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.

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.

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.

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.

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.

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.

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.

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.

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.

The carbonate gas reservoir of Leikoupo Formation in western Sichuan has good potential for exploration and development, making it a key area for increasing natural gas reserves and production. Porosity is one of the important parameters for reservoir evaluation in the Leikoupo Formation, but it is difficult to obtain directly during drilling, which restricts the development of real-time interpretation and evaluation. Through research, the impact of different degrees of dissolution on porosity is analyzed. Based on elemental logging data, porosity calculation models are established based on the degree of dissolution, specifically for formations with no dissolution, partial dissolution, and full dissolution. Ultimately, a method for real-time porosity calculation while drilling in the Leikoupo Formation of western Sichuan is developed. Field applications in over 10 wells have demonstrated the effectiveness of this method. The calculation results provide robust support for real-time interpretation and evaluation, offering a basis for formation evaluation of the Leikoupo Formation in western Sichuan.

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 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.

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.

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 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.

The unconventional hydrocarbon reservoirs develop in the 2nd Member of Liushagang Formation,Yongan structure,Fushan Sag. Clarifying the microscopic pore structures within its reservoir sandstones is crucial for the development of such hydrocarbon reservoirs. Through various experimental methods,an accurate 2D-3D analysis on the pore structures of the sandstone reservoirs in the 2nd Member of Liushagang Formation is conducted,the main findings are as follows. First, results from thin section examination of rock,field emission scanning electron microscope,MaipSCAN （rock and mineral scanning electron microscope）,cast thin section identification,environmental scanning electron microscope,and high-pressure mercury injection experiments show that the sandstone types of the 2nd Member of Liushagang Formation are mainly lithic sandstone and feldspathic litharenite,with high quartz content. The detrital components are dominated by rock cuttings and quartz,and the heteroatom fillers mainly consist of clay minerals and a small amount of fine silt debris. The pore types in the reservoir space are primarily composed of intergranular residual pores,clastic grain dissolution pores,and grain fractures,with macropores being predominant. Second,the analysis of the constant speed mercury injection experimental data shows that the pore throat types in the reservoirs of the study area are mainly pore-dominated,pore throat co-controlled, and throat-dominated types. Third,the results of the gas adsorption （N₂ and CO₂） experiments show that the reservoir space is mainly composed of wide,narrow and long slits in the shape of plates,and macropores with pore radii ranging from 50 to 55 nm are mainly developed. Fourth, the distribution and connectivity characteristics of the microscopic pores in the 2nd Member of Liushagang Formation were characterized by 3D reconstruction using FIB-SEM（Focused Ion Beam Scanning Electron Microscopy） and micro-nano CT. The experimental results show that the pore structures of the reservoirs in the study area have good connectivity. Based on the above analysis results,a multi-scale and full pore size joint characterization was conducted,indicating that the pore structure of the sandstones in the study area is dominated by macropores,with underdeveloped microscopic pores and mesopores. By conducting a comprehensive 2D-3D analysis on the pore structures in the 2nd Member of Liushagang Formation,Yongan structure,the microscopic pore structure characteristics of the reservoirs in the study area were deeply dissected,providing more reliable technical support for the later stage of oilfield development in this area.

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.

In order to study the formation and distribution law of the dominant reservoirs in the deep sag area of the Paleogene system in Huanghua depression,this paper analyzes the sandstone reservoir characteristics, the main controlling factors and development patterns in the deep sag area based on the drilling coring of wells CT 1 and HT 1,and comprehensively analyzes the data of drilling,mud logging,well logging and seismic data. The study concludes that the formation and distribution of dominant reservoirs in the deep sag area of Huanghua depression are controlled by sedimentary and diagenetic facies. The lithology of sandstone reservoirs is mainly feldspathic sandstone, the pore structure is dominated by class Ⅱ-Ⅲ,and the reservoirs are dominated by low porosity-ultra-low porosity, low-permeability-ultra-low-permeability reservoirs,and dominant sedimentary facies with larger thickness,coarser grains, and higher maturity are the basis for the development of effective reservoirs in deep sag area. Under the control of sedimentary microfacies, overpressure porosity-keeping and eatly-stage strong charging of oil and gas, effective reservoirs are still developed at a burial depth of 5 000 m in the deep sag area.A favorable development pattern is formed, featuring "thick-bedded sandstone with rapid deep burial,porosity preservation and early-stage strong charging of oil and gas in dissolved pores." In 2024,wells XH 1 and XH 2 were deployed in the tectonically low part of the CT 1 well area of the deep sag area in the east of Cangdong Sag,and obtained high-yield industrial production rates of 25.3 m³/d and 59.2 m³/d respectively, which confirmed that the deep sag area has the potential of benefit development.

There are multiple sets of source rocks and oil-bearing series in the Nanpu No.3 structural belt. To identify the oil-source relationship in the Nanpu No.3 structural belt,the characteristics of source rocks and crude oil were analyzed and compared by using multiple geochemical test data. Results show that the Es₃ Formation is good-very good source rocks with type Ⅱ kerogen. It is dominated by input of aquatic organic matter and formed in an alternating depositional environment of fresh water and brackish water with weakly reducing,and has high hydrocarbon generation potential. The Es₁ Formation is fair-quality to high-quality source rocks with dominant type Ⅱ and little amount of types I and Ⅱ-Ⅲ kerogen. It is characterized by terrestrial organic matter and developed in a brackish water depositional environment with weak oxidation and weak reduction,and has medium hydrocarbon generation potential. The Ed₃ Formation is good-very good source rocks with dominant type Ⅱ kerogen and contains a small amount of types I and Ⅱ-Ⅲ kerogen. It is characterized by mixed organic matter input from aquatic and terrestrial sources and formed in a freshwater depositional environment with weak oxidation and weak reduction, having medium hydrocarbon generation potential. The distribution characteristics of normal alkanes,sterane,and terpane in the crude oil of the under-source,in-source and upper-source accumulation combinations are significantly different. Based on the comparison of biomarker compound fingerprint features,the crude oil in the under-source accumulation combination is derived from the Es₃ source rocks. The crude oil in the in-source accumulation combination is either contributed by Es₁ source rocks or mixture contributed by Es₁ and Es₃ source rocks. The crude oil in the upper-source accumulation combination is derived from the Ed₃ source rocks. The findings in the study can provide a basis for further understanding of the rule of hydrocarbon migration and accumulation in the Nanpu No.3 structural belt.