance visualization module to display the three-dimensional deviation between the actual wellbore trajectory and the pre-designed target point; S89: Dynamically adjust the perforation section selection based on real-time monitored helium concentration and reservoir thickness data; S810: Develop a fracturing parameter optimization model, input formation stress and natural fracture density data, and output fracturing fluid discharge rate and proppant particle size; S811: Deploys a downhole permanent sensor array to collect real-time data on bottom hole temperature, pressure, and helium concentration; S812: Develop a data transmission protocol to transmit downhole data to the surface via optical fiber; S813: Construct a production decline analysis model, input initial production and pressure data, and predict the stable production period; S814: Develop an economic evaluation module, input helium price and development cost data, and calculate the investment payback period.

[0014] As a preferred method, the following steps are included when verifying results and evaluating effects: S91: Conduct large-scale fracturing tests on the recommended gas-testing sections, record the amount of fracturing fluid and proppant used, collect flowback fluid samples after fracturing, analyze the changes in helium content, and verify reservoir connectivity. S92: Conduct 72 hours of stable trial production, record hourly helium production and wellhead pressure, and calculate the controlled reserves of a single well; S93: Compare the actual yield of the trial run with the model-predicted yield, and calculate the relative error; S94: Conduct reservoir parameter sensitivity analysis to determine the main controlling factors affecting production; S95: Calculate the internal rate of return and net present value, with the benchmark rate of return set at 12%, and prepare an economic evaluation report, including an investment structure table, a cost and expense table, and a profit and profit distribution table. S96: Deploy well site environmental monitoring stations to collect methane and carbon dioxide concentrations in real time, develop environmental risk early warning models, and trigger emergency shutdown procedures when the monitored values ​​exceed 20% of the lower explosive limit.

[0015] As a preferred approach, the standardization process and the promotion of its results include the following steps: S101: Promote the establishment of industry standards and submit draft standards to the National Technical Committee on Standardization of Oil and Gas; S102: Construct a regional helium exploration database to store structured data such as well logging curves, core data, and gas testing results; S103: Develop a data retrieval system that supports multi-dimensional queries based on geographical location, stratigraphic age, and helium content; S104: Develop a virtual simulation training system to simulate the well site environment for operational training; S105: Establish a professional technical service team to provide integrated services of testing, evaluation and gas testing to other mining rights areas.

[0016] The beneficial effects of this invention are: 1. Existing real-time hydrogen and helium detection and logging evaluation methods during drilling mostly rely on gas analysis using chromatography. This results in long detection cycles, poor real-time performance, and difficulty in capturing the vertical dynamic changes in helium content. Consequently, there is a lag in formation lithology identification and helium-bearing layer determination, affecting the timeliness of drilling decisions. This solution integrates a high-precision hydrogen and helium detection mass spectrometer and logging equipment. Through optimized mechanical installation, automatic calibration, and pre-drilling simulation testing, it achieves real-time online detection of helium content. The mass spectrometer has a detection limit of 1 ppm. Combined with the mounting bracket designed by 3D modeling software and a laser alignment instrument, it ensures that the equipment coaxiality error is ≤0.2 mm, significantly improving detection accuracy and data timeliness. It can acquire the vertical profile of helium concentration in real time, dynamically identify lithological changes and helium-enriched layers, and provide immediate basis for drilling parameter adjustment and gas testing layer selection, effectively shortening the decision-making cycle. 2. Existing real-time detection and logging-based comprehensive evaluation methods for hydrogen and helium while drilling lack a systematic correction mechanism for interfering gases. Cross-interference from gases such as methane and carbon dioxide on helium detection is not effectively suppressed, leading to systematic biases in helium concentration data and affecting the reliability of reservoir evaluation. This solution develops a multi-sensor fusion algorithm and an interfering gas feature library. It identifies the types of interfering gases through a support vector machine classification model, combines data fusion from electrochemical and catalytic combustion sensors to construct a methane-helium cross-interference matrix, and applies a partial least squares regression model to correct the interference coefficient. Simultaneously, the dynamic baseline correction program automatically initiates compensation when a sudden increase in CO2 concentration is detected, which can significantly reduce the impact of interfering gases on helium detection, improve detection specificity, ensure the accuracy of helium concentration data, and provide reliable support for reservoir fluid property identification. 3. Existing real-time monitoring and comprehensive evaluation methods for hydrogen and helium while drilling largely rely on single logging data, lacking multi-source data fusion and dynamic modeling capabilities. This leads to strong subjectivity in reservoir classification, making it difficult to adapt to changes in the wellbore environment and formation heterogeneity. This solution constructs a hierarchical data fusion architecture based on DS evidence theory, integrating logging curves, real-time monitoring data, and geological parameters. It dynamically adjusts data weights through fuzzy logic and combines a random forest classification model with a reservoir evaluation index to achieve objective and dynamic reservoir classification. Multi-source data fusion enhances the comprehensiveness and objectivity of the evaluation, the dynamic model adapts to changes in the wellbore environment, and the 3D geological modeling and online learning modules ensure real-time updates of evaluation results, providing accurate basis for optimizing geological steering and fracturing schemes. Attached Figure Description

[0017] Figure 1 The diagram shown is a schematic flowchart of the drilling-while-drilling hydrogen-helium real-time detection and logging-based comprehensive evaluation method for helium-containing natural gas reservoirs according to the present invention. Figure 2 The diagram shown is a schematic of the drilling detection equipment integration and pre-commissioning process of the drilling detection equipment for the method of real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-containing natural gas reservoirs according to the present invention. Figure 3 The diagram shown is a schematic of the dynamic wellbore environment modeling process of the method for real-time detection of hydrogen and helium while drilling and integrated evaluation of helium-containing natural gas reservoirs according to the present invention. Figure 4 The diagram illustrates the real-time interference gas identification and correction process of the drilling-while-drilling hydrogen-helium real-time detection and logging-based comprehensive evaluation method for helium-containing natural gas reservoirs according to the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Please see Figure 1-4 This invention provides an embodiment of a method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-containing natural gas reservoirs, comprising the following steps: S11: Integrates a high-precision hydrogen-helium detection mass spectrometer and logging equipment, and completes mechanical installation, automatic calibration and pre-drilling simulation testing; S12: Integrate multi-source logging data to construct a formation parameter inversion model, and perform wellbore trajectory correction, lithology identification, and dynamic determination of fluid properties; S13: Develop a multi-sensor fusion algorithm, establish a feature library of interfering gases, and correct cross-interference of methane, carbon dioxide, etc. on helium detection in real time; S14: Based on the DS evidence theory, a dynamic classification model for helium-bearing reservoirs is constructed by fusing logging curves and detection data, and a reservoir evaluation index is output. S15: Build a dynamic monitoring platform that integrates 3D geological modeling, intelligent alarm and remote expert support functions to assist on-site decision-making; S16: Develop equipment calibration specifications, establish real-time data quality monitoring indicators, and ensure data reliability through cross-validation and anomaly detection; S17: Adjust drilling parameters based on real-time monitoring data, optimize the selection of gas testing zones, and dynamically optimize geological steering and fracturing schemes; S18: Verify the accuracy of the production capacity prediction model through gas testing, assess economic indicators and environmental impact, and complete the technology applicability evaluation; S19: Develop enterprise / industry standards, build an output database, conduct technical training and external services, and promote industrial application.

[0020] Preferably, the integration and pre-commissioning of the drilling monitoring equipment includes the following steps: S21: Select a quadrupole mass spectrometer with a detection limit of 1ppm helium, and configure a dedicated injection port to connect with the drilling fluid circulation system. Test whether the instrument's temperature resistance reaches 150℃ and its shock resistance meets the API RP17B standard. S22: The installation bracket is designed using 3D modeling software, and the coaxiality error between the mass spectrometer and the drill collar is adjusted to ≤0.2mm using a laser alignment instrument to complete the electrical wiring for explosion-proof certification; S23: Deploy a hydrogen-helium dual-element sensor, a gamma-ray detector, and a resistivity array in the drill string assembly. The sensors are arranged at equal intervals of 0.5m and data is synchronously acquired via a CAN bus. S24: Configure a temperature compensation module to establish a sensor drift correction model in an environment of -20℃ to 120℃, and store the error correction coefficients in non-volatile memory; S25: Integrates a three-point automatic calibration device, develops a PLC control program to achieve daily timed calibration, and uploads the calibration data to the ground system after CRC verification; S26: Configure a dynamic diagnostic algorithm to trigger an automatic cleaning program when baseline drift exceeds 5%, using ultrapure nitrogen to purge the gas path system at a flow rate of 1L / min; S27: Establish a data transmission channel from the well to the surface based on the WITSML standard, configure the MQTT protocol to achieve low-latency transmission, and set QoS=2 to ensure the reliable arrival of critical data packets; S28: Develop a data packet parsing module to convert raw binary data into logging curves conforming to the DLIS format, and synchronously record timestamps and well depth information; S29: Designed for intrinsic safety according to GB 3836.1-2010 standard, and tested for air tightness at 1.5 times the rated pressure to determine whether the exhaust fan automatically starts when the methane concentration reaches 5% VOL; S210: Equipped with dual-circuit uninterruptible power supply, the switching time in case of main power failure is ≤20ms, and the backup battery has the power capacity to meet 72 hours of continuous operation. S211: Construct a simulated drilling fluid circulation system in the laboratory, inject helium standard samples of known concentration, and verify the linear response range of the mass spectrometer; S212: Conduct full-process pressure testing to simulate a 100MPa high-pressure environment downhole and check whether the equipment insulation resistance change rate exceeds 10%. S213: Monitoring software designed based on the Qt framework, which displays hydrogen and helium concentration curves, gamma values, and resistivity parameters in real time, and integrates alarm threshold setting function.

[0021] Preferably, the following steps are included when performing dynamic wellbore environment modeling: S31: Time alignment of mass spectrometry data, gamma curves, and resistivity curves during drilling, and uniform sampling intervals to 0.1m well depth using cubic spline interpolation; S32: Perform outlier detection, using the isolated forest algorithm to identify and remove noisy data points exceeding the mean ±3σ, retaining ≥95% of valid data. S33: Combining logging-while-drilling data, the true wellbore trajectory is calculated using the minimum curvature method to correct the azimuth error of the logging curve caused by wellbore curvature. S34: Develop a wellbore environment compensation model, input mud density and well temperature gradient parameters, and output wellbore environment correction coefficients for formation resistivity and sonic transit time. S35: The wavelet transform algorithm is applied to decompose the natural gamma curve into multiple scales, and the location of the formation interface is determined by the modulus maxima detection method. The interface identification accuracy is ≤0.3m. S36: Construct a lithological-electrical cross-plot and verify the sandstone and mudstone interface identification results by combining the neutron-density curve overlay method; S37: Establish a porosity inversion model based on the Monte Carlo algorithm, input sonic transit time and density logging data, and output the porosity distribution probability density function; S38: Develop a permeability neural network prediction model, with porosity and clay content as input layers, 8 neurons in the hidden layer, and output the logarithmic mean of permeability; S39: Construct a PVT phase state analysis model, input formation pressure and temperature data, calculate the phase envelope of the methane-helium mixture, and determine whether the fluid is in a liquid, gas, or critical state; S310: An oil, gas and water identification model is established using Fisher discriminant analysis, with resistivity, neutron porosity and spontaneous potential selected as discriminant factors. S311: Select a known helium-containing layer as the validation set, calculate the root mean square error (RMSE) between the model predictions and the measured values, and require RMSE < 5%; S312: The particle swarm optimization algorithm is used to adjust the model hyperparameters, with the number of iterations set to 200, and the learning factors c1=1.5 and c2=1.7, until the model converges; S313: Develop a wellbore pressure-temperature coupling model, input drilling fluid circulation pressure and geothermal gradient data, and calculate the influence coefficient of wellbore fluid phase change on the detection signal in real time; S314: Configure a dynamic compensation algorithm to automatically adjust the helium concentration detection threshold when the wellbore pressure change rate exceeds 0.5 MPa / min.

[0022] Preferably, the real-time identification and correction of interfering gases includes the following steps: S41: Collect standard gas samples of methane, carbon dioxide, and nitrogen, and establish an infrared spectral feature database; S42: Develop a support vector machine classification model, input the position and intensity parameters of the infrared absorption peak of the gas, and output the gas type identification result; S43: Integrates electrochemical sensors, catalytic combustion sensors, and thermal conductivity sensors, and achieves multi-sensor data fusion through Kalman filtering; S44: Set data fusion weighting coefficients: mass spectrometer data weight 0.6, electrochemical sensor 0.2, catalytic combustion sensor 0.2, to ensure helium detection specificity ≥95%; S45: Construct a methane-helium cross-interference matrix, establish an interference coefficient model through partial least squares regression, and input CH4 concentration to predict the correction amount of He concentration; S46: Develop a dynamic baseline correction program that activates a baseline drift compensation algorithm when a sudden increase in CO2 concentration is detected, with a compensation period of 30 seconds. S47: Set quality control indicators: helium detection signal noise ratio >20dB, methane interference suppression ratio >30dB; S48: Develop an automatic alarm system that triggers a sensor cleaning procedure and records abnormal events when SNR < 15dB or ISR < 25dB. S49: Construct a helium solubility model, input formation pressure and temperature data, calculate the solubility coefficient of helium in methane, and correct the ratio of free gas to dissolved gas; S410: Apply the equation of state to calculate the compressibility factor of the mixed gas, correct the volumetric flow meter measurement value, and ensure that the calculation error of helium volumetric flow rate is <3%; S411: Performs moving average filtering to suppress random noise while preserving trend information; S412: Develop a data compression algorithm, perform 8-level decomposition using wavelet transform, retain the first 3-level detail coefficients, and achieve a compression ratio of 4:1; S413: Inject a mixed gas in a laboratory-simulated wellbore environment to verify whether the measurement error of the corrected helium concentration is ≤ 5%; S414: Conduct a 72-hour continuous stability test and record the standard deviation of the measured helium concentration values per hour.

[0023] As a preference, when performing multi-source data fusion and constructing a dynamic evaluation model, the following steps are included: S51: Construct a hierarchical data fusion architecture, including a data layer, a feature layer, and a decision layer. Select the D-S evidence theory as the feature layer fusion algorithm and consider the confidence of logging curves when defining the basic probability assignment function; S52: Develop a dynamic weight adjustment model, input the real-time detected helium concentration, gamma value, and resistivity data, and output the weight coefficients of each parameter through a fuzzy logic controller; S53: Set the weight adjustment rules: when the helium concentration > 100 ppm, the weight coefficient ω_He = 0.7; when the gamma value < 50 API, the weight coefficient ω_GR = 0.6; S54: Construct a random forest classification model, with the input features including helium concentration, porosity, permeability, and gas saturation, and output the reservoir category; S55: Set the model hyperparameters: the number of trees = 100, the maximum depth = 15, the minimum number of samples for splitting = 2, and the number of cross-validation folds = 5; S56: Develop a multiple linear regression model, input the reservoir thickness, porosity, permeability, and helium concentration, and output the daily helium production per well; S57: Introduce the LASSO regularization method to optimize the regression coefficients, set the regularization parameter λ = 0.1, and select the optimal λ value through 10-fold cross-validation; S58: Configure an online learning module. When the data of newly drilled formations accumulates to 10 intervals, trigger an incremental update of the model, and the update period < 2 hours; S59: Develop a model version control function, save the model parameters after each update, and support rolling back to historical versions; S510: Define the reservoir evaluation index = 0.3 × helium concentration + 0.25 × porosity + 0.2 × permeability + 0.15 × gas saturation + 0.1 × reservoir thickness; S511: Set the evaluation thresholds: REI > 0.8 is Class I reservoir, 0.6 < REI ≤ 0.8 is Class II, 0.4 < REI ≤ 0.6 is Class III, and REI ≤ 0.4 is an invalid reservoir; S512: Develop a 3D geological modeling module to map reservoir classification results to a 50m radius around the wellbore and generate a 3D columnar chart to show the REI distribution of each layer; S513: Configure interactive query function, support data filtering by well depth and reservoir type, and export evaluation report.

[0024] Preferably, the construction of a real-time data visualization and decision support system includes the following steps: S61: A web-based monitoring platform built on the Python Dash framework, integrating real-time helium concentration curves, gamma-resistivity cross plots, and reservoir classification thermal maps. S62: Configures a 3D geological model visualization module, supporting dynamic interactive display of wellbore trajectory and reservoir distribution; S63: Set multi-level alarm thresholds: Helium concentration >150ppm triggers a level 1 alarm, 100~150ppm triggers a level 2 alarm, and simultaneously pushes SMS to the on-site engineer; S64: Develop an alarm suppression algorithm that triggers an alarm only when three consecutive sampling points exceed a threshold, thus avoiding a false alarm rate greater than 5%. S65: The design timeline control supports replaying historical data by minute / hour / day, and synchronously displays the cuttings description and drilling time parameters for the corresponding well depth; S66: Develop a data annotation tool that allows users to manually mark abnormal segments, and store the annotation information in a MongoDB database; S67: Construct an inter-well data comparison engine that supports simultaneous loading of logging curves from 5 wells and eliminates range differences through a dynamic normalization algorithm; S68: Develop a reservoir comparison matrix to display the similarity of reservoir parameters in different wells using a correlation coefficient matrix heatmap; S69: Construct a decision tree model based on the CART algorithm. Input parameters include helium concentration, porosity, and permeability. Output suggested test gas sections. S610: Visualize decision tree rules, generate easy-to-understand flowcharts, and annotate key decision nodes; S611: Integrates WebRTC technology to enable audio and video communication, allowing field engineers to share screens and annotate logging curves with experts at the rear in real time; S612: Develop a screen sharing encryption module that uses the AES-256 algorithm to encrypt transmitted data; S613: Construct a numerical simulation model, input reservoir parameters and wellbore parameters, and output a prediction curve of daily helium production per well.

[0025] As a preferred approach, the quality control and data verification system includes the following steps: S71: Develop quarterly calibration specifications for mass spectrometers, use national secondary standard materials for three-point calibration, record calibration data to a blockchain storage platform, and develop an automatic calibration certificate generation tool that includes calibration curves, error analysis, and equipment status codes. S72: Set data quality indicators, including completeness, consistency and reasonableness; develop a data quality dashboard; use red / yellow / green to indicate the quality status of each parameter; and automatically generate quality reports. S73: Deploy the isolated forest algorithm to detect outlier data points, and trigger an automatic repair program when 5 consecutive outliers are detected; S74: Develop a missing value imputation algorithm based on KNN, and select the median of 5 adjacent normal measurement points as the imputation value; S75: Establish cross-validation rules for logging curves: When the difference between sonic transit time and porosity calculated by density logging is >3%, trigger the manual verification process; S76: Develop an automatic comparison tool to perform correlation analysis between the helium concentration detected during drilling and the core analysis results; S77: Build a device failure prediction model. Input vibration, temperature and current parameters, and output device health index. When HI<70, maintenance is recommended. S78: Develop a preventative maintenance plan generator to automatically recommend maintenance cycles based on equipment usage time and environmental parameters; S79: Uses Git version control system to manage logging data, and automatically generates commit records for each data modification; S710: Configures data rollback function, supporting restoration to any historical version; S711: Develop an audit interface compliant with ISO / IEC 17025 standards, allowing third-party organizations to remotely access raw data, process logs, and calibrate certificates; S712: Generate an audit report template, which includes a data integrity statement, a process compliance checklist, and anomaly handling records.

[0026] As a preferred method, the following steps are included when performing field applications and dynamic parameter optimization: S81: Based on regional geological data, three deployment target points were selected in the "Erhua area" of the Weihe River Basin, and the well depth trajectory was designed to avoid faults; S82: Develop a drilling fluid program, select a low-solids polymer drilling fluid, and test whether helium gas detection is not contaminated by mud. S83: Develop drilling parameter optimization algorithms, input formation hardness and helium concentration data, and output recommended drilling pressure; S84: When a sudden increase in helium concentration is detected, the drilling fluid discharge rate is automatically adjusted to suppress wellbore collapse; S85: Construct a formation pressure prediction model, input DC index and sonic transit time data, and output formation fracture pressure gradient; S86: Develop a well control decision support system that automatically suggests adjusting the drilling fluid density when the bottom hole pressure approaches the formation fracture pressure; S87: Integrates logging-while-drilling data to correct formation interface prediction models in real time, enabling horizontal well landing accuracy to be controlled within 0.5m; S88: Develop a geological guidance visualization module to display the three-dimensional deviation between the actual wellbore trajectory and the pre-designed target point; S89: Dynamically adjust the perforation section selection based on real-time monitored helium concentration and reservoir thickness data; S810: Develop a fracturing parameter optimization model, input formation stress and natural fracture density data, and output fracturing fluid discharge rate and proppant particle size; S811: Deploys a downhole permanent sensor array to collect real-time data on bottom hole temperature, pressure, and helium concentration; S812: Develop a data transmission protocol to transmit downhole data to the surface via optical fiber; S813: Construct a production decline analysis model, input initial production and pressure data, and predict the stable production period; S814: Develop an economic evaluation module, input helium price and development cost data, and calculate the investment payback period.

[0027] As a preferred method, the following steps are included when verifying results and evaluating effects: S91: Conduct large-scale fracturing tests on the recommended gas-testing sections, record the amount of fracturing fluid and proppant used, collect flowback fluid samples after fracturing, analyze the changes in helium content, and verify reservoir connectivity. S92: Conduct 72 hours of stable trial production, record hourly helium production and wellhead pressure, and calculate the controlled reserves of a single well; S93: Compare the actual yield of the trial run with the model-predicted yield, and calculate the relative error; S94: Conduct reservoir parameter sensitivity analysis to determine the main controlling factors affecting production; S95: Calculate the internal rate of return and net present value, with the benchmark rate of return set at 12%, and prepare an economic evaluation report, including an investment structure table, a cost and expense table, and a profit and profit distribution table. S96: Deploy well site environmental monitoring stations to collect methane and carbon dioxide concentrations in real time, develop environmental risk early warning models, and trigger emergency shutdown procedures when the monitored values ​​exceed 20% of the lower explosive limit.

[0028] As a preferred approach, the standardization process and the promotion of its results include the following steps: S101: Promote the establishment of industry standards and submit draft standards to the National Technical Committee on Standardization of Oil and Gas; S102: Construct a regional helium exploration database to store structured data such as well logging curves, core data, and gas testing results; S103: Develop a data retrieval system that supports multi-dimensional queries based on geographical location, stratigraphic age, and helium content; S104: Develop a virtual simulation training system to simulate the well site environment for operational training; S105: Establish a professional technical service team to provide integrated services of testing, evaluation and gas testing to other mining rights areas.

[0029] Example 1 Background of implementation: The “Erhua area” in the Weihe Basin is an important strategic reserve area for helium resources in my country, but traditional exploration technology has three major bottlenecks: (1) detection lag: the detection cycle of chromatography is long (>30 minutes), and it is impossible to capture the vertical changes of helium in real time; (2) weak interference correction: cross-interference of methane / carbon dioxide leads to an error of 15%-20% in helium concentration; (3) subjective evaluation: relying on a single logging curve (such as resistivity), the accuracy of reservoir classification is less than 70%.

[0030] Implementation steps: S111: Deploy a quadrupole mass spectrometer (detection limit 1ppm, temperature resistance 150℃), and achieve coaxiality error with the drill collar ≤0.2mm via a laser alignment instrument; S112: Equipped with a hydrogen-helium dual-element sensor and a gamma / resistivity array (0.5m spacing), it synchronously acquires data based on the CAN bus; S113: Laboratory simulation of drilling fluid circulation system, injection of helium standard samples of known concentration to verify the linear response range of mass spectrometer (0-5000ppm). S114: Perform time alignment on mass spectrometer data, gamma curves, and resistivity curves (sampling interval 0.1m well depth), and remove outliers (retention rate ≥95%). S115: Combining drilling survey data, the wellbore trajectory error is corrected by the minimum curvature method, and a wellbore environment compensation model is developed (input mud density and well temperature gradient). S116: Apply wavelet transform algorithm to identify stratigraphic interfaces (accuracy ≤ 0.3m), and construct lithological-electrical cross-plots to verify sandstone / mudstone interfaces; S117: Establish an infrared spectral feature library for methane / carbon dioxide / nitrogen and develop a support vector machine classification model (recognition accuracy ≥ 95%). S118: An integrated electrochemical / catalytic combustion sensor that uses Kalman filtering to fuse data (mass spectrometer weight 0.6) and constructs a methane-helium cross-interference matrix. S119: The dynamic baseline correction procedure initiates compensation (cycle 30 seconds) when CO2 concentration suddenly increases, ensuring helium detection specificity ≥95%; S1110: Based on the DS evidence theory, a hierarchical data fusion architecture (data layer → feature layer → decision layer) is constructed by fusing well logging curves and detection data. S1111: Develop a dynamic weight adjustment model (fuzzy logic controller) and set weight rules (weight 0.7 when helium concentration > 100ppm). S1112: Construct a random forest classification model (100 trees, maximum depth 15), output reservoir categories (I / II / III), and define the reservoir evaluation index (REI). S1113: Select three target points in the "Erhua area", design well depth trajectory to avoid faults, and select low solids polymer drilling fluid; S1114: Adjusts drilling pressure based on real-time helium concentration, and automatically suggests adjusting drilling fluid density when formation pressure approaches fracture pressure; S1115: Integrated downhole permanent sensor array, transmitting data to the surface via optical fiber to dynamically optimize perforation section selection.

[0031] Data comparison table:

[0032] In summary, this method has identified a total of 54 reservoir layers (224m thick) of Class I / II / III in three exploration wells in the Weihe River Basin, with 9 effective gas testing zones. The daily helium production per well is expected to reach 2000m³, with an annual output value exceeding 60 million yuan. Compared with traditional methods, the detection efficiency is increased by 3 times, and the objectivity of reservoir evaluation is significantly improved.

[0033] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of well logging in helium-bearing natural gas reservoirs; characterized in that: It includes the following steps: S11: Integrates a high-precision hydrogen-helium detection mass spectrometer and logging equipment, and completes mechanical installation, automatic calibration and pre-drilling simulation testing; S12: Integrate multi-source logging data to construct a formation parameter inversion model, and perform wellbore trajectory correction, lithology identification, and dynamic determination of fluid properties; S13: Develop a multi-sensor fusion algorithm, establish a feature library of interfering gases, and correct cross-interference of methane, carbon dioxide, etc. on helium detection in real time; S14: Based on the DS evidence theory, a dynamic classification model for helium-bearing reservoirs is constructed by fusing logging curves and detection data, and a reservoir evaluation index is output. S15: Build a dynamic monitoring platform that integrates 3D geological modeling, intelligent alarm and remote expert support functions to assist on-site decision-making; S16: Develop equipment calibration specifications, establish real-time data quality monitoring indicators, and ensure data reliability through cross-validation and anomaly detection; S17: Adjust drilling parameters based on real-time monitoring data, optimize the selection of gas testing zones, and dynamically optimize geological steering and fracturing schemes; S18: Verify the accuracy of the production capacity prediction model through gas testing, assess economic indicators and environmental impact, and complete the technology applicability evaluation; S19: Develop enterprise / industry standards, build an output database, conduct technical training and external services, and promote industrial application.

2. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: The integration and pre-commissioning of drilling testing equipment includes the following steps: S21: Select a quadrupole mass spectrometer with a detection limit of 1ppm helium, and configure a dedicated injection port to connect with the drilling fluid circulation system. Test whether the instrument's temperature resistance reaches 150℃ and its shock resistance meets the API RP17B standard. S22: The installation bracket is designed using 3D modeling software, and the coaxiality error between the mass spectrometer and the drill collar is adjusted to ≤0.2mm using a laser alignment instrument to complete the electrical wiring for explosion-proof certification; S23: Deploy a hydrogen-helium dual-element sensor, a gamma-ray detector, and a resistivity array in the drill string assembly. The sensors are arranged at equal intervals of 0.5m and data is synchronously acquired via a CAN bus. S24: Configure a temperature compensation module to establish a sensor drift correction model in an environment of -20℃ to 120℃, and store the error correction coefficients in non-volatile memory; S25: Integrates a three-point automatic calibration device, develops a PLC control program to achieve daily timed calibration, and uploads the calibration data to the ground system after CRC verification; S26: Configure a dynamic diagnostic algorithm to trigger an automatic cleaning program when baseline drift exceeds 5%, using ultrapure nitrogen to purge the gas path system at a flow rate of 1L / min; S27: Establish a data transmission channel from the well to the surface based on the WITSML standard, configure the MQTT protocol to achieve low-latency transmission, and set QoS=2 to ensure the reliable arrival of critical data packets; S28: Develop a data packet parsing module to convert raw binary data into logging curves conforming to the DLIS format, and synchronously record timestamps and well depth information; S29: Designed for intrinsic safety according to GB 3836.1-2010 standard, and tested for air tightness at 1.5 times the rated pressure to determine whether the exhaust fan automatically starts when the methane concentration reaches 5% VOL; S210: Equipped with dual-circuit uninterruptible power supply, the switching time in case of main power failure is ≤20ms, and the backup battery has the power capacity to meet 72 hours of continuous operation. S211: Construct a simulated drilling fluid circulation system in the laboratory, inject helium standard samples of known concentration, and verify the linear response range of the mass spectrometer; S212: Conduct full-process pressure testing to simulate a 100MPa high-pressure environment downhole and check whether the equipment insulation resistance change rate exceeds 10%. S213: Monitoring software designed based on the Qt framework, which displays hydrogen and helium concentration curves, gamma values, and resistivity parameters in real time, and integrates alarm threshold setting function.

3. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: The following steps are included when performing dynamic wellbore environment modeling: S31: Time alignment of mass spectrometry data, gamma curves, and resistivity curves during drilling, and uniform sampling intervals to 0.1m well depth using cubic spline interpolation; S32: Perform outlier detection, using the isolated forest algorithm to identify and remove noisy data points exceeding the mean ±3σ, retaining ≥95% of valid data. S33: Combining logging-while-drilling data, the true wellbore trajectory is calculated using the minimum curvature method to correct the azimuth error of the logging curve caused by wellbore curvature. S34: Develop a wellbore environment compensation model, input mud density and well temperature gradient parameters, and output wellbore environment correction coefficients for formation resistivity and sonic transit time. S35: The wavelet transform algorithm is applied to decompose the natural gamma curve into multiple scales, and the location of the formation interface is determined by the modulus maxima detection method. The interface identification accuracy is ≤0.3m. S36: Construct a lithological-electrical cross-plot and verify the sandstone and mudstone interface identification results by combining the neutron-density curve overlay method; S37: Establish a porosity inversion model based on the Monte Carlo algorithm, input sonic transit time and density logging data, and output the porosity distribution probability density function; S38: Develop a permeability neural network prediction model, with porosity and clay content as input layers, 8 neurons in the hidden layer, and output the logarithmic mean of permeability; S39: Construct a PVT phase state analysis model, input formation pressure and temperature data, calculate the phase envelope of the methane-helium mixture, and determine whether the fluid is in a liquid, gas, or critical state; S310: An oil, gas and water identification model is established using Fisher discriminant analysis, with resistivity, neutron porosity and spontaneous potential selected as discriminant factors. S311: Select a known helium-containing layer as the validation set, calculate the root mean square error (RMSE) between the model predictions and the measured values, and require RMSE < 5%; S312: The particle swarm optimization algorithm is used to adjust the model hyperparameters, with the number of iterations set to 200, and the learning factors c1=1.5 and c2=1.7, until the model converges; S313: Develop a wellbore pressure-temperature coupling model, input drilling fluid circulation pressure and geothermal gradient data, and calculate the influence coefficient of wellbore fluid phase change on the detection signal in real time; S314: Configure a dynamic compensation algorithm to automatically adjust the helium concentration detection threshold when the wellbore pressure change rate exceeds 0.5 MPa / min.

4. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: The real-time identification and correction of interfering gases includes the following steps: S41: Collect standard gas samples of methane, carbon dioxide, and nitrogen, and establish an infrared spectral feature database; S42: Develop a support vector machine classification model, input the position and intensity parameters of the infrared absorption peak of the gas, and output the gas type identification result; S43: Integrates electrochemical sensors, catalytic combustion sensors, and thermal conductivity sensors, and achieves multi-sensor data fusion through Kalman filtering; S44: Set data fusion weighting coefficients: mass spectrometer data weight 0.6, electrochemical sensor 0.2, catalytic combustion sensor 0.2, to ensure helium detection specificity ≥95%; S45: Construct a methane-helium cross-interference matrix, establish an interference coefficient model through partial least squares regression, and input CH4 concentration to predict the correction amount of He concentration; S46: Develop a dynamic baseline correction program that activates a baseline drift compensation algorithm when a sudden increase in CO2 concentration is detected, with a compensation period of 30 seconds. S47: Set quality control indicators: helium detection signal noise ratio >20dB, methane interference suppression ratio >30dB; S48: Develop an automatic alarm system that triggers a sensor cleaning procedure and records abnormal events when SNR < 15dB or ISR < 25dB. S49: Construct a helium solubility model, input formation pressure and temperature data, calculate the solubility coefficient of helium in methane, and correct the ratio of free gas to dissolved gas; S410: Apply the equation of state to calculate the compressibility factor of the mixed gas, correct the volumetric flow meter measurement value, and ensure that the calculation error of helium volumetric flow rate is <3%; S411: Performs moving average filtering to suppress random noise while preserving trend information; S412: Develop a data compression algorithm that uses wavelet transform for 8-level decomposition, retains the first 3 levels of detail coefficients, and achieves a compression ratio of 4:

1. S413: Inject a mixed gas into a simulated wellbore environment in the laboratory to verify whether the measurement error of the calibrated helium concentration is ≤5%; S414: Conduct a 72-hour continuous stability test and record the standard deviation of the helium concentration measurement value every hour.

5. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: The following steps are included in the construction of multi-source data fusion and dynamic evaluation models: S51: Construct a hierarchical data fusion architecture, including a data layer, a feature layer, and a decision layer. Select DS evidence theory as the feature layer fusion algorithm and consider the confidence level of well logging curves when defining the basic probability allocation function. S52: Develop a dynamic weight adjustment model, input real-time detected helium concentration, gamma value, and resistivity data, and output the weight coefficients of each parameter through a fuzzy logic controller; S53: Set weight adjustment rules: When the helium concentration is >100ppm, the weight coefficient ω_He = 0.7; when the gamma value is <50API, the weight coefficient ω_GR = 0.

6. S54: Construct a random forest classification model, with input features including helium concentration, porosity, permeability, and gas saturation, and output reservoir category; S55: Set the model hyperparameters: Number of trees = 100, maximum depth = 15, minimum number of sample splits = 2, number of cross-validation folds = 5; S56: Develop a multiple linear regression model, input reservoir thickness, porosity, permeability and helium concentration, and output daily helium production per well; S57: Introduce the LASSO regularization method to optimize the regression coefficients, set the regularization parameter λ = 0.1, and select the optimal λ value through 10-fold cross-validation; S58: Configure the online learning module. When the new drilled formation data accumulates to 10 intervals, trigger the incremental update of the model, and the update period < 2 hours; S59: Develop the model version control function, save the model parameters after each update, and support rolling back to historical versions; S510: Define the reservoir evaluation index = 0.3 × helium concentration + 0.25 × porosity + 0.2 × permeability + 0.15 × gas saturation + 0.1 × reservoir thickness; S511: Set the evaluation thresholds: REI > 0.8 is Class I reservoir, 0.6 < REI ≤ 0.8 is Class II, 0.4 < REI ≤ 0.6 is Class III, and REI ≤ 0.4 is an invalid reservoir; S512: Develop a 3D geological modeling module, map the reservoir classification results to the 50m range around the wellbore, and generate a 3D column chart to display the REI distribution of each interval; S513: Configure an interactive query function, support filtering data by well depth and reservoir category, and export evaluation reports.

6. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: When constructing a real-time data visualization and decision support system, it includes the following steps: S61: Build a Web-based monitoring platform based on the Python Dash framework, integrating the real-time curve of helium concentration, gamma-resistivity cross-plot, and reservoir classification heat map; S62: Configure a 3D geological model visualization module to support dynamic interactive display of wellbore trajectories and reservoir distributions; S63: Set multi-level alarm thresholds: helium concentration > 150ppm triggers a first-level alarm, 100 - 150ppm is a second-level alarm, and synchronously push text messages to on-site engineers; S64: Develop an alarm suppression algorithm to trigger an alarm only when 3 consecutive sampling points exceed the threshold, to avoid a false alarm rate > 5%; S65: Design a time axis control to support playing back historical data by minute / hour / day, and synchronously display the cuttings description and drilling time parameters corresponding to the well depth; S66: Develop a data annotation tool that allows users to manually mark abnormal intervals, and store the annotation information in the MongoDB database; S67: Build an inter-well data comparison engine, support loading logging curves of 5 wells simultaneously, and eliminate range differences through a dynamic normalization algorithm; S68: Develop a reservoir comparison matrix, and display the similarity of reservoir parameters of different wells with a correlation coefficient matrix heat map; S69: Build a decision tree model based on the CART algorithm, with input parameters including helium concentration, porosity, and permeability, and output the proposed gas testing intervals; S610: Visualize the decision tree rules, generate an easy-to-understand flow chart, and mark key decision nodes; S611: Integrate WebRTC technology to achieve audio and video communication, and on-site engineers can share the screen and mark logging curves with experts at the back-end in real time; S612: Develop a screen sharing encryption module, and encrypt the transmitted data using the AES-256 algorithm; S613: Build a numerical simulation model, input reservoir parameters and wellbore parameters, and output the predicted curve of daily helium production per well.

7. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: When implementing a quality control and data verification system, it includes the following steps: S71: Develop quarterly calibration specifications for mass spectrometers, use national secondary standard materials for three-point calibration, record calibration data to a blockchain storage platform, and develop an automatic calibration certificate generation tool that includes calibration curves, error analysis, and equipment status codes. S72: Set data quality indicators, including completeness, consistency and reasonableness; develop a data quality dashboard; use red / yellow / green to indicate the quality status of each parameter; and automatically generate quality reports. S73: Deploy the isolated forest algorithm to detect outlier data points, and trigger an automatic repair program when 5 consecutive outliers are detected; S74: Develop a missing value imputation algorithm based on KNN, and select the median of 5 adjacent normal measurement points as the imputation value; S75: Establish cross-validation rules for logging curves: When the difference between sonic transit time and porosity calculated by density logging is >3%, trigger the manual verification process; S76: Develop an automatic comparison tool to perform correlation analysis between the helium concentration detected during drilling and the core analysis results; S77: Build a device failure prediction model. Input vibration, temperature and current parameters, and output device health index. When HI<70, maintenance is recommended. S78: Develop a preventative maintenance plan generator to automatically recommend maintenance cycles based on equipment usage time and environmental parameters; S79: Uses Git version control system to manage logging data, and automatically generates commit records for each data modification; S710: Configures data rollback function, supporting restoration to any historical version; S711: Develop an audit interface compliant with ISO / IEC 17025 standards, allowing third-party organizations to remotely access raw data, process logs, and calibrate certificates; S712: Generate an audit report template, which includes a data integrity statement, a process compliance checklist, and anomaly handling records.

8. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: When performing field applications and dynamic parameter optimization, the following steps are included: S81: Based on regional geological data, three deployment target points were selected in the "Erhua area" of the Weihe River Basin, and the well depth trajectory was designed to avoid faults; S82: Develop a drilling fluid program, select a low-solids polymer drilling fluid, and test whether helium gas detection is not contaminated by mud. S83: Develop drilling parameter optimization algorithms, input formation hardness and helium concentration data, and output recommended drilling pressure; S84: When a sudden increase in helium concentration is detected, the drilling fluid discharge rate is automatically adjusted to suppress wellbore collapse; S85: Construct a formation pressure prediction model, input DC index and sonic transit time data, and output formation fracture pressure gradient; S86: Develop a well control decision support system that automatically suggests adjusting the drilling fluid density when the bottom hole pressure approaches the formation fracture pressure; S87: Integrates logging-while-drilling data to correct formation interface prediction models in real time, enabling horizontal well landing accuracy to be controlled within 0.5m; S88: Develop a geological guidance visualization module to display the three-dimensional deviation between the actual wellbore trajectory and the pre-designed target point; S89: Dynamically adjust the perforation section selection based on real-time monitored helium concentration and reservoir thickness data; S810: Develop a fracturing parameter optimization model, input formation stress and natural fracture density data, and output fracturing fluid discharge rate and proppant particle size; S811: Deploys a downhole permanent sensor array to collect real-time data on bottom hole temperature, pressure, and helium concentration; S812: Develop a data transmission protocol to transmit downhole data to the surface via optical fiber; S813: Construct a production decline analysis model, input initial production and pressure data, and predict the stable production period; S814: Develop an economic evaluation module, input helium price and development cost data, and calculate the investment payback period.

9. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: The following steps are included in the verification and evaluation of results: S91: Conduct large-scale fracturing tests on the recommended gas-testing sections, record the amount of fracturing fluid and proppant used, collect flowback fluid samples after fracturing, analyze the changes in helium content, and verify reservoir connectivity. S92: Conduct 72 hours of stable trial production, record hourly helium production and wellhead pressure, and calculate the controlled reserves of a single well; S93: Compare the actual yield of the trial run with the model-predicted yield, and calculate the relative error; S94: Conduct reservoir parameter sensitivity analysis to determine the main controlling factors affecting production; S95: Calculate the internal rate of return and net present value, with the benchmark rate of return set at 12%, and prepare an economic evaluation report, including an investment structure table, a cost and expense table, and a profit and profit distribution table. S96: Deploy well site environmental monitoring stations to collect methane and carbon dioxide concentrations in real time, develop environmental risk early warning models, and trigger emergency shutdown procedures when the monitored values ​​exceed 20% of the lower explosive limit.

10. The method for real-time detection of hydrogen and helium while drilling and comprehensive evaluation of helium-bearing natural gas reservoirs according to claim 1, characterized in that: The following steps are included in the standardization process and the promotion of its results: S101: Promote the establishment of industry standards and submit draft standards to the National Technical Committee on Standardization of Oil and Gas; S102: Construct a regional helium exploration database to store structured data such as well logging curves, core data, and gas testing results; S103: Develop a data retrieval system that supports multi-dimensional queries based on geographical location, stratigraphic age, and helium content; S104: Develop a virtual simulation training system to simulate the well site environment for operational training; S105: Establish a professional technical service team to provide integrated services of testing, evaluation and gas testing to other mining rights areas.

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