To address the issues of traditional mud logging interpretation charts relying on manual drawing,inconsistent standards,low efficiency,strong subjectivity,and difficulty in meeting the demands of fine mud logging interpretation for complex hydorcarbon reservoirs,intelligent generation and intelligent interpretation technology for mud logging interpretation charts were researched and developed.It uses adjacent well mud logging data as the core data source,collecting key mud logging parameters such as gas logging,rock pyrolysis,and light hydrocarbons,and completing data cleaning and standardization preprocessing. Use the grey relational analysis method to screen sensitivity parameters that are highly correlated with reservoir oil-gas bearing properties,and build an intelligent chart generation model. By combining with a Bayesian-weighted global-local fusion method,the boundary lines of the intersection charts are scientifically delineated,simultaneously generating standardized mud logging interpretation charts and quantitative interpretation standards adapted to the study area. Construct a comprehensive scoring evaluation model of multiple indicators based on the entropy weight method.Compare and analyze the target well mud logging data with the generated charts and standards,and automatically output mud logging interpretation conclusion of the target horizons through the intelligent discrimination algorithm. Select data from multiple adjacent wells in block G of Liaohe Oilfield for experimental verification. After this technology was applied to the interpretation of target horizons,the accuracy rate of small-layer discrimination in the block reached 94.64%,the interpretation coincidence rate was increased by 5.78% compared with traditional manual interpretation,and the efficiency of single-well interpretation was improved by more than 97%. This enhanced the accuracy,consistency and efficiency of mud logging interpretation. The practice has shown that this technology can achieve efficient reuse of adjacent well data and automated generation of interpretation charts and standards,effectively improve the intelligence level and interpretation accuracy of mud logging interpretation,adapt to complex hydrocarbon reservoir mud logging interpretation scenarios,and possess good engineering practicality and promotion value.

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.

To address the challenges in offshore oilfield mud logging operations,including diverse data types professional knowledge crossover,response lag,and complex network conditional complexity,an improved Retrieval-Augmented Generation （RAG） intelligent knowledge system-OffshoreRAG has been proposed and implemented. Based on the traditional RAG framework,the system introduces domain-oriented enhancements：for the first time,professional data such as mud logging curves are deeply integrated through a multimodal knowledge processing supported by a metadata-based knowledg management structure. A hybrid retrieval strategy adopting structural tags with vector recall is designed,incorporating a Cross-Encoder for fine-grained semantic re-ranking. And in PostgreSQL,pgvector columns,table partitioning,and parallel execution plans are integrated to achieve second-level response mechanism,forming a traceable closed-loop process from data acquisition to question and answer generation. Experiments using over 1 800 real offshore oilfield mud logging reports,well test design files,and equipment documents as multimodal data constructed a test set of 1 000 queries covering operational procedures,equipment fault diagnosis,and standards lookup. Field deployment application results show that the system effectively integrates multi-source information,significantly enhancing engineers' decision-making efficiency and emergency response capability in high-intensity work environments. The results demonstrate the feasibility and significant advantages of multimodal RAG systems tailored to specific industrial scenarios. In the future by expanding real-time curve data access capability,increasing concurrency and latency simulation tests,and optimizing curve feature extraction and storage strategies,the system's stability and engineering value under complex offshore conditions is further improved.

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.

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.

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.

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.

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.

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.

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.

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.

The current artificial intelligence early warning technology for drilling safety can only process single-modal data,be difficult to integrate multi-source information,and lack a cross-validation mechanism,making it difficult to build a complete chain of explanation and analysis. To address the technical issues in the intelligent construction of oil and gas wellbore engineering risk areas,such as edge data cleaning,multimodal model research and development,and model collaborative reasoning,and to comprehensively enhance the level of drilling risk warning,a large model for lost circulation and drill pipe sticking risk scenarios has been successfully developed and applied by adopting the methods of lost circulation and drill pipe sticking events automatic identification for multi-model collaborative reasoning,ultimately achieving the monitoring of over 12 drilling risk related parameters at the second level. Applications in the Liaohe and Southwest oil & gas fields indicate that,compared with the traditional single risk models,this model improve risk identification rates for lost circulation and drill pipe sticking to 88.89% and 89.23% respectively,the daily false alarm times are controlled below 10,and daily real-time data analysis and automatic abnormal report generation for 50 wells are achieved. The practical implementation of this model services verified the effectiveness of the large model for lost circulation and drill pipe sticking risk scenarios in the oil and gas wellbore engineering field. The model provides technical support for the intelligent development of regional companies.

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.

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.

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.

During oil and gas exploration and development,accurate identification of formation lithology serves as the fundamental work for reservoir evaluation,hydrocarbon-bearing zone identification,and drilling operation guidance. Traditional lithology identification methods suffer from high cost,long cycle time,and poor spatial continuity,making it difficult to meet the demands of modern high-efficiency exploration. Therefore,it is urgent to develop an efficient and automated lithology identification method using geophysical data. To this end,this study integrates the U-Net network with a residual network to perform lithology homing using well logging data,aiming to reduce manual workload and improve identification accuracy and efficiency through deep learning models. The residual blocks introduced into the model to enhance the efficiency and stability of feature extraction,while the attention module improved sensitivity to lithological boundaries. Moreover,compared with traditional processing workflows,the well logging curves are standardized during the data preprocessing stage,and a depth-slice dataset is constructed via statistical median extraction to enhance the model's generalization ability and interpretability. The research results indicate that,compared with traditional manual lithology homing,the deep learning-based method offers advantages such as higher classification accuracy and more precise homing identification. The method was applied to well ZJ 143 in the Zhongjia Block,achieving an identification accuracy of 93.8% and a speed increase of 6-8 times compared to manual interpretation.

The accurate assessment of the gas concentration in deep coal-rock gas reservoirs is crucial for the prediction of resource potential and the selection of development sweet spots. In response to the bottleneck of high cost and poor timeliness in traditional coring desorption methods,this paper systematically analyzed the occurrence mechanism of deep coal-rock gas using the No.8 coal in the Ordos Basin as the research object,and clarified seven main geofactors,including macro coal-rock type,coal-rock reservoir thickness,coal content,ash content,metamorphic grade,total organic carbon content （TOC）,and sealing gland thickness. On this basis,the while drilling data from gas logging,geochemistry,and element logging etc.,were used to screen sensitive parameters. A gas concentration comprehensive prediction model was established through the multi-parameter fusion method. The model applied in Block X of the Ordos Basin achieved a prediction accuracy of over 85%. By combining the reservoir hydro-fracturing potential characterization parameters,the geology-engineering dual sweet spot comprehensive evaluation standards were formed,significantly improving the efficiency of sweet spot identification and the timeliness of exploration decisions,providing new theoretical basis and technical means for the economically efficient development of deep coal-rock gas.

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.

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.

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 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.

In response to the challenges faced in the reserves that are hard to put into production in China,such as strong reservoir heterogeneity and complex engineering conditions,a "intelligent+diversified" 3D geological modeling and dynamic technology system has been constructed based on the fast and slow loop iterative theory of geology-engineering integration. This system achieves the collaborative optimization of geological understanding and engineering execution through the real-time data mutual feedback and dynamic iteration between the slow loop （geological modeling and conceptual design） and the fast loop （drilling construction and fracturing implementation）.Key technologies include holographic spread-spectrum fracture identification technology based on deep learning,"rotary steering+screw" composite drilling speed-up template and "dense cutting+CO₂ energy storage" differentiated fracturing process. In the application of block D 13 in Xinjiang Oilfield,the drilling cycle has been shortened from 133 d to 60 d,with a complex time efficiency of less than 1.8%. The estimated ultimate recovery （EUR） of a single well has reached 4.3 × 10⁴ t,achieving economic development of reserves that were hard to put into production. This achievement provides theoretical methods and technical models for the intelligent development of unconventional oil & gas reservoirs,and it has guiding significance for ensuring national energy security.

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.

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.

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 ensure efficient and safe operation of gas gathering stations in oil and gas fields ,based on analyzing the existing production process,equipment management and safety guarantee system of gas gathering stations,the paper takes the production management of gas gathering stations in Sulige Gas Field as an example to points out the efficiency limitations of the traditional mode under complex working conditions and large-scale production. A full-link integrated intelligent digital management and control system of "sensing-transmission-governance-decision-execution" is constructed to realize efficient collaboration and intelligent control of the whole production process. The system mainly has three core modules：digital infrastructure,data fusion platform and intelligent analysis application. Field application shows that compared with manual operation,the intelligent digital system shows faster response,higher regulation accuracy,better production control efficiency,smaller production fluctuation and more stable natural gas supply. The digital-intelligent construction of production management for unattended gas gathering stations is of great significance in optimizing production management,strengthening safety guarantee,improving production efficiency,reducing operation cost and realizing resource optimization.

The tight sandstone reservoirs in Shenfu Block are characterized by low porosity, low permeability, small pore throats, and strong heterogeneity, leading to significant deviations between actual productivity and predicted values after fracturing in some optimized reservoirs. To address this core issue, a geological sweet spot index（D_G） was constructed based on key parameters such as excavation effect index, permeability, total hydrocarbon content, and reservoir pressure. Combined with brittleness index and reservoir stress difference coefficient, an engineering sweet spot index（F_E） was established, and then coupled to form a reservoir geology-engineering dual sweet spot index（S_I） evaluation model. Analysis reveals that sand stone confining pressure difference is greater than 3.3 MPa, with S_I greater than 0.44, indicating Class I sweet spot area. When S_I is of 0.35-0.44, it is Class Ⅱ sweet spot area. When S_I is less than 0.35, it belongs to Class Ⅲ non-sweet spot area. Application examples verify that this dual sweet spot evaluation model simultaneously considers the differences in confining pressure difference and intrinsic reservoir quality, effectively screening high-quality stimulation targets, providing reliable technical support for the development deployment of tight sandstone reservoirs and the optimization of fracturing sweet spot areas.

In order to solve the problem that it is difficult to quantitatively control the wellbore trajectory in blind side-tracking of PX 101 deep ultra-high pressure complex well in carbonate formation in Penglai Gas Field,Sichuan Basin,this paper analyzes the element composition and identification elements of rock cuttings and cement plugs based on high-precision element analysis technology,establishes a multi-linear regression identification model,and realizes the trajectory control of blind side-tracking of deep ultra-high pressure complex well. The results show that：（1）The characteristic identification elements of formation cuttings and cement plugs are Mg,Si,Al,Ca,Fe;（2）Based on the characteristic identification elements,multi-linear regression is established to calculate the side-tracking coefficient. According to the side-tracking coefficient while drilling,multi-factor comprehensive quantitative control of the wellbore trajectory change can be realized. The multiple linear regression model established by element logging technique can quantitatively evaluate the proportion of cuttings,wellbore trajectory change and wellbore stability of blind side-tracking. This study has achieved good application effect in blind side-tracking of PX 101 deep ultra-high pressure complex well.

To realize the exploration and development of the deep coal-rock gas in Su X block, northern Sulige Gas Field, the research method of "experimental analysis-well logging modeling-planar distribution analysis-favorable target optimization " was adopted, and a systematic evaluation was conducted on the 8^# coal seam in Benxi Formation of this block. Based on laboratory data from key cored wells, through systematic analysis of the characteristics such as the gas concentration, petrology, physical properties, microscopic pore structure and rock mechanics of the coal seams, it has been made clear that the coal seams in this block have the reservoir bases of the poor development of a primary-cataclastic texture, millipore and cleat, ultra-low permeability, medium brittleness, etc. Through the regression analysis of core experimental data and corresponding logging curves, a well logging interpretation model for key parameters such as industrial components, gas concentration, porosity, and permeability applicable to this block was established, achieving accurate and continuous evaluation of coalbed methane reservoirs. On this basis, the target coal seam′s buried depth, thickness, spatial distribution of interbedded gangue layers and the other key geological conditions were comprehensively analyzed, and the evaluation criteria of favorable targets were established from the coupling perspective of multiple factors such as coal seam thickness, planar distribution characteristics, coal-rock ash, coal-rock gas concentration, tectonic setting, roof lithology and the number of interbedded gangue layers. The favorable target areas for coal-rock gas development have been delineated, providing an important basis for the next exploration deployment of deep coal-rock gas in this block.

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.

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.

The current sensor verification work faces issues such as decentralized data storage, non-uniform management standards, and inefficient cross-departmental collaboration, resulting in blocked information flow and difficulties in data integration and analysis, and severely restricting the improvement of verification efficiency and the digitalization process of the industry. To overcome the above difficulties, a set of fully functional and easy-to-operate data management system for mud logging sensor verification has been designed. The system adopts a modular interface architecture, integrating functional modules such as sensor management and report management. Through standardized process design, the process of generating sensor verification reports has been standardized and made more efficient. Unified storage of verification data breaks data silos, multi-dimensional statistical analysis supports scientific decision making, the electronic report template ensures the format is specified and unified, and the cross-departmental data sharing mechanism enhances collaborative efficiency. Practical applications have shown that the system can significantly enhance management efficiency and reduce labor and material operating costs. At the same time, the credibility and compliance of the verification results are guaranteed through the whole lifecycle data traceability function, which strongly propels the normalization and digital management process of sensor verification work, provides solid technical support for the high-quality development of mud logging industry, and has significant practical value in improving the overall efficiency of the industry and promoting data assetization operation. It also provides a replicable technology paradigm for the digital transformation of the industry.

The Chang 7 Member shale oil reservoirs in the Yishaan Slope structural belt of the Ordos Basin exhibits complex and diverse lithologies. Reservoir properties such as porosity and permeability vary significantly,while the content and distribution of various minerals within the reservoirs also show marked differences. To achieve precise evaluation of the Chang 7 Member shale oil reservoirs and provide a solid foundation for subsequent exploration and development decisions,rock-mineral scanning logging technology was introduced to the field. By analyzing scanning data from multiple wells within the same block,this study established a comprehensive quality computing method and comprehensive evaluation criteria for the four properties of the Chang 7 Member shale oil reservoirs,achieving significant application results in several key areas. In quantitative micro-reservoir physical property analysis,it enables precise characterization of pore structure and fluid distribution,providing more accurate data support for productivity assessment. In detailed rock mechanics analysis,it accurately captures mechanical properties,aiding in optimizing fracturing schemes. For real-time optimization of horizontal well geosteering trajectories,it delivers timely and precise geological information to drilling operations,significantly enhancing drilling efficiency and success rates. In sweet spot prediction and evaluation,comprehensive analysis on multi-parameter enables precise identification of high-potential development zones. For personalized fracturing design,customized fracturing plans tailored to distinct reservoir characteristics significantly enhance fracturing effectiveness. Taking the application at Well H 701 in the Yishaan Slope of the Ordos Basin as an example,this paper validates the excellent applicability of rock-mineral scanning logging technology in shale oil reservoir evaluation. This not only accumulates valuable experience for shale oil reservoir evaluation in other similar basins or regions but also provides important reference for broader unconventional energy reservoir evaluation.

To address the frequent wellbore instability issues during drilling in the mud shale formations of the third Member of Shahejie Formation in Qikou Sag,Dagang Oilfield,this study comprehensively employs techniques such as XRF,XRD,formation pressure test,and drilling fluid evaluation to reveal the main controlling factors of wellbore instability and propose optimized countermeasures. Key findings are obtained in five aspects. First, an elemental combination of low Ca content （4.2%-5.1%）,high Fe content（3.5%-4.7%）,and low Al content （5.7%-7.0%） is prone to inducing wellbore instability. Second,the brittleness index model constructed based on elemental and mineral characteristics indicates that the brittleness index of the block-falling section is significantly higher than that of the non-collapsing section. High brittleness is an intrinsic key factor inducing wellbore instability. Third,when the content of carbonate minerals is low （18%-23%）,that of clay minerals is medium（23%-40%）,that of quartz is high（25%-40%）,the risk of collapse significantly increases. On this basis, when the illite content exceeds 50%,the risk of collapse increases.Fourth,Reducing the hole deviation angle and drilling along the direction of the minimum horizontal principal stress can enhance stability. Fifth, drilling fluid experiments show that the water-base drilling fluid systems （BH-KSM、BH-WEI） has a shale expansion ratio in the block-falling that is 1.43%-3.84% higher than in the non-collapsing section, with a decrease of 17.85% to 18.30% in rolling recovery rate, while the oil-base drilling fluid system（BH-OBM） exhibits excellent overall performance for both types of rock cuttings, the maximum expansion ratio caused by it in the block-falling section is only about 1.41%, and the average recovery ratio loss is about 2.5%, indicating a good inhibitory effect. The recommended on-site priority is BH-OBM>BH-KSM>BH-WEI. The findings provide a theoretical basis and technical support for the safe and efficient development of shale oil in Qikou Sag.

The Upper Paleozoic gas reservoir in Jingbian Gas Field is a typical tight sandstone gas reservoir with abundant reserves and resources, and the sandstone geological accumulation conditions of the He 8 Member of Shihezi Formation and Shanxi Formation are superior, which are the main successor layer is for long-term stable production of the gas field. However, the strong reservoir heterogeneity and great difficulty for gas-bearing property evaluation severely restrict the efficient development of gas field. In order to further clarify the reservoir characteristics and development potential of tight sandstone gas reservoirs, based on core observation, thin section identification, physical property testing, logging interpretation and other technologies, combined with gas testing and production data, the fine study on reservoir characteristics and gas-bearing property logging evaluation of He 8 Member are deepened. The gas-bearing property lower limit of the reservoirs was determined through the establishment of well logging crossplot charts, and on the basis of detailed classification and evaluation of the reservoirs, the favorable gas-bearing areas of the reservoirs were evaluated and predicted. The results show that the rock types of He 8 Member are mainly detritus sandstone and detritus quartz sandstone, quartz sandstone is secondary, but the physical properties of quartz sandstone is better than detritus quartz sandstone and detritus sandstone. Secondary porosity such as rock debris dissolution holes, inter-granular dissolution holes, and inter-crystalline holes control the formation of effective reservoir space. The predicted favorable reservoir gas-bearing areas are classes Ⅱ and Ⅲ, which are distributed in the central and eastern parts of the study area. The study results provide a certain geological evidence for development potentials and exploration employments of the Upper Paleozoic gas reservoirs in Jingbian Gas Field.

To solve the reservoir effectiveness identification problems in south Kaiping Oilfield, Pearl River Mouth Basin,based on the characteristics of well logging and mud logging technologies, the methodology for rapid evaluation while drilling with regional characteristics and well logging and mud logging combination for low-porosity and low-permeability reservoirs was developed. The specific methods are as follows. （1） Establish a reservoir effectiveness index（I_D） to rapidly evaluate reservoir effectiveness by statistically analyzing mobility data of formation pressure measurement. （2）Correct the FLAIR gas logging data to obtain corrected methane content per unit volume of rock（VC1）, and use the normalized abnormal multiple value （AC1） to construct the reservoir oil saturation index（X）. （3）Based on regional pressure measurement sampling and testing data calibration, a chart and criteria for well logging and mud logging combined interpretation and evaluation were constructed by using I_D and X, realizing rapid evaluation of low-porosity and low-permeability reservoirs. The application results at the well site show that the methodology has a coincidence rate of over 85% in rapid interpretation while drilling in south Kaiping Oilfield, effectively guiding the subsequent formulation of cable pressure measurement sampling plans and operating decisions. It has achieved considerable economic benefits and has broad prospects for promotion and application, exploring a new way for rapid evaluation of the complex reservoirs.

In the absence of LWD data,in order to ensure the timely and accurate lithology identification,reservoir physical property evaluation,pressure monitoring while drilling for deep high pressure formation in Wenchang A sag,the Pearl River Mouth Basin,as well as to meet the needs of multi-technology fusion,DRIFTS（Diffuse Reflectance Infrared Fourier Transform Spectroscopy） cuttings logging technology was introduced.Taking the application of well WC-X 1 in Wenchang A Sag of the Pearl River Mouth Basin as an example,based on the analysis of the technical principles,the sample analysis and processing workflow was optimized,and a study on multiple linear regression fitting of natural gamma （D_GR） based on mineralogical analysis was carried out. Based on TOC and maturity parameters,the source rocks are evaluated. Comprehensively evaluate the pressure while drilling based on montmorillonite content,TOC,and other parameters. Evaluate reservoir quality through the mineral reservoir quality index （D_RQI）,and establish reservoir classification standards based on logging porosity and the mineral reservoir quality index. This standards are used for rapid evaluation and decision-making while drilling,showing the excellent prospects of this technology in the field of interpretation and evaluation while drilling.

With the continuous deepening of the natural gas development process, the X block of Sulige Gas Field has gradually rolled from the enrichment area to the peripheral area for production. The effective reservoirs have gradually thinned, and the gas bearing properties distribution has become complex. Conventional post-stack seismic inversion has become difficult to meet the current requirements of reservoir prediction and gas-bearing property detection. Therefore,using common reflection point （CRP） gather data, pre-stack elastic parameters inversion is carried out to quantitatively predict the distribution of effective reservoirs in He 6, He 8 and Shan 1 Members.First, optimize the trace gather and apply travel-time correction method to improve the consistency of the trace gather data. Furthermore, the Xu-White model is applied for rock physics modeling and shear wave velocity prediction, obtaining shear wave velocity data that meets the requirements of pre-stack elastic parameters inversion. Subsequently, a target bed petrophysical analysis is carried out, confirming that the longitudinal and transverse wave velocity ratio can effectively distinguish lithology, and the crossplot analysis for it and P-wave impedance can effectively identify gas-bearing properties. Finally, the wavelet parameters are optimized to obtain the pre-stack angular seismic wavelet with a higher correlation coefficient, and the effective reservoir thickness and distribution are quantitatively predicted by the pre-stack elastic parameters inversion. The key findings involve three aspects. （1） A threshold value of 1.82 for the longitudinal and transverse wave velocity ratio can distinguish sandstone from mudstone, and a threshold value of 11 ‍000 （g/cm³）·（m/s） for P-wave impedance can distinguish effective reservoirs from dry layers. （2） The sandstone thickness is negatively correlated with the longitudinal and transverse wave velocity ratio, while effective sandstone thickness is positively correlated with the number of crossplot data points between the longitudinal and transverse wave velocity ratio and the value below the P-wave impedance threshold. （3） Based on the inversion results, nine well locations were implemented, proving the pre-stack elastic parameters inversion method provides a reliable basis for the subsequent rolling production and capacity replacement target optimization in the X Block.

To explain the genesis of the "knife-cut-like" texture in rhyolite from Sheshan, Shanghai, and to explore its enlightenment significance to the formation mechanism and evaluation methods of rhyolite reservoirs, through field observation, thin section analysis, and Bohai downhole data analysis, the development stages（fracture formation period, I-shaped period, V-shaped period and W-shaped period） were divided, and the microscopic pore structure and dissolution characteristics of the rhyolite were also analyzed in depth. The results indicate that the rhyolite "knife-cut-like" texture is a product of preferential dissolution of fractures under prolonged freshwater leaching, demonstrating rhyolite′s high inherent dissolubility, and fractures critically facilitate secondary dissolved pore formation, both of which constitute the core mechanism of reservoir formation. Based on this, a reservoir evaluation method combining element logging（K element characterizing dissolution factor, Si element characterizing devitrification factor） and a standardized mechanical specific energy model（fracture factor） was proposed. The comprehensive evaluation models for different zoning（weathering crust/parent rock zone） were established using the Analytic Hierarchy Process（AHP）, achieving a 91% coincidence rate with well logging interpretation in well X application within Bohai Sea, and providing pivotal evidence and theoretical support for the coupling mechanism of dissolution-fracture in volcanic rock reservoirs.

Aiming at the problems of uncertainty and instability of rock cutting fluorescence observed by human eyes in the field exploration and development, research on the method of quantitative calculation of rock cutting fluorescence content based on image processing technology was carried out. After comparing characteristics of Robert, Prewitt, and Sobel operators, Sobel edge detection operator and morphological algorithm are finally selected to accurately lock the rock cutting area, and then the images of the rock cutting area are converted to HSV space to extract specific fluorescence areas, so as to realize the quantitative expression of the fluorescence content of rock cuttings. Meanwhile, through comparing, it can be seen that the error between this method and the manually chosen region is less than 5%, and the error of fluorescence content extraction is less than 0.2%, the correct rate of field application is 98.35%, which indicates that this method has a high degree of accuracy, and it can effectively overcome the problems of uncertainty and insufficient quantification of human eye recognition, enhance the mud logging work efficiency, and have great significance in advancing the intelligent development of the mud logging technology intelligently.