Mud logging technology has the four advantages of "more,faster,better,cheaper",but the development of this industry is still facing the four challenges of "weak basic theory,insufficient advanced technology,shortcomings in technical services,and poor development ecology". It is urgent to grasp the source of "thinking mode" to drive the benign development of mud logging technology innovation. Therefore,this paper puts forward three basic thinking modes of theoretical thinking,system thinking and in-situ thinking that should be followed in the innovation of mud logging technology. Theoretical thinking includes three levels：application of theory,extension of theory and innovation of theory. System thinking includes three levels：technology chain thinking,technology series thinking and technology system thinking. In-situ thinking includes two levels：in-situ thinking of hydrocarbon reservoirs and in-situ thinking of geology-engineering integration. These three thinking modes complement each other and constitute the cornerstone of innovative thinking in mud logging technology. These basic thinking modes have a certain guiding and promoting role in enriching the mud logging theory system,deepening the mud logging technology system,improving the mud logging services system,and perfecting the mud logging ecosystem.

Rock hardness and plasticity coefficient are important indicators for oil drilling, and the method of obtaining these indicators by core experiments is limited due to the difficulty and high cost of full scale core coring. Based on this background, the cores of Qingshankou Formation in an oilfield are selected to construct data sets of hardness, plasticity coefficient and element characteristics through conducting static load indentation experiments and XRF element experiments. The Pearson correlation analysis and the algorithm of particle swarm optimization BP neural network （PSO-BP） were used to reveal the main controlling elements of rock hardness and plasticity coefficient, and a method of shale hardness and plasticity coefficient evaluation while drilling based on XRF element logging was established. The predictive results show the major elements for main controlling shale hardness are Fe, Al, K and Ca, and the trace elements are Cr and Rb, shale hardness is positively correlated with Ca element and negatively correlated with other main controlling elements. The major elements for main controlling plasticity coefficient is Ti, and the trace elements are Cd, Nb, Ni and V, plasticity coefficient is negatively correlated with Ti and positively correlated with other main controlling elements. The network error of the shale hardness prediction model is 8.06×10^-8, and the network error of the plasticity prediction model is 3.02×10^-11, both of which are below the preset thresholds. From the application of this method in tracking while drilling shale oil, it can be known that the shale host in the work area belongs to the low-plasticity rock of medium-soft grade 3, so the suitable PDC drill bit is recommended, and a high penetration rate is obtained. The method provides technical support for drilling dynamic optimization.

The development of drilling technology has greatly improved drilling efficiency,but it has made it difficult for traditional naked eye debris identification methods to meet efficient logging. In addition,the differences in the technical level of field personnel have seriously restricted the improvement of debris logging quality. Therefore,a digital intelligent identification system for debris that integrates artificial intelligence,image analysis and Internet of things technology has realized automated collection,real-time processing and intelligent interpretation of debris data. This technology integrates high-resolution image acquisition,digital image feature extraction,and an AI lithology classification model based on deep learning. Practical application in Changqing Oilfield shows that the lithology identification accuracy of the debris digital intelligent identification system has been increased to more than 90%,effectively solving the problem of difficulty in naming micro rocks in traditional manual identification. The system not only significantly improves the accuracy and efficiency of cuttings logging,but also provides key technical support for the transformation of logging operations from empirical to intelligent,which has important practical significance and broad application prospects for improving the overall logging quality.

Aiming at the problems of complex geological conditions of deep coal rock, easy collapse of target coal rock, difficult logging operation, limited log information, and immature comprehensive evaluation of mud logging reservoir quality, and relying on the key mud logging parameters of more than two new wells in Changqing Oilfield in recent two years, this paper establishes a set of deep coal-rock gas reservoir quality evaluation technology based on mud logging technology from four aspects： rock quality, source rock quality, gas quality and engineering quality. This evaluation technology has been successfully applied to the fracturing layer selection scheme design of 15 deep coal-rock gas wells in Changqing Oilfield, highlighting the technical advantages in comprehensive evaluation of mud logging and achieving deep fusion of geology-engineering integration. This technology effectively meets the exploration and development needs of deep coal-rock gas reservoirs, provides reliable technical support for horizontal well trajectory optimization and fracturing layer optimum selection, and verifies its strong advantages in deep coal-rock gas evaluation.

Western oilfields such as Xinjiang Oilfield and Tarim Oilfield cover vast territories. Due to communication signal coverage limitations in traditional transmission networks, data collected at some well sites cannot be transmitted back in a timely manner. This has certain impacts on optimizing drilling operations at the frontier well sites, real-time monitoring of drilling operations at the rear base, and geological interpretation research. To address the challenge of real-time mud logging data transmission caused by insufficient network coverage, this paper proposes a real-time mud logging data transmission system based on Beidou short messages. By analyzing the Beidou 3 satellite navigation system transmission protocol, constructing a Beidou mud logging data transmission test environment, and developing Beidou mud logging data real-time transmission system, an integrated closed-loop transmission system for mud logging data "acquisition-encoding-transmission" has been achieved. This research not only resolves traditional network dependency issues but also reduces energy data security risks through the autonomous controllability of the Beidou protocol. It provides highly reliable, low-cost communication technology support for oilfield digital transformation and offers a practical example for the large-scale application of the Beidou system in the energy sector.

To tackle the limitations of ultrasonic wave liquid level sensors in drilling fluid outlet buffer tank level monitoring at oilfield drilling sites, which particularly include susceptibility to environmental interference and insufficient measurement accuracy. The improvement plan for 78 GHz Frequency-Modulated Continuous Wave （FMCW） radar liquid level sensor is proposed in this study, which achieves millimeter-level precision （error of ±3.2 mm） with narrow （≤6°） by optimizing beam angle and anti-jamming algorithms. Application to well W 204HX-X in Southwest Oil & Gas Field demonstrated that superior performance: compared to ultrasonic wave sensors, the radar sensor improved measurement accuracy by 79.6% and data stability by 16.2% under severe liquid surface fluctuation conditions. The technology effectively overcomes measurement deviations caused by high-temperature smoke and water mist interference while enhancing monitoring stability.This research reveals and expands the applicability boundaries of high-frequency radar technology in complex industrial environments. Both cost-benefit analysis and functional evaluations confirm its viability as a replacement for traditional ultrasonic wave level sensors. The findings provide a novel technical pathway for intelligent drilling fluid level monitoring equipment development in oil and gas drilling operations.

With the deepening of exploration and development of oil and gas resources, it is more important to accurately monitor the three pressures of formation （pore pressure, collapse pressure and fracture pressure）. However, although the traditional formation three-pressure monitoring method has been widely used, it has the limitation of relying on human experience and parameter setting. In order to realize the real-time and accurate monitoring of formation three pressures, this paper takes four wells to be monitored in Beidagang buried hill structural belt of Huanghua Depression as an example. Based on the drilling, recording and logging data of completed wells around the wells to be monitored, combined with advanced machine learning algorithms such as Extreme Gradient Boosting （XGBoost）, light Gradient Boosting Machine （LightGBM） and Random Forest （RF）, the drilling and logging data are coupled with the logging calculation pressure based on poroelastic mechanics theory. The results show that the pore pressure prediction average relative error of all measured pressure points in the study area is 6.32%, and the overall monitoring accuracy is over 93%. The multi-parameter coupled formation three-pressure monitoring technology has high prediction accuracy, good generalization performance and feasibility. In view of the fact that this method can achieve accurate monitoring of three pressures while drilling in multi-lithologies, multi-types of reservoirs, and multi-pressure genesis formations, it can be popularized and applied to three pressures while drilling monitoring in other complex oil and gas reservoirs, providing technical support for integrated risk prediction and safe and efficient drilling construction of geological engineering.

During the monitoring of the outlet drilling fluid parameters at the drilling site,due to the fast flow velocity and strong impact force of the drilling fluid returning from the well bore,along with the deposition of cuttings,it often leads to the sensor not being installed vertically and the probe being buried,resulting in large measurement parameter errors,which affect the discovery and evaluation of the show of gas and oil and wellbore safety. Therefore,based on the principles of automation control and quantitative acquisition technology of drilling fluid,a quantitative monitoring system for outlet drilling fluid parameters has been researched and developed. By optimizing the working environment and monitoring methods of the sensors,this system can ensure that the outlet temperature,density and conductivity sensors achieve quantitative monitoring under the optimum working conditions,effectively solving problems such as substandard working conditions at the well site. It has been applied to 32 wells in Tuha,Xinjiang,Sulige and other oil and gas fields,which has significantly improved the accuracy of monitoring outlet drilling fluid parameters and has provided reliable technical support for wellbore safety control in the process of oil and gas field drilling and subsequent hydrocarbon resources development and evaluation.

During drilling operations, the penetration of the drill bit into the target formation and the occurrence of formation penetration can be determined by monitoring changes in the ion concentration of the drilling fluid. However, conventional manual chemical titration measurements suffer from lengthy operation cycles, significant errors, and substantial influence from human factors. The ion selective electrodes （ISE） method employed by some laboratories suffers from significant measurement inaccuracies influenced by pH and temperature, rendering it unsuitable for field applications. This paper therefore proposes an error compensation method for ISE measurements affected by drilling fluid pH and temperature interference. Building upon an overview of ISE measurement principles, the paper analyzes the influence characteristics of pH and temperature parameters on ISE and establishes an experimental scheme for chloride ion concentration measurement. Subsequently, based on experimental data and model analysis of pH and temperature, linear compensation terms for pH and temperature are introduced into the Nernst equation to eliminate interference from pH and temperature variations in the solution to be measured. This establishes pH and temperature compensation models for ion selective electrodes. Finally, the models are validated using ion concentration parameters are actually collected at different temperatures and pH values. After compensation, the measurement errors for chloride ion concentration are consistently within 10%, meeting field requirements.

As a new type of unconventional energy, coal-rock gas has attracted much attention in its exploration and development. However,the chemical composition of coal-rock gas reservoirs is complex,which affects gas content,adsorption capacity,and development effects. Traditional laboratory analysis and logging methods are high cost, long cycle length,and difficult to adapt complex well conditions. It is difficult to meet the needs for efficient development of deep coal-rock gas,which further highlighting the urgent need for efficient and accurate dynamic evaluation methods. This research takes coal rocks from the Carboniferous Benxi Formation and Permian Shanxi Formation in the central-eastern part of the Ordos Basin as the object,and proposes a method for comprehensively evaluating the industrial components of coal rocks based on X-ray element logging technology and gas logging total hydrocarbon data. By analyzing 36 coal-rock samples,a linear positive correlation model between major element content and ash content, as well as a linear negative correlation model with fixed carbon are constructed. Considering that volatile component and moisture are controlled by the pyrolysis of organic matter,the dynamic inversion of volatile component and moisture is realized by introducing gas logging total hydrocarbon data. The model was applied to Wells M 172 and WT 1,providing guiding data for reservoir reconstruction,with gas test yields reaching 13.6×10⁴ m³/d and 10.4×10⁴ m³/d respectively. This method breaks through the limitations of traditional laboratory analysis and logging methods,which can analyze and evaluate the industrial components of coal rocks in complex well types （horizontal wells,highly-deviated wells） in real time,and provides low-cost and high-efficiency technical support for deep coal-rock gas exploration.

The shale oil layers of the third member of Shahejie Formation in Qikou Sag has higher clay mineral content, stronger water sensitivity than that of Ek₂ of Cangdong Sag, developed fracture system, and extremely complicated engineering in the drilling process. Since the use of white oil-based drilling fluid in horizontal wells in the third member of Qikou Shahejie Formation in 2023, drilling and development efficiency has been significantly improved, but it has a great impact on gas logging. By comparing and analyzing the response characteristics of gas logging under water-based and oil-based drilling fluids, this paper summarizes the adsorption characteristics of oil-based drilling fluids to total hydrocarbons and hydrocarbon components under different gas kick degrees, and based on the data, the measured relative percentage change rate of each component, the gas logging correction method is studied, and the gas logging interpretation chart and evaluation standard are established, which provide the basis for the evaluation of shale oil sweet spots under oil-based drilling fluid,and have certain guiding significance through the verification of production effect.

Baoding Sag is currently a key play for exploration and development. Due to the reservoirs with shallow burial depth of oil reservoirs in the region,there are not only characteristics of low-mature oil reservoirs such as high methane content and high oil density,but also widespread phenomena of biodegradation and water washing damage,making the gas composition characteristics no longer sensitive to evaluation,and the level of real object show is relatively high. In mud logging evaluation and analysis,parameters such as P_g are all affected to a certain extent. The existing mud logging interpretation and evaluation techniques show certain limitations,resulting in decreased applicability. To effectively solve the problems of the complex mud logging response characteristics of the reservoirs and difficult identification of reservoir fluid properties,the sensitive parameters were extracted by integrating relevant influencing factors through oil testing and productivity determination data as well as various analysis and testing data. Statistical analysis software was used to classify and discriminate,and a linear regression model was constructed. It has made the coincidence rate of mud logging interpretation while drilling reach 73.33%,meeting the need for rapid reservoir fluid property evaluation while drilling and meeting the conditions for on-site promotion and application,simultaneously providing support for the digital and intelligent transformation of mud logging industry.

The shale gas resource potential of Wujiaping Formation in Hongxing area,the eastern margin of Sichuan Basin is large,but the production capacity of each well varies significantly,and the influencing factors have not been systematically quantified. For the Analytic Hierarchy Process （AHP） can effectively realize the quantitative evaluation of multi-factors, the first-level index layer of geology and engineering factors is established, covering 5 second-level index layers of trajectory factor,physical property factor,gas storage factor,fracturing factor,gas test factor,and 21 third-level indexes such as the horizontal section length, the total gas content,the fracture pressure,and backflow volume,etc.The judgment matrix is constructed and the indicator weight for each layer is calculated. Typical single wells in Hongxing area are selected,and the standardized values of each indicator are multiplied with the weights calculated by AHP to get the comprehensive evaluation value of each well,and fitted with the open flow potential and recoverable reserves to verify the reliability of the model. The results show that the weights of methane content,open pressure and organic carbon content are 0.121 8、0.111 1 and 0.088 9 respectively,which are the main control factors of gas-well deliverability. The comprehensive evaluation value of each well fits well with the open flow potential and recoverable reserves,and the fitting degree is higher than 85%. Through this study,the key control parameters of shale gas wells are clarified in Wujiaping Formation in Hongxing area,which can provide theoretical basis for the efficient development of this area.

In order to ensure the safe and smooth operation of the gas storage and effectively evaluate the geologic body sealing capacity of the gas storage, the stability of the cap rocks and faults is evaluated. Taking Dagang Z gas storage as the research object, based on data such as geological gas reservoir characteristics, production dynamic characteristics, rock mechanics experiments, combined with the characteristics of alternating ground stress changes in the gas storage after multiple cycles of high-speed alternating injection and production, a four-dimensional dynamic geomechanics model is established to comprehensively evaluate the trap sealing capacity after 30 years of development and 20 injection and production cycles. The research results show that:①The ultimate bearing capacity of the geologic body cap of the Z gas storage is 48.8 MPa, the ultimate bearing capacity of controlling-trap T fault is 35.1 MPa, and the upper limit pore pressure of the Z gas storage operation is 31.5 MPa, which is within the safe range;②During the long term high-speed alternating injection and production operation of Z gas storage, the cap rocks, reservoirs and faults are not damaged, but geomechanical simulations show that individual risk points appeared on controlling-trap T fault, but it is still relatively safe at present;③It is recommended to carry out real-time dynamic monitoring of the geologic bodies of the Z gas storage based on the research of the four-dimensional geomechanical model, and combined with the actual dynamic monitoring data. Monitoring wells on the upper wall of the T fault risk points of the Z gas storage are deployed, strengthening injection and production dynamic monitoring and ensuring long-term safe and smooth operation of the gas storage.

As oil and gas exploration progresses in the eastern South China Sea, low-porosity and low-permeability reservoirs have gradually become key exploration targets. These reservoirs exhibit complex pore-throat structures and a weak correlation between porosity and permeability, making it difficult for conventional well logging, mud logging, and core experimental analysis to meet the accuracy requirements for exploration, thereby significantly increasing operational costs. To address this, a combination of micro-coring while-drilling and digital cuttings technology has been introduced in the eastern part of the South China Sea. By thoroughly analyzing sensitive parameters of digital cuttings and leveraging CT scanning images and micron pore-throat radius, digital cuttings reconstruction was accomplished using computer image processing technology. This enabled reservoir classification and the prediction of mobility based on the micron median pore-throat radius. As a result, a qualitative and quantitative evaluation system for low-porosity and low-permeability reservoirs in the Panyu 4 sub-sag of the Xijiang Sag has been established. This approach significantly enhances the precision of comprehensive interpretation and evaluation for such reservoirs, offering a new pathway for the exploration of low-porosity and low-permeability reservoirs in the eastern South China Sea.

The "dual carbon" strategy has accelerated the energy transition and the development of new energy in the petroleum industry. Currently,a certain number of long-closed oil and gas wells remain in major oilfields. Repurposing these wells into geothermal wells can revitalize assets,reduce production costs,and serve as a critical measure for clean energy substitution. Taking Shenjiapu Oilfield in Cangdong Sag, Dagang Oilfield as an example,the distribution characteristics of the Guantao Formation thermal reservoir are systematically analyzed by collecting and organizing drilling and geological data. The geothermal resource quantity is evaluated by using the thermal reservoir volumetric method and 3D geological modeling,and a quantitative evaluation method for the potential of converting long-closed oil and gas wells into geothermal wells is established. The research results indicate：The Guantao Formation thermal reservoir in the study area features significant stratum thickness,moderate depth,stable distribution,and high potential,primarily hosting medium-to-low temperature hydrothermal resources. The thermal reservoir temperature ranges from 49.6 to 64.3 °C,with a total geothermal resource of 0.13×10¹⁸ J,geothermal water resources of 2.9×10⁸ m³,geothermal water heat of 0.05×10¹⁸ J,and a geothermal resource abundance of 15×10¹⁵ J/km²,indicating rich thermal reservoir resources. Ten evaluation factors,including structural feature, thermal reservoir buried depth, thermal reservoir thickness and thermal reservoir temperature,were selected to establish a quantitative index （Z_i）for evaluating the potential of converting long-closed wells into geothermal wells. The wells were classified,and six Type Ⅰ wells with Z_i more than 80,representing superior geothermal resource conditions,were prioritized for implementation. The pilot test on Well J-9 demonstrated a maximum allowable geothermal water exploitation of 55.46 m³/h from the Guantao Formation,indicating medium production capacity. These findings provide critical support for deepening the understanding of the Guantao Formation thermal reservoir in Shenjiapu Oilfield and Dagang Oilfield as a whole,as well as for promoting the large-scale development and utilization of geothermal resources in oilfields. This study offers valuable insights for sustainable oilfield utilization and the advancement of clean energy.

In view of the shortcomings of traditional formation pore pressure monitoring methods in the strata with strong hydrocarbon generation pressurization effects, a new method for discriminating formation pressure trend based on synergistic coupling of formation pore pressure monitoring technology and diffuse reflectance infrared Fourier transform spectroscopy（DRIFTS） technique is proposed. During the process of formation pressure monitoring while drilling, the characteristics of logging parameters （such as dc index, interval transit time, resistivity, etc.） deviating from the normal trend line are used to identify abnormal pressure formations. At the same time, DRIFTS technique is introduced to rapidly analyze the mineral composition, total organic carbon content （TOC） and vitrinite reflectance （R_o） of cuttings samples, revealing the hydrocarbon generation pressurization effects of organic matter. Taking well B in Wenchang A Sag of Pearl River Mouth Basin as an example, a chart was constructed through the synergistic coupling of formation pressure and DRIFTS techniques, and then 4 350 m was identified as the inflection point of hydrocarbon generation pressurization, and it was found that the rising trend of formation pore pressure was highly consistent with the increasing trend of TOC and R_o, which verified the effectiveness of the synergistic discrimination method. Compared with the traditional models, this method can simultaneously quantify the overpressure contributions of undercompaction and hydrocarbon generation, significantly improving the pressure discrimination accuracy of complex overpressure mechanism formations. The high-resolution analysis ability of DRIFTS technique for minerals and organic matter, dynamically combined with the data of pressure monitoring while drilling, provides more reliable formation pressure prediction and safety guidance for drilling engineering, and has important field application value.

Mud logging and well logging data play an important role in reservoir fluid identification, especially during the drilling stage. The data volumes of mud logging and well logging data depend on the number of wells in the area, and the number of samples is relatively small in terms of big data dimensions of offshore oil and gas exploration, which limits the machine learning of reservoir fluid identification due to the small amount of labeled data and leads to overfitting and poor generalization ability issues. To address the above problems, this paper proposes a fluid identification method that combines semi-supervised learning （Self-Train） with Markov Chain Monte Carlo （MCMC）. First, train the neural network model using a small amount of labeled data. Second, combining semi-supervised self learning algorithms to generate machine labels （pseudo labels） for unlabeled data. Then, using MCMC method to randomly sample and quantify the uncertainty predicted by the model, machine labels with high confidence coefficient are selected to expand the high-quality training dataset. Finally, by combining the screened machine tag with the original label data, and adopting adaptive training method to adjust and use the neural network model that is established with labeled data, a reservoir fluid identification model is created for mud logging and well logging data suitable for few-shot conditions. The model validation for new drilling wells achieved a coincidence rate of over 85%, demonstrating well application results. The reservoir identification model established after screening machine tags using the MCMC method improved the accuracy and generalization ability of the fluid interpretation model while drilling, providing effective technical support for rapid identification of fluids while drilling at the well site.

To meet the strict requirements for pressure measurement accuracy in the petroleum industry production field and address the limitations of traditional pressure gauge calibration methods,high-precision pressure sensors,automation control technology,machine vision and image processing technology are applied to develop and design the intelligent calibration device from both hardware and software aspects. The hardware focuses on the key lectotype and architecture construction of modules such as pressure sources and sensors. The software implements functions such as data acquisition,processing,storage,and automatic calibration process. Field tests and applications show that the device features high calibration accuracy,good stability and convenient operation. It can significantly improve the efficiency of pressure gauge calibration,ensure the quality of pressure gauge,and provide strong technical support for petroleum measurement work.

To reduce the workload of data acquisition personnel on the drilling site,fully reuse the collected drilling data,and achieve the effect of "one party entry,multiple parties sharing" of data,data changes are perceived through database triggers,and data heterogeneous synchronization is realized with the dynamic configuration of model transformation rules. The use of message queue to decouple the functions of data change capture and data synchronization improves system performance,ultimately leading to the research and development of a heterogeneous synchronization system for drilling data. Since the system went online,a total of 2.8×10⁸ pieces of data have been synchronized,with an average of 61.14×10⁴ pieces of real-time synchronized data per day,greatly reducing the burden of data filling in and submitting for on-site personnel,making data sharing more secure and timely,and realizing unified management of data synchronization. This system has produced a marked effect in breaking down data barriers,eliminating data silos,improving data utilization rate,ensuring data consistency,and promoting cross-disciplinary collaboration.It provides strong data support for the analysis and decision-making activities of enterprise managers.

Metamorphic buried hill is the key field of oil and gas exploration in Bohai Bay Basin. As a horst structure on the west side of the eastern depression of Liaohe Depression,the oil and gas distribution of Ciyutuo buried hill is controlled by the weathered crust structure and significant physical property differentiation. In order to clarify the cause of uneven distribution of oil and gas in Ciyutuo buried hill,the longitudinal structure division and characteristics of the weathering crust on the top of buried hill were studied by using core,thin section,well logging and analysis data,so as to analyze its evolutionary process and physical property distribution law and clarify the control effect of the weathering crust on hydrocarbon accumulation in the buried hill. The results show that the weathering crust of Ciyutuo buried hill lacks clay bands and weathered glutenite bands,and is vertically composed of leached zones and disintegration zones. The weathering crust thickness and physical properties differences control the formation of "up dip pinch out" traps,and the leached zone （permeability higher than 10 mD） is the dominant migration channel. Graded simulations of hydrocarbon generation intensity show that under high hydrocarbon generation intensity with 5×10⁶ t/km²,oil and gas can charge the disintegration zone （thickness higher than 12 m）. The results reveal the controlling effect of weathering crust on hydrocarbon accumulation in buried hill,and provide an important basis for deepening the understanding of reservoir-formation law and optimizing exploration deployment.

To address the issues of inconsistent data quality and the difficulties in multi-source heterogeneous data governance in petroleum drilling engineering, this paper designs and implements a governance system specifically for drilling data to enhance data consistency, accuracy, and usability. The system consists of the data management module, the data quality assessment module, the data governance module, and the video recognition module. The data management module mainly realizes functions such as data query, file import, and file download.The data quality assessment module evaluates data quality based on missing values, invalid values, outliers detection and correlations calculation. The data governance module corrects and supplements abnormal data through time series segmentation, working condition identification, and data interpolation. The video recognition module employs large model technology to provide dynamic intelligent monitoring for on-site safety. The trial operation of the system in Huabei Oilfield shows that it can significantly improve the quality of drilling data and provide a reliable data base for subsequent analysis and decision support.

Point dam sand bodies, as the core reservoir space in the sedimentary system of X block meandering rivers, have complex and variable internal architectures and spatial distribution characteristics, the sheet-like thick sand members formed by external genetic control are difficult to divide. Therefore, the analytic hierarchy process for reservoir architecture was adopted. Based on the spatial combination characteristics of sand bodies and their cause classification, the distribution range of effective reservoir composite meander belts in meandering rivers was determined with sand-mudstone as the boundary. From the composite meander belts, single channel sand bodies were identified. According to the migration direction of the channels, the spatial configuration relationship of the sand bodies was formed and abandoned channels were identified. With abandoned channels as the boundaries and combined with the rhythmic characteristics of point bar sand bodies, the head and tail of the point bars were determined to divide the point bar range, and then each individual point bar sand body was identified from the plane and section. The internal characteristics of point bar sand bodies were analyzed in detail. By precisely identifying lateral accretion mudstone, the depositional stages of point bars were divided. Based on the quantitative calculation of the occurrence of lateral accretion mudstone by the method of twin wells, the width of the single lateral accretion layer was approximately 430 m. Through the comparison of horizontal well drilling, the calculated results were similar to the actual drilling results. By applying this method to guide the deployment of well K-4 X, good development effects were achieved. It was confirmed that this method has strong practical guiding significance in the identification and quantitative characterization of point bars in X block.

To enhance the real-time data analysis and intelligence level of offshore oilfield mud logging operations, a large language model OffshoreGPT for mud logging tasks has been constructed. This model was pre-trained based on 7 405 structured domain paragraphs and approximately 6 000 high-quality "question-answer pairs". The Supervised Fine-Tuning and Instruction Tuning strategies are combined to improve the domain term analysis and professional text generation ability. And the full-process training is completed in a high-performance server environment equipped with multiple GPU cards, ensuring stability and fast response capabilities under complex working conditions. The test results show that OffshoreGPT has achieved an increase of 81.77% in BLEU-4 score and 63.43% in ROUGE-L score in domain knowledge questions and answers and fault diagnosis tasks. In the simulated mud logging scenarios, it can real-time identify key operational events and generate risk alerts, thereby improving operational accuracy and safety while reducing manual intervention. The model has shown good adaptability in on-site technical support, indicating that it is both feasible and advantageous for the intelligent application of mud logging operations in offshore oilfields.

As a strategic energy base in China,the potential of deep coal-rock gas resource in the Ordos Basin （buried depth more than 2000 m） is huge and is regarded as another important area for unconventional natural gas exploration and development after shale gas and tight gas. The core challenge of current industrial development is how to accurately locate sweet spots layers with the characteristics of "high gas content and high compressibility" through the multi-dimensional data integration and intelligent technical means,so as to achieve economic and efficient development of resources. In response to this technical bottleneck,a trinity evaluation system of "gas mud logging-rock pyrolysis logging-element logging" is innovatively built. That is to say,by analyzing reservoir property,source rock,gas content,flowability and brittleness,a gas-bearing dynamic characterization model,seepage capacity classification standard and brittleness index calculation equation are established. In the end,classification evaluation standards for coal-rock gas reservoirs in the 8^# coal seam of Benxi Formation （types Ⅰ,Ⅱ and Ⅲ） are established,and a spiral cognitive improvement mechanism of "data acquistion-model construction-site verification" is formed. The results show that this system has significantly improved the efficiency and accuracy of reservoir evaluation. The sweet spot identification efficiency is increased by about 20% compared with traditional methods,and the prediction accuracy rate of type Ⅰreservoirs in typical blocks exceeds 85%. The research results have been promoted and applied in many key blocks in the Ordos Basin,showing good adaptability and promotion value,which provide a replicable technical paradigm for the development of deep unconventional gas reservoirs.

With the deepening of shale oil and gas exploration and development,lithofacies identification has become a key link in shale oil reservoir evaluation and sweet spot prediction. Aiming at the problems of high cost and poor continuity of traditional lithofacies identification methods （such as core observation and well logging interpretation）,this paper divides the shale in Weixinan Sag,South China Sea into 10 lithofacies types through X-ray diffraction （XRD） and organic geochemical logging data,combend member three-end meber-third-order lithofacies classification method of "organic matter abundance+sedimentary structure+inorganic mineral content". On the basis of compiling three types of mud logging data characteristic indicators：mineral,rock pyrolysis and engineering,this paper selects 10 characteristic indicators including clay minerals,felsic minerals,carbonate minerals,TOC,HI, oil saturation,ROP, torque,S₂,S₁ to construct a shale lithofacies identification model using support vector machine （SVM） and random forest （RF） algorithms respectively. The results show that the random forest model can accurately identify shale lithofacies,and its accuracy rate is 92%. The model has a good application effect in the field,and the coincidence rate reaches 100%. The results of this study can quickly realize lithofacies identification while drilling,and provide guidance for sweet spot evaluation and fracturing stage and cluster design of shale reservoirs.

Well completion geological reports serve as critical deliverables in oil and gas exploration and development. Addressing the inefficiencies, error-prone nature, and lack of standardization inherent in traditional manual compilation methods, this study developed an intelligent generation system based on the GeoWell software. Leveraging data provided by CNOOC's EOM data lake, the system enables batch import and parsing of multi-source data including mud logging, well logging, and testing records. Guided by CNOOC corporate standards and incorporating geologist experience, it constructs a configurable rule repository and structured templates. Employing a closed-loop architecture of "data access→rule-based decision→content assembly→standard output", it integrates data mapping, dynamic content generation, and automated typesetting to achieve one-click conversion from raw data to standardized reports. Scaled application across 158 wells in the Bohai Oilfield demonstrates that this system boosts report preparation efficiency by over 73%, significantly enhances data accuracy and formatting compliance, and effectively facilitates the transition of geologists from transactional tasks to high-value analytical research. It provides a model for the digital and intelligent transformation of oil and gas geological operations.

To address issues in the exploration of Paleozoic carbonate rock buried hills of the Bozhong A structure in Bohai Bay Basin, such as inaccurate lithology naming, low precision in interface determination, difficulty in stratigraphic division and correlation, and slow identification of high-quality reservoirs, X-ray diffraction （XRD） logging technology has been introduced. By building a lithology naming triangular chart, the precise naming of carbonate transitional lithologies was achieved. Based on the "three-stage" variation law of mineral concentration in overlying mudstone, a method for early warning and identification of the buried hill interfaces was established. The fitting model combining feldspar content with natural gamma logging curve enabled fine stratigraphic division and correlation while drilling. Integrating mineral composition with rock brittleness, a rapid identification method for high-quality reservoirs based on brittleness index was established. The application of this technology to seven exploration wells in Bozhong A structure shows that the lithology naming coincidence rate is 92%, the interface determination accuracy is controlled within 3 meters below the interface, the stratigraphic horizon division error does not exceed 5 m, and the interpretation coincidence rate of high-quality reservoirs is 83%, effectively supporting drilling safety and exploratory decisions.

Cuttings fluorescent detection is an important means to judge the oil content and SG&O grade of reservoirs,but the traditional artificial interpretation has the problems of subjectivity and difficulty in quantification. To this end,an automatic identification method of fluorescent images based on HSV color space is proposed. By converting RGB images to the HSV color space and decoupling hue （H）,saturation（S）,and value （V）,the interference of uneven illumination is effectively suppressed.By combining threshold segmentation to extract fluorescent regions and statistically analyzing the proportion of pixels in different colors （e.g.,yellow-white,bright yellow,light blue）,fluorescent features can be quantitatively characterized. By further integrating deep information of geology and constructing a "depth-image" profile,the depth alignment between images and mud logging curves has been achieved. This method has been applied to the interpretation and evaluation of oil content in 1286 reservoir units of multiple preliminary prospecting wells,including well F 25 in the Ordos Basin. After verification through oil test,pressure measurement,and production dynamic data,the interpretation results of 1 103 reservoirs were consistent with the actual fluid properties. The overall coincidence rate reached 85.8%,an 18% increase over traditional manual interpretation. This significantly enhances the objectivity,consistency,and visualization level of the oil and gas discrimination,providing technical support for the automatic identification and classification of SG & O in intelligent mud logging.

Oil volume factor is an important parameter for calculating crude oil reserves by volumetric method,which is of great significance for improving the development efficiency of oil and gas fields and reducing economic risks. In order to solve the problems of high cost,no design of high pressure physical property sampling and low prediction accuracy of gas logging data comparison method for predicting oil volume factor,a new method is put forward by combining the carbon isotope logging data of natural gas with regional geologic information. Using IsoBox software,which couples the simulation of hydrocarbon generation kinetics,isotope fractionation and hydrocarbon expulsion,the GOR is calculated,and then the formation oil volume factor is obtained through oil volume factor and GOR correlation.This method is verified by using the carbon isotope logging data of natural gas from 5 exploration wells in Weizhou X 6 Oilfield. The relative deviation between the oil volume factor calculated by this method and the result of high-pressure physical property analysis is less than 6.3%,which shows that this method has high prediction accuracy. It has a good exploration application prospect.

As an important part of China's unconventional energy system,deep coal-rock gas plays a crucial role,and the precise identification of its high-quality reservoirs is significant for ensuring development efficiency and economic benefits of coal-rock gas. However,deep coal-rock gas reservoirs are generally characterized by "high temperature,high pressure,high stress and strong heterogeneity",traditional evaluation methods are difficult to meet the needs of rapid evaluation while drilling due to problems such as low timeliness,high cost and single parameter. To this end,a high-quality coal-rock gas reservoir evaluation method based on Thermogravimetric Analysis （TGA） technology is proposed. By monitoring the mass changes of slack samples returned from the wellbore during the heating process,five key parameters including ash content,temperature of maximum mass loss rate,thermogravimetric mass difference,maximum mass loss rate,and moisture content are obtained simultaneously to achieve a comprehensive analysis of reservoir quality and gas content while drilling. This technology has been successfully applied to new multi-well drilled,increasing the effective reservoir drilling rate by 8% and shortening the decision-making cycle of fracturing and layer selection by 12%. It provides a new technological approach and decision support for highly efficient exploration and development of deep coal-rock gas reservoirs.

Traditional reservoir porosity prediction methods, such as multiple linear regression, are usually difficult to capture the spatial-temporal characteristics of logging data and to grasp the complex nonlinear relationship between logging data and porosity, resulting in greater errors between predicted results and measured data. To address this, a hybrid neural network method of Convolutional Neural Network （CNN）-Long Short-Term Memory （LSTM） is introduced to expound the reservoir porosity prediction principle of CNN-LSTM hybrid neural network based on logging data. Four logging parameters with strong correlation to porosity, namely interval transit time, volume density, compensated neutron and natural gamma, were selected by mutual information method screening as the input features of the model to construct the prediction process of the CNN-LSTM hybrid neural network model. Sample data were selected and divided into train and test sets. The sample data were processed through missing value handling, standardization, and data reshaping, ultimately establishing a porosity prediction model based on the nonlinear mapping relationship between logging data and reservoir porosity. The test and assessment results of the model show that the hybrid neural network model reduces the error indices MAE and RMSE in the same well prediction to below 0.2 and 0.25 respectively, representing more than 60% lower than multiple linear regression model, more than 50% lower than recurrent neural network model, and more than 40% lower than long short-term memory network model.In the field application of well C that did not participate in training, the prediction accuracy reached 92.3%, and the application effect was good.

Accurate prediction of the gas content in deep coal seams is a key basis for selecting the "sweet spot" of coalbed methane, precisely deploying well sites for development, and optimizing fracturing schemes. However, traditional prediction models often have obvious limitations in gas content prediction, such as insufficient representation ability of complex nonlinear relationships, poor generalization performance, and being prone to falling into local optimal solutions, resulting in prediction accuracy being difficult to meet actual needs. Therefore, based on a systematic analysis of the correlation between coal seam gas content and log parameter, this paper selects five log parameters, namely interval transit time, compensated neutron, natural gamma, density and resistivity as input features, and proposes an ensemble learning model that integrates random forest and genetic algorithm to optimize BP neural networks. This model first uses 3σ criteria to clean the outliers, uses random forest to initially complete the assessment of the importance of regression features, and then fuses the prediction results as new features with the original parameters and inputs them into the BP neural network optimized by genetic algorithm for fine modeling. The performance of the model is evaluated by using 5-fold cross-validation, the results of the model test set are R² of 0.894, RMSE of 1.698, MAE of 1.313. The verification results of application wells show that the absolute prediction error of this model in wells Y-1 and Y-2 is between -1.37 and 1.39 m³/t, which is in good agreement with the measured values.This fusion model effectively improves the prediction accuracy, demonstrates good robustness and generalization ability, and provides a reliable technical method for the assessment of deep coalbed methane resources.

The outlet flow rate of drilling fluid is one of the key parameters to monitor the downhole overflow and lost circulation in mud logging engineering. Given that conventional target type flowmeters and other measurement methods are affected by non-full pipe flow regimes, on-site vibrations, and medium characteristics, resulting in low measurement accuracy and significant delay in early warning, they are unable to meet the requirements for high-precision safe drilling. A drlling fluid online overflow and lost circulation monitoring system based on radar hydrodynamometer, radar liquidometer, high definition camera, and multiparameter fusion model has been researched and developed, which realizes real-time online measurement of drilling fluid outlet flowrate under non-full pipe conditions. Compared with the comprehensive mud logging units, it can detect overflow and lost circulation anomalies 1 to 3 minutes earlier and give an alarm. The wellsite application indicates that the system is easy to install, simple to operate, and has low maintenance costs, effectively solving the problem of low measurement accuracy of non-full pipe flowrate and providing reliable technical support for drilling safety.

With the significant progress in mud logging analysis technology,cuttings mineralogical identification technology represented by RoqSCAN has become an important tool in mud logging analysis. However,this technology currently can only test the mineral composition within cuttings particles and cannot achieve automatic lithology identification,making it even more difficult to identify complex lithologies. Therefore, a complex lithology identification method based on digital rock image analysis was proposed, i.e., using edge detection and image segmentation techniques to achieve the automatic extraction of rock particles, combining particle morphology and mineral information to conduct particle-level lithology identification, and providing scientific and reliable data support for the identification and classification of complex lithologies through statistical and visual analysis of the identification results. At present, this recognition method has been successfully applied to well D 2 in Ordos Basin, significantly improving the accuracy and efficiency of identifying complex strata, and providing a new technical method for efficient identification of the cuttings in mud logging sites.

Helium is a scarce strategic resource indispensable for national defense construction and the development of high-tech industries. Helium-containing natural gas is currently the only source for industrial helium production. The existing helium detection technologies mainly rely on laboratory mass spectrometer detection and on-site chromatography+thermal conductivity detector （TCD） detection. The former has discontinuous detection data, while the latter has problems such as long cycle and insufficient detection accuracy, making it difficult to meet the real-time evaluation needs while drilling exploration. To overcome the technical bottlenecks mentioned above, the equipment for helium rapid detection while drilling was researched and developed. The equipment mainly consists of a mass spectrometry detection system, an anti-interference preprocessing system, and a supporting control software system. Core unit mass spectrometry detection system is composed of an electronic ionization system, a quadrupole mass analyser screening system, and an electronic multiplier detection system, and is equipped with special data processing software. With a rack-mounted portable design, it seamlessly integrates with comprehensive mud logging units. Its minimum detectable limit for helium is 2×10⁻⁶, with an analysis cycle of 30 s. It can realize synchronous analysis, data processing, mapping output and real-time monitoring with comprehensive mud logging units, providing equipment support and technical assurance for rapid helium detection while drilling and reserve estimate. The equipment has been applied to the fields in the Ordos Basin and the lower Yangtze Basin. The detection data show good consistency with the laboratory test results for helium from the natural gas. The application has shown significant effects, providing important technical support for the efficient exploration and development of helium-bearing gas pool.

In the process of oil and gas exploration,multi-component carbon isotope spectrometers are prone to laser wavelength drift and decreased measurement accuracy under the complex temperature conditions of drilling sites. To address this issue,a high-precision temperature control system based on a three-stage temperature control strategy has been developed. The system consists of three parts: an outer insulation box,a middle constant-temperature chamber,and a core laser temperature control unit. By implementing progressive thermal resistance,environmental interference is mitigated. The system integration tests and field measurements demonstrate that under laboratory conditions at a constant temperature of 25 °C,the laser temperature stabilizes at 37.7±0.005 °C,with a corresponding wavelength drift of less than 0.002 nm. Under field conditions with temperatures ranging from 3 to 38 °C,the system operates continuously for 72 h with a steady-state temperature control error better than ±0.01 °C. This improves the measurement deviation of CH₄ δ¹³C₁ to ±0.5‰,significantly enhancing the stability and resolution of the spectrometer. The system provides key technical support for oil and gas origin analysis and reservoir evaluation.

To meet the development needs of modular mud logging instruments and achieve the intelligence and automation of channel signal detection systems, a multi-channel signal detection system for modular mud logging instruments has been developed. The system utilizes standard signal generation circuits and data acquisition circuits for rapid, accurate and intelligent detection of the channel signals in the instrument acquisition system. The metering results have traceability and transmissibility, meeting the requirements of petroleum industry standards. The application results show that the detection system has high level of intelligence, small errors and powerful functions, realizes the accurate detection of mud logging instrument channel signal, and further improves the quality of wellsite mud logging services.

When the annulus liquid level is not at the wellhead during the drilling process, in order to solve the technical difficulties of continuousiy monitoring downhole liquid level, the lack of automatic means for identifying the fluid level depth, and the inability to link with drilling engineering parameters to guide the decision-making of the whole overflow and lost circulation process in the well opening state, the drilling-mud logging integration downhole liquid level continuous monitoring technology and its application program are proposed. Based on the actual situation on site, the study is conducted on the sensor-based design of downhole liquid level monitoring instruments and their deep integration with the compound logging. By optimizing the installation location to reduce acoustic interference, the problem of continuous monitoring in the well opening state has been solved. The correlation algorithm based on the periodic attenuation characteristics of echoes solves the problem of downhole liquid level automatic identification and improves the automation extent of continuous monitoring. The technology interacts with the compound logging system for data exchange and monitors real-time curves. Combined with the liquid level depth, the dynamic models of wellbore leakage for calculating leakage rate and the lost circulation volume were established, realizing the application of drilling engineering-compound logging integration in the case of lost returns for lost circulation. The purposes of real-time monitoring, analysis, alarm and recording of downhole conditions and overflow and lost circulation situations are achieved. In practical application cases on well sites, real-time monitoring of well opening has achieved good application results, realizing dynamic warning under lost returns for lost circulation conditions and accurately guiding plugging decisions, and providing strong guarantees for well control safety.

To address the challenges in deep exploration of Xihu Sag, East China Sea Basin, where PDC bits cause severe damage to original formation particles, making accurate analysis of clastic rock granularity from cuttings difficult, and where drilling coring and sidewall coring are costly and time-consuming, this study establishes a method of rapid and accurate clastic rock lithology granularity evaluation while drilling. Based on element logging data and geological mechanism analysis, typical element combinations related to granularity were optimized. Machine learning methods were then employed to establish predictive models for different series of strata： linear regression for Huagang Formation, and decision tree and random forest algorithms for the upper and middle sections of Pinghu Formation, respectively. This summarizes a clastic rock granularity evaluation method applicable to different series of strata in Xihu Sag. Practical applications demonstrate that this method achieves an overall accuracy rate of 89.7% in predicting lithology granularity in Huagang and Pinghu Formations of Xihu Sag, effectively identifying 7 granularity levels from mudstone to glutenite. It provides reliable technical means and reference basis for sweet spot evaluation while drilling and subsequent operational decisions in clastic rock reservoirs of Huagang and Pinghu Formations in Xihu Sag.