Deepwater gas field development faces complex challenges such as ultra-deep water, strong multi-field coupling, and high operational risks, including hull stability risks, difficulties in reservoir characterization, limited accessibility of monitoring data, and the complexity of integrated production management. Traditional approaches often rely on fragmented single-module simulations and manual decision-making, resulting in delayed model updates, isolated information, and the inability to achieve end-to-end collaborative optimization across reservoirs, pipeline networks, and platforms. Digital-twin technology overcomes these limitations by breaking down data silos, enhancing model coupling, and reducing decision latency. It enables real-time interaction, closed-loop control, and full-chain integrated management, thereby eliminating information barriers among reservoirs, wellbores, pipelines, and platforms and providing coordinated, efficient production control and safety assurance for deepwater gas fields. Focusing on the “Deep Sea No. 1” production platform, this study explores the construction of a production digital-twin system that spans the entire business chain of reservoir-wellbore-pipeline network-platform-operation-finance. First, the research progress of digital twins in oil and gas production is systematically reviewed, including typical modeling methods, technical frameworks, and engineering practices. Secondly, a modular hybrid modeling approach integrating physical mechanism models and data-driven models is proposed, establishing a complete modeling workflow comprising system decomposition, model construction, data integration, optimization solving, and feedback control. Third, based on the actual application scenario of the “Deep Sea No. 1” platform, digital-twin modules are developed for mooring and hull management, flow assurance management, intelligent reservoir management, and 3D visualization, enabling early warning, predictive maintenance, decision support, immersive visualization, and full-chain closed-loop control. Field application results demonstrate that the system significantly improves the automation and intelligence of the platform, reducing production allocation calculation time from 4-5 days to less than 1 hour with prediction accuracy exceeding 90%. Finally, in response to current issues such as limited model transferability and heavy manual intervention, this paper suggests establishing a linkage framework of large and small models, strengthening integration with subsea control systems, and building a full-lifecycle digital-twin system. The research results provide a feasible technical pathway and engineering reference for the intelligent and efficient development of deepwater gas fields.

To address the challenges of conventional temporary plugging agents in oilfield development, such as inefficient gel breaking at later stages, prolonged degradation time, low gel strength, and significant permeability damage caused by residues, this study developed a self-degrading nano-composite gel temporary plugging agent (PAE) based on a physicochemical cross-linking strategy. The PAE was synthesized via free radical polymerization in aqueous solution using acrylamide (AM), acrylic acid (AA), polyethylene glycol diacrylate (AE), and hydrophobic monomer stearyl methacrylate (SMA), with nano-silica (SiO₂) incorporated to reinforce the cross-linked network. The effects of cross-linker (MBA) dosage, hydrophobic monomer content, initiator (APS) concentration, and temperature on gelation time and strength were systematically investigated. The degradation behavior of PAE under varying temperatures (70-120 ℃), pH (3-12), and salinity (20-50 g/L) was elucidated. Characterization techniques including Scanning Electron Microscopy (SEM), Fourier-Transform Infrared Spectroscopy (FTIR), and Thermogravimetric Analysis (TGA) were employed to analyze the microstructure, chemical composition, and thermal stability of PAE. Experimental results demonstrated that under the optimized conditions (monomer concentration 8%, APS 0.2%, SMA 0.4%, and temperature 70 ℃), PAE exhibited controllable gelation time (30-120 min) and formed a dense three-dimensional network with a gel strength of grade 9 (no deformation upon inversion). The degradation time of PAE ranged from 3 to 10 h at 70-120 ℃, with post-degradation viscosity below 10 mPa·s, significantly outperforming conventional agents (>96 h). Sand-packed tube tests revealed a breakthrough pressure gradient of 1.870 MPa/m and a plugging efficiency exceeding 90%. Core flooding experiments confirmed a permeability recovery rate above 90% after gel breaking, indicating minimal formation damage. Mechanistic studies revealed that the high plugging strength of PAE originated from the synergistic enhancement of physicochemical dual-crosslinking networks and nano-SiO₂, while self-degradation was achieved through ester bond saponification under alkaline conditions and dissociation of hydrophobic association networks. This research provides a theoretical foundation and technical solution for developing high-performance, environmentally friendly temporary plugging agents in oilfield applications.

With the continuous advancement of oil and gas exploration technologies, unconventional hydrocarbon reservoirs have emerged as a pivotal domain for global energy resource augmentation and production enhancement. However, the inherent characteristics of low permeability, dense rock matrix, and complex heterogeneity in these reservoirs pose substantial challenges to conventional logging interpretation methodologies, particularly in constructing theoretical models, deriving empirical formulas, and inverting reservoir parameters, thereby hindering accurate reservoir identification and efficient development. The recent breakthroughs in artificial intelligence (AI) technologies have provided innovative solutions for logging interpretation in unconventional reservoirs. Through systematic analysis of cutting-edge research achievements worldwide, this paper first elucidates the core geological characteristics and evaluation challenges of unconventional reservoirs. Subsequently, it comprehensively summarizes the implementation modalities and operational efficacy of AI techniques, including machine learning and deep learning algorithms, in critical logging interpretation processes such as lithology identification, porosity prediction, permeability estimation, and hydrocarbon-bearing potential assessment. The study particularly highlights the transformative capabilities of convolutional neural networks in processing multi-scale logging data, recurrent neural networks in handling time-series measurements, and ensemble learning approaches in enhancing prediction accuracy under high-dimensional parameter spaces. The research demonstrates that AI-driven approaches achieve remarkable performance improvements compared to conventional methods, with reported accuracy enhancements of 25%~40% in lithofacies classification and 15%~30% reduction in mean absolute error for porosity estimation across various case studies. Furthermore, advanced deep learning architectures have shown exceptional capability in capturing nonlinear relationships between logging responses and reservoir properties, effectively addressing the “low signal-to-noise ratio” dilemma common in unconventional reservoir evaluation. A critical evaluation is conducted from multiple dimensions, including data quality requirements, algorithmic adaptability, computational efficiency, and model interpretability. The analysis reveals that while data-driven models excel in pattern recognition, their physical consistency and generalization capability require further improvement, particularly when dealing with spatially heterogeneous formations and limited training datasets. To address these challenges, the paper proposes three strategic development directions: (1) Hybrid modeling frameworks integrating physical constraints with data-driven approaches. (2) Transfer learning schemes for small-sample learning scenarios. (3) Multi-modal data fusion architectures incorporating logging, core, and seismic information. Moreover, the study emphasizes the necessity of establishing standardized workflows for feature engineering, model validation, and uncertainty quantification in AI-based logging interpretation systems. Special attention is given to emerging technologies such as graph neural networks for 3D reservoir characterization and physics-informed neural networks for incorporating petrophysical laws into machine learning architectures. This comprehensive review not only synthesizes the current state-of-the-art in intelligent logging interpretation but also provides a strategic roadmap for future research endeavors. The findings offer valuable theoretical references and methodological guidance for optimizing AI-based interpretation techniques in unconventional reservoir evaluation, ultimately contributing to more reliable reservoir characterization and enhanced hydrocarbon recovery in complex geological settings.

Driven by the “dual carbon” strategy, nuclear energy is increasingly recognized as a vital component of the energy mix due to its low-carbon and high-efficiency advantages. As the key raw material for nuclear power, the green extraction of uranium is of great significance. In-situ leaching (ISL) has emerged as a mainstream method for uranium extraction owing to its environmentally friendly and efficient nature. However, low-permeability uranium deposits present significant challenges during ISL, such as poor lixiviant flow and low uranium recovery rates. As a mature permeability enhancement technique, hydraulic fracturing can effectively create fracture networks through the injection of fracturing fluids, thereby improving formation permeability and enhancing lixiviant flow, which ultimately increases uranium recovery efficiency. This paper systematically reviews various ISL processes and their applicability, and thoroughly analyzes the mechanisms and current research status of advanced hydraulic fracturing technologies, including multi-stage fracturing, diversion fracturing, intelligent fracturing, acid fracturing, foam fracturing, and supercritical CO₂ fracturing. Based on the resource characteristics and extraction demands of low-permeability uranium deposits, this study proposes a synergistic application of ISL and hydraulic fracturing technologies. It further explores how hydraulic fracturing can enhance formation permeability and lixiviant efficiency. A development pathway integrating multiple technologies and intelligent optimization is suggested to achieve high-efficiency resource recovery and minimized environmental risks, providing robust support for the green and sustainable development of uranium mining.

The Lower Cambrian Qiongzhusi Formation in the Sichuan Basin exhibits significant shale gas resource potential, with major exploration breakthroughs achieved in the Deyang-Anyue rift sag. However, the complex hydrocarbon accumulation processes under multi-phase tectonic activities have constrained the optimization of shale gas enrichment zones and efficient exploration and development. This study focuses on typical Qiongzhusi Formation shale gas reservoirs in the Zizhong-Weiyuan area of the Deyang-Anyue Rift Sag. Through petrographic observations of fracture veins, fluid inclusion thermometry, laser Raman analysis, and basin modeling, the evolutionary processes and differences in shale gas accumulation in the Zizhong-Weiyuan area were elucidated. Results reveal three distinct stages of fracture vein development in the Cambrian Qiongzhusi shale: Stage I veins formed during the late Caledonian movement (ca.420~405 Ma), containing abundant primary bitumen inclusions indicative of peak oil generation; Stage II veins developed during the Indosinian movement (ca.235~215 Ma), characterized by both primary bitumen and methane inclusions reflecting high-to-over mature shale conditions; Stage III veins formed during the Yanshanian-Himalayan reservoir preservation and adjustment stage, predominantly hosting primary methane inclusions. The Weiyuan and Zizhong areas exhibit vein formation during the Late Cretaceous (ca.75~60 Ma) and Eocene (ca.45~35 Ma), respectively. This may due to that the Weiyuan area is situated in the aulacogen margin, whereas the Zizhong area is located in the inner zone of aulacogen. Therefore, the Weiyuan area began to uplift ~10 Ma before the Zizhong area during the Yanshannian orogeny. Additionally, the Zizhong area benefits from superior basal sealing by the Maidiping Formation, forming an effective gas containment system. Its location on the intra-sag slope belt features less developed faults and fractures compared to the Weiyuan anticlinal region. These combined factors contribute to the overall superior gas-bearing characteristics of the Qiongzhusi Formation in the intra-sag Zizhong area relative to the sag-margin Weiyuan area.

The rapid advancement of generative artificial intelligence (AI), exemplified by models like the DeepSeek series, has substantially lowered the application barrier for large language model (LLM) technology. This progress injects new intelligent capabilities into the field of oil and gas exploration and development, a domain highly dependent on expertise and data-intensive analysis. However, the practical capabilities and implementation pathways of large language models in vertical industry scenarios remain unclear. This study comprehensively and systematically evaluates the multidimensional application capabilities of the DeepSeek model series within this specific domain. A comprehensive six-dimensional quantitative assessment framework was designed to evaluate core competencies, including foundational domain knowledge, complex reasoning, computational proficiency, multimodal processing, performance on open-ended and innovative problems, and professional task execution capabilities. The testing results indicate that LLMs demonstrate exceptional performance in terms of breadth of foundational knowledge coverage and in handling open-ended, innovative questions, revealing strong domain-specific comprehension and application and significant potential for interdisciplinary knowledge integration. However, several critical limitations were identified. The models exhibit hallucination risks when processing specific instances and data, display a lack of sufficient granularity in logical reasoning within complex problem-solving scenarios, and show deficiencies in the accuracy and efficiency of intricate numerical computations. Furthermore, distinct capability boundaries were observed, particularly in multimodal processing - especially the generation and interpretation of professional diagrams and images -, in the operation of specialized software, and responsiveness to real-time engineering demands. To address these identified limitations, this paper proposes an integrated four-dimensional technical pathway to facilitate intelligent transformation. This cohesive strategy comprises: 1) a dynamic knowledge fusion mechanism based on Retrieval-Augmented Generation (RAG) to mitigate knowledge obsolescence and data hallucinations; 2) a knowledge-graph-driven reasoning engine designed to enhance logical reasoning precision for complex problems; 3) a specialized software collaboration architecture that extends the model’s operational boundaries via API gateways integrating domain-specific tools; and 4) an Agent-empowered engineering system for the automated decomposition and execution of complex tasks. The research further delves into key technical challenges, such as the construction of vertical domain knowledge graphs, software ecosystem interoperability, and real-time decision-making by AI agents, proposing targeted directions for technological breakthroughs. In conclusion, the deep integration of LLMs into the oil and gas sector necessitates tight coupling with domain knowledge engineering, specialized software ecosystems, and edge computing technologies. The transition from point solutions to systemic intelligence should be gradual, starting with focused scenario development, overcoming core technical bottlenecks, and ultimately realizing the synergistic application of “Data-Knowledge-Tools.”

In the process of petroleum exploration and development, long-term accumulated documents contain a large amount of engineering knowledge and practical experience, and these materials are of great significance for the scientific development of oilfields and production decision-making. However, such information is mostly preserved in multimodal and unstructured forms such as textual descriptions, data tables, and illustrative figures, lacking a unified structured representation, which leads to low efficiency in retrieval and utilization, and makes the knowledge difficult to be systematically applied. Traditional information retrieval methods have limitations in dealing with complex cross-paragraph and multimodal corpora, and relying only on large-scale language models for question answering is prone to hallucinations and context fragmentation, which cannot meet the requirements of professional fields for accuracy and interpretability. To solve this problem, this paper, based on Microsoft’s open-source graph retrieval-augmented generation framework, constructs a graph retrieval-augmented intelligent question answering system for reservoir geology. Aiming at the linguistic complexity, hierarchical diversity, and structural heterogeneity of oilfield documents, three optimization methods were applied: a logical structure-based segmentation method was used to identify heading hierarchies and numbering rules to achieve reasonable division of semantic units, thereby avoiding semantic fragmentation in entity and relation extraction; a prompt optimization mechanism combined with the terminology system of reservoir geology was applied to improve the accuracy and completeness of entity and relation recognition and extraction, and to reduce errors and omissions; and a multimodal output mechanism was employed to realize the linkage of textual answers with relevant figures and tables through embedding matching, so that the results not only have linguistic coherence but also obtain visual evidence support, enhancing the interpretability and credibility of the answers. In the experimental part, a comprehensive report of about ninety thousand characters from a typical offshore oilfield was used as the data source to construct a knowledge graph and carry out system evaluation. Compared with unoptimized methods and the original framework, the results show that the optimized system has achieved significant improvements in factuality, answer relevance, context precision, and context recall. The improvement in factuality and answer relevance indicates that the system can more accurately generate answers that conform to facts and question intent, while the improvement in context indicators shows that it has greater advantages in cross-paragraph integration and multimodal association. The research results show that this system exhibits higher accuracy and reliability in knowledge extraction, organization, and application, has good engineering adaptability and scalability, not only provides a feasible solution for the structured management and intelligent utilization of complex oilfield knowledge, but also offers references and practical experience for the application of large language models in petroleum engineering and other highly specialized fields.

Rate of penetration (ROP) prediction is of great significance to drilling engineering and can provide important reference basis for drilling optimization, resource allocation, safety guarantee. In recent years, artificial intelligence has sparked a new round of intelligent transformation, promoting the intelligent transformation and upgrading of drilling engineering and giving rise to a large number of new ROP prediction methods. However, there is still a lack of systematic summary and analysis of these new ROP prediction methods at present. This paper, through systematic research and refinement of the main ROP prediction models and methods at home and abroad, summarizes and expounds the development background and theoretical principles of three types of ROP prediction methods: explicit ROP equation, numerical simulation, and artificial intelligence model. It also deeply analyzes the key technical problems and challenges faced by various ROP prediction methods in practical applications at present. It is also pointed out that the ROP prediction method integrating mechanism and data is an important direction to break through the existing bottlenecks and also the mainstream trend of future technological development. Based on this, combined with the development trend of intelligent drilling and the main bottlenecks of the ROP prediction model, five future development directions are proposed: ① Automated and intelligent drilling equipment; ② A dedicated mechanism and data fusion model for ROP prediction; ③ Environmental response mechanism of ROP prediction model based on embodied intelligence, swarm intelligence, reinforcement learning and online learning; ④ A general ROP intelligent prediction model based on large models and transfer learning algorithms; ⑤ Closed-loop optimization of ROP prediction model based on scientific knowledge discovery.

The Tarim Basin, functioning as China's strategic hydrocarbon resource succession zone, encounters globally recognized technical bottlenecks in the exploitation of deep/ultra-deep reservoirs. A comprehensive chronological analysis is conducted on the development trajectory of stimulation technologies for ultra-deep hydrocarbon reservoirs in the Tarim Oilfield, particularly highlighting stimulation technology breakthroughs implemented in the field’s dual primary production zones: For complex carbonate reservoirs in ultra-deep intracratonic basins, an innovative integrated design approach for fracture-cavity-system identification and stimulation was proposed; for ultra-deep fractured classic reservoirs in the Kuqa piedmont zone, a series of high-efficiency stimulation technologies were developed. The research has achieved three major technological breakthroughs: First, the successful development of high-temperature resistant acid systems has significantly enhanced stimulation effectiveness in ultra-deep reservoirs; second, the breakthrough in high-density weighted-fracturing fluid technology provides critical support for ultra-deep well stimulation; third, continuous innovations in supporting process technologies have established a solid foundation for efficient development of ultra-deep oil and gas reservoirs. Integrating exploration trends with development challenges of ultra-deep oil and gas reservoir in the Tarim Basin, the paper addresses the production demands and existing technical deficiencies in reservoir stimulation, including: the fundamental laboratory research on deep/ultra-deep reservoir stimulation, artificial fracture propagation mechanisms, development gaps in novel stimulation fluids, zonal isolation tools, temporary plugging materials and supporting application techniques, limitations in real-time monitoring and interpretation technologies for stimulation operations. Six key technical recommendations are proposed: (1) Establishment of an ultra-high temperature/pressure experimental platform to conduct fundamental research on rock mechanics, fluid flow, and conductivity testing; (2) Investigation fracture propagation mechanisms in high-stress complex reservoirs and develop a multi-physics coupled fracture growth model; (3) Development of high-performance acid systems resistant to 200 °C, with breakthroughs in weighted, low-friction, thermal-stable, and controlled-acid-generation technologies; (4) Development of engineer stratified stimulation tools (including diverting agents and supporting techniques) to optimize treatment in interbedded formations; (5) Enhancement of “multi-cluster limited-entry” fracturing for ultra-deep horizontal wells to improve fracture-controlled reserves; (6) Implemention of real-time fracture diagnostics using fiber-optic monitoring and develop high-temperature downhole monitoring tools. This study not only systematically synthesizes the stimulation technology framework for “triple-ultra” (ultra-deep, ultra-high temperature, ultra-high pressure) reservoirs in the Tarim Oilfield, but also establishes critical technological foundations for China’s 10 000-meter-depth reservoir stimulation endeavors. The research outcomes provide significant theoretical value and engineering guidance for promoting efficient development of deep hydrocarbon resources in China, while the innovative technological approaches may also serve as valuable references for global oilfield development under analogous geological conditions.

With the optimization of China ‘s energy structure, the consumption and import volume of Liquefied Natural Gas (LNG) as a clean energy have continued to increase. However, the light hydrocarbons containing ethane and above not only affect calorific value and metering, but also restrict the comprehensive utilization efficiency of resources. Based on the review of traditional processes such as US patent US0188996A1, US7069743B2 and Chinese patent CN1318543C, a new process for LNG light hydrocarbon recovery based on Direct Heat Exchange (DHX) is proposed. This process introduces a heavy contact tower to achieve secondary fine separation of methane and light hydrocarbons, eliminates the flash tower in the traditional process, and optimizes the heat exchange network and energy consumption distribution through designs such as stepwise utilization of rich liquid thermal energy and energy recuperation within the deethanizer. Process simulations were conducted based on the PR (Peng Robinson) state equation and HYSYS software. Comparative analyses covered energy consumption, product quality, exergy, heat exchange network, etc. The objective function optimization was achieved by combining the response surface experimental design and the NSGA-II (Non-dominated Sorting Genetic Algorithms-II) algorithm. The results show that if only methane is extracted from LNG rich liquid, the energy consumption of the process proposed in this paper is reduced by 35.9%, 46.4% and 44.9% respectively compared with the US patent US0188996A1, the US patent US7069743B2 and the Chinese patent CN1318543C. The high calorific value of the process in this paper can be reduced to 34.16 MJ/m³, meeting the quality requirements of Class I natural gas. The total exergy loss is 7171 kW, and the exergy efficiency of the system is 57.4%. Heat exchange network analysis shows that its minimum heat exchange temperature difference and logarithmic mean temperature difference are smaller, and the heat integration degree is higher. After optimization by the NSGA-II algorithm, with little change in ethane yield, the total energy consumption can be reduced from 16,863 kW to 16,701 kW. With little change in total energy consumption, the ethane yield can increase from 93.52% to 97.85%. The process proposed in this paper has significant advantages in reducing energy consumption, improving product quality and resource recovery rate, and can provide important theoretical support for the engineering design and on-site operation of LNG light hydrocarbon recovery.

Accurate and reliable production forecasting is a critical component for the efficient development of oil and gas fields and supports informed scientific decision-making. Although machine learning methods have achieved significant progress in this domain, existing models are typically trained from scratch using limited historical production data, making it difficult to effectively capture the complex nonlinear dynamics, long-term temporal dependencies, and high-dimensional interactions among variables inherent in production time series. This often leads to insufficient generalization capacity and limited predictive robustness. To address these challenges, this study proposes a novel gas well production forecasting method based on large language models (LLMs). The approach builds upon a pre-trained GPT-2 architecture and incorporates several key adaptations to enable effective time-series prediction. First, the input data—including daily gas production rate, tubing pressure, casing pressure, and production time—are subjected to instance normalization to facilitate knowledge transfer. Second, a trainable embedding layer is designed to map numerical time-series data into the semantic embedding space of the LLM, thereby achieving cross-modal alignment between continuous signals and the discrete representation format required by the model. Third, a parameter-efficient transfer learning strategy combining freezing and fine-tuning is implemented: the core self-attention and feed-forward network layers of the LLM are frozen to preserve general-purpose knowledge acquired during pre-training, while the positional encoding and layer normalization modules are selectively fine-tuned to enhance the model’s ability to characterize temporal patterns specific to production dynamics. The resulting model, termed GPT4TS, is systematically evaluated on real-world production data from a marine carbonate gas reservoir in the Sichuan Basin. Experimental results show that for wells with long production histories, GPT4TS significantly outperforms the conventional LSTM model. Under univariate input, the mean absolute percentage error (MAPE) is reduced by 18.573% on average; under multivariate input, the MAPE reduction reaches 35.610%, demonstrating its superior capability in modeling complex trends and leveraging multi-variable synergies. However, for newly commissioned wells with short production histories, insufficient data hinders effective fine-tuning, leading to lower prediction accuracy compared to LSTM. This study not only validates the potential of large language models in petroleum production forecasting but also highlights their strong dependence on historical data length, providing both theoretical insights and practical guidance for model selection in real-world engineering applications.

Carbonate fractured-vuggy reservoirs in the Middle-Lower Ordovician of the Tarim Basin are strongly controlled by deep strike-slip faults. Their pronounced heterogeneity has become a key challenge to the efficient exploitation of ultra-deep oil and gas resources. In this study, a representative strike-slip fault within a carbonate outcrop on the northwestern margin of the basin was selected as the research target. By integrating multiple analytical approaches, including field structural measurements, petrographic thin-section observations, high-pressure mercury intrusion porosimetry, and rock physics experiments, the study systematically characterizes the multi-scale heterogeneity of fault-controlled carbonate fractured-vuggy reservoirs and identifies their dominant controlling factors. The development patterns of high-quality reservoir zones are also summarized. At the macro scale, fracture distribution along the fault strike is highly uneven, with the overlap segments exhibiting the highest fracture densities. Within these segments, the boundary fault zones are characterized by small breccias with high roundness, indicating strong interconnectivity. Along the dip direction, fault core zones are distinguished by abundant large fractures and vugs with only minor vein filling. These zones have porosities approximately 4~6 times those of the damage zones and compressive strengths only 25%~50% as high, making them prime sites for high-quality reservoir development. Vertically, zones of high porosity and low strength alternate with sealing layers, resulting in a discrete vertical distribution of high-quality reservoirs. At the micro scale, fault cores exhibit diverse pore types, including intracrystalline pores, intercrystalline pores, semi-filled microfractures, and microvugs. In addition, they display high surface porosity, large pore aspect ratios, numerous interconnected pore nodes, and well-developed throat channels, which together contribute to their significantly higher permeability compared with the damage zones. On this basis, the Lorenz curve method is combined with the entropy weight method, which is applied for the first time to evaluate the heterogeneity of fault-controlled carbonate fractured-vuggy reservoirs. The evaluation results indicate that these reservoirs exhibit overall strong heterogeneity, with the micro scale showing a higher degree than the macro scale. This heterogeneity is primarily governed by the coupling of fault structures and diagenetic fluid-driven dissolution-precipitation processes. Integrating these findings, three types of “sweet spot” zones are identified within the fault-controlled reservoirs: boundary fault zones in the overlap segments along the fault strike, fault core zones along the dip, and dolomitic limestone intervals in the vertical sequence. This study fills a gap in understanding the heterogeneity of fault-controlled carbonate fractured-vuggy reservoirs and provides theoretical support for improving the recovery efficiency of ultra-deep oil and gas resources.

Low-frequency distributed acoustic sensing in adjacent wells, a recently emerged fracturing monitoring technology, enables detailed diagnosis of hydraulic fractures. To promote industry understanding of recent advances in low-frequency distributed acoustic sensing technology for hydraulic fracture monitoring and facilitate its large-scale field application, this paper begins with the principles of distributed acoustic sensing. It briefly explains the sensing mechanism and well deployment methods, systematically summarizes research progress in numerical simulation, physical modeling, and field applications during hydraulic fracturing, and concludes by outlining future development directions for low-frequency distributed acoustic sensing technology. Research findings indicate that: ①Low-frequency fiber-optic acoustic sensing technology for hydraulic fracturing delivers high precision and real-time monitoring capabilities. This technology is increasingly being deployed for field fracture monitoring and has garnered significant attention from researchers worldwide. Disposable fiber optic systems offer distinct advantages including simplified deployment, low cost, compact footprint, and excellent value proposition. They represent a promising primary solution for future offset-well fracturing monitoring. Mitigating fiber slippage artifacts’ impact on strain response is therefore paramount for enhancing strain data fidelity in fiber optic sensing applications. ②Forward modeling primarily involves comparative analysis of simulated fiber optic strain fields with actual monitoring data to qualitatively characterize strain patterns. This establishes correlations between distinct fracture propagation types and their corresponding strain signatures, enabling interpretation of hydraulic fracture geometry and growth modes in offset wells. Current strain interpretation models predominantly consider two monitoring configurations: horizontal and vertical offset wells. However, these models fail to characterize fracture deflection induced by stress shadowing, resulting in discrepancies with field monitoring observations. Future work urgently requires developing sophisticated multi-fracture forward models that incorporate stress interference effects and fluid partitioning mechanisms to provide reliable guidance for field data interpretation. ③Inversion modeling primarily utilizes the Displacement Discontinuity Method(DDM) to construct fracture propagation models and solve for fracture dimensions. Current solution approaches include Least Squares, Picard iteration, Levenberg-Marquardt (L-M) method, and the Delayed Rejection Adaptive Metropolis (DRAM) algorithm. However, none can simultaneously invert fracture geometric parameters in all three spatial dimensions. Future inversion research must focus on optimizing solution algorithms, where effectively mitigating the impact of solution non-uniqueness will be the primary research focus for subsequent algorithmic enhancements. ④Physical simulation experiments primarily integrate distributed optical fiber interrogators based on Optical Frequency Domain Reflectometry (OFDR) technology with True Triaxial fracturing apparatuses to monitor fracture propagation. However, current experimental parameter configurations still fall short of fully replicating field conditions. Optimizing fiber deployment methodologies across diverse rock specimens and advancing the interpretation of laboratory-derived fiber optic data represent critical research priorities for future physical simulation studies. The study concludes that offset-well fiber optic monitoring demonstrates significant potential for interpreting hydraulic fracture dimensions. This technology holds considerable promise as a key enabling technology for addressing critical bottlenecks in unconventional resource development.

The extraction of shale oil and gas is confronted with numerous complex geomechanical issues. As a core factor determining extraction efficiency and safety, the mechanical properties of shale urgently require in-depth research and exploration. Against this backdrop, machine learning, with its powerful capabilities in data processing and pattern recognition, has opened up new avenues for research on shale mechanical properties. This paper focuses on the application of machine learning in the study of shale mechanical properties, systematically elaborating on the current status, challenges, and prospects in this field. Firstly, it details the application achievements of current machine learning algorithms in the prediction of shale mechanical parameters and the recognition of failure modes, demonstrating their significant advantages over traditional research methods in processing complex data and mining potential patterns. Subsequently, it summarizes the multiple challenges faced in the application of machine learning to the study of shale mechanical properties. Shale sample data exhibits high-dimensional and small-sample characteristics, which easily lead to overfitting in models. Meanwhile, the internal operating mechanisms of most machine learning models are difficult to interpret, restricting their popularization and application. In addition, the geological conditions of shale are complex and variable, with significant differences in mineral composition and pore structure of shale in different regions. The existing models show obviously insufficient universality when applied across regions and geological conditions. Finally, the future is prospected based on the development trends of cutting-edge technologies. Machine learning has broad prospects in the field of shale mechanical properties research. By integrating multi-source data such as geological, geophysical, and logging data, it can provide more abundant information for models and reduce the negative impact caused by data dimensionality. Optimizing algorithm architectures and combining technologies such as transfer learning and ensemble learning can improve the generalization ability of models. Constructing physics-constrained machine learning models can not only enhance the interpretability of models but also improve their adaptability under complex geological conditions. These strategies are expected to break through existing bottlenecks, promote the in-depth application of machine learning in the study of shale mechanical properties, and provide solid theoretical and technical support for the efficient development of shale oil and gas resources.

The Waste Oil-Based Drilling Fluids (WOBDF: 8#, 10# and 20#) from drilling platform in Nanhai Oilfield have high solid content, viscosity and density, and are difficult to be recovered, transported and recycled. The on-site use of high-speed centrifugation and thermal desorption methods cannot meet the requirement for the solid content in WOBDF, and conventional flocculants also cannot effectively remove harmful solid particles from WOBDF. In this paper, tetraethyl silicate and nano-Fe₃O₄ were used as raw materials, and methyl acrylate was used as graft monomer to prepare magnetic nano-cores. Then Michael addition reaction was carried out with 1,3-propanediamine and triethylenetetramine, respectively. Thus, two flocculants with magnetic cores and hyperbranched structures were successfully prepared (with 1,3-propanediamine as the end capping agent for flocculant-1, with triethylenetetramine as the end capping agent for flocculant-2). The molecular structures of the flocculants were determined by FT-IR and elemental analysis. Then, the coagulation centrifugation method was used to investigate the effects of two types of hyperbranched coagulants on the solid content, density, and viscosity of WOBDF. The results show that when the dosage of flocculant is 2.5wt%, the harmful solid removal rates of flocculant-1 for 8#, 10# and 20# drilling fluids are 82.75%, 62.30% and 70.56% respectively, and the solid content of 8#, 10# and 20# drilling fluids after treatment is was 5.21%, 15.34%, and 14.43%. The removal rates of harmful solids from 8#, 10# and 20# drilling fluids by flocculant-2 are 81.06%, 59.13% and 69.48% respectively, and the solid content of 8#, 10# and 20# drilling fluids after treatment is 5.72%, 16.63% and 14.96% respectively. After treatment, the densities of the three drilling fluids are 0.86~1.16 g·cm^-3; The apparent viscosity and plastic viscosity are 52~90 mPa·s. The flocculation mechanism is related to the adsorption performance of its hyperbranched molecular structure besides charge neutralization and adsorption bridging. The treated WOBDF meets the requirements of drilling platform for offshore waste drilling fluid: the solid index of 8# drilling fluid (which has been thermal desorptioned) is 5%~7%, and the solid index of 10# drilling fluid and 20# drilling fluid (which has not been treated) is 10%~18%, which provides technical methods for the treatment of other WOBDF.

The distribution characteristics of carbon isotopes in sedimentary organic matter are often used as indicators for oil-source correlation, determination of organic matter origins, and paleoenvironmental analysis. However, when organic matter reaches the over-mature stage, cracking processes can lead to isotopic fractionation, resulting in anomalies such as carbon isotope reversal. This phenomenon, particularly in deep and ultra-deep petroleum systems, has long been a key and challenging issue in hydrocarbon geology research. This study examines the carbon isotopic distributionsof various solid and liquid organic materials from source rocks and reservoirs in the Anyue Gas Field, the central Sichuan Uplift, Sichuan Basin, based on existing data and relevant research. The findings indicate that paleo-oil reservoirs experienced thermal alteration, causing the early-formed liquid hydrocarbons in both source and reservoir layers to crack into gaseous hydrocarbons, residual liquid hydrocarbons, and pyrobitumen. The pyrobitumen can be categorized into in-situ pyrobitumen in source rocks and reservoir pyrobitumen. In present-day source and reservoir layers, an overall inversion is observed where kerogen has lower δ¹³C values than liquid hydrocarbons, along with a localized inversion in which saturated and aromatic hydrocarbons show higher δ¹³C values than non-hydrocarbon components and asphaltenes. Additionally, the δ¹³C values of reservoir pyrobitumen are lower than those of both kerogen and liquid hydrocarbons. Comprehensive analysis of relevant data and simulation experiments indicates that carbon isotopic enrichment during hydrocarbon cracking is the primary cause of the observed isotopic inversions in both source and reservoir samples. After high-temperature cracking, the residual liquid hydrocarbons derived from original liquid hydrocarbons in source rocks and paleo-reservoirs exhibit an overall increase in δ¹³C values of approximately 4‰. Due to varying thermal exposure in source rocks and paleo-reservoirs, the extent of δ¹³C increase differs among various group components of the residual liquid hydrocarbons. Although reservoir pyrobitumen largely inherits the isotopic signature of the original crude oil, thermochemical sulfate reduction (TSR) during its formation may also contribute to its anomalously light carbon isotopic values. In summary, the carbon isotopic inversions observed in the source and reservoir layers of the study area are primarily attributed to carbon isotopic fractionation during liquid hydrocarbon cracking—a phenomenon that may be common in deep and ultra-deep petroleum reservoirs that have experienced high temperatures.

Faults serve as crucial pathways and sites for hydrocarbon migration and accumulation, making their identification a key task in the interpretation of seismic data. However, the diversity of fault types, extensive distribution, and complex characteristics pose significant challenges to fault identification. To address this issue, this paper proposes a fault identification method using a 3D TransUnet model. Constructed based on 3D CNN and transformer modules, this model adopts an end-to-end structural design of the 3D Unet architecture. By learning the spatial relationships of three-dimensional faults in synthetic seismic data, it directly predicts fault information in actual seismic data. The method has been successfully applied to seismic work areas in the F3 block of the Dutch North Sea and the Halahatang area of the Tarim Basin, achieving excellent results. The research findings demonstrate that the 3D TransUnet model combines the high local accuracy of CNN and the global attention mechanism of Transformer, enabling inference and prediction of faults in complex regions based on global fault information. Compared with the 3D Unet model and other traditional fault identification methods, the 3D TransUnet model achieved a recall rate of 0.87 and a precision rate of 0.83 on the validation set, significantly outperforming other approaches. In practical applications within three-dimensional seismic work areas, the 3D TransUnet model accurately identifies fault information across different regions. For faults with subtle features, the incorporation of the Transformer module equips the model with a global attention mechanism, allowing it to infer the presence of faults by analyzing distribution trends across the entire work area. By applying the trained fault identification model to different practical seismic work areas (the F3 block and the Halahatang area in this study), the universality of the method is demonstrated, indicating that the trained fault identification model can be effectively utilized across seismic data from various regions. This study finds that the method can effectively identify microfracture information within formations. In oil and gas fields where microfractures serve as reservoirs, since microfractures primarily develop along major faults, well locations are typically deployed near these large faults. However, during the middle and late stages of oil production in such fields, well placement decisions rely more heavily on the development degree of microfractures. Therefore, this fault identification method provides valuable guidance for well placement in oil and gas fields where microfractures act as reservoirs.

Formation drillability assessment is crucial for drilling operations, as it directly influences operational efficiency and cost-effectiveness. Traditional three-dimensional (3D) assessment methods often face challenges due to the unstable integration of multi-source and cross-scale data, resulting in limited spatial generalization and suboptimal prediction performance. To address these limitations, this paper proposes a multi-source data fusion method based on a gated recurrent unit (GRU) network to enhance intelligent formation drillability assessment and improve drilling efficiency in a study area in eastern China. The method consists of two phases: well data training and 3D application. In the first phase, pseudo-depth domain seismic records synthesized from seismic average wavelets and well logging data serve as the foundation. Sensitive attributes related to formation drillability are further extracted as network inputs. These sensitive attributes include a velocity model incorporating geological information and a seismic frequency-fraction attribute that captures multi-scale stratigraphic structure. A corrected drillability index (D_c) is used as a label for model training, ensuring that the network learns to establish an accurate mapping relationship between input attributes and drillability indicators. This training method leverages the temporal and sequential learning capabilities of the GRU network to effectively model complex relationships in the data. In the second phase, the pretrained network was extended to 3D applications, constructing a 3D input dataset by extracting the corresponding attributes. This dataset was then fed into a pretrained GRU model to predict formation drillability in the study area. Analysis of five representative wells in the study area validated the effectiveness of D_c in characterizing rock drillability in the study area. Furthermore, experiments using the Marmousi numerical model demonstrated that the method outperformed traditional intelligent prediction methods, such as those relying solely on raw seismic data or a combination of raw seismic and well logging data. Practical application in the study area further confirmed the method’s ability to effectively capture variations in formation drillability. By providing reliable predictions, the method becomes a powerful tool for optimizing drilling operations and enhancing drilling engineering decision-making.

Seismic petrophysical inversion is an effective method for reservoir physical property evaluation. Direct prediction of reservoir parameters from seismic data has lower uncertainty and higher accuracy than estimation of reservoir parameters from seismic elastic parameters. However, at present, there is little discussion on the establishment of initial model in direct reservoir parameter inversion. A reasonable initial model can not only improve the accuracy of inversion results but also reduce the calculation cost of inversion process. To solve this problem, this paper proposes a seismic reservoir characterization method based on pre-stack and post-stack joint inversion, which combines post-stack impedance inversion and statistical rock physical model to provide a reliable initial model for pre-stack seismic rock physical inversion, and makes full use of the high signal-to-noise ratio of post-stack seismic data and the high resolution of pre-stack seismic data to improve the stability and accuracy of reservoir parameter inversion. Firstly, the critical porosity model is calibrated based on the existing logging data, and the reservoir parametric reflection coefficient formula is constructed based on Zoeppritz reflection coefficient equation, which establishes the direct relationship between seismic data and reservoir physical properties. Then the P-wave impedance is obtained by post-stack inversion, and the initial model of reservoir physical parameter inversion is obtained by using the statistical petrophysical model obtained from logging data. Finally, based on Bayesian framework and Cauchy prior constraints, the inversion of physical property parameters such as porosity, shale content and water saturation from pre-stack seismic data is realized. The synthetic tests show that the superior anti-noise performance of post-stack impedance can provide a reliable initial model for reservoir parameter prediction, and can significantly improve the accuracy of physical property inversion. The field data test verifies the advantages of this method in improving inversion accuracy and enhancing lateral continuity in direct estimation of reservoir physical properties.

Multi-stage alternating injection acid fracturing is commonly employed in the stimulation of tight carbonate reservoirs to enhance differential etching along the fracture surfaces and improve the conductivity of acid-etched fractures. The numerical simulation technique serves as an effective tool for optimizing the operational parameters of such treatments, significantly contributing to the enhancement of post-fracturing productivity and long-term production stability. However, existing numerical simulation approaches for multi-stage alternating injection acid fracturing often neglect acid-rock reactions or adopt simplified equivalent viscosity methods, which result in considerable deviations between simulation results and actual field observations. To address this issue, this study developed a mathematical model for multi-stage alternating injection acid fracturing based on the Volume of Fluid (VOF) method. This model incorporated both the interface tracking between reactive and non-reactive fluids, and also the acid-rock reaction. The governing equations of the mathematical model were discretized using the finite difference method, and the resulting numerical model was solved through computer programming. The accuracy of the model in capturing viscous fingering behavior and acid-etching profiles was verified by comparing the simulation results with the experimental data and analytical solutions. Based on this validated model, simulations were conducted to investigate the flow and reaction behavior of acid under different numbers of alternating injection stages, as well as the evolution of viscous fingering patterns and changes in etched fracture width. To comprehensively evaluate the effectiveness of differential etching, a viscous fingering index was introduced, which was accounted for acid penetration distance, the number of fingering branches, and the area covered by the viscous fingering. Simulation results demonstrate that under typical fracture widths and alternating injection conditions, low-viscosity acid gradually forms preferential flow channels in the fracture due to the viscosity contrast, which is the manifestation of the fingering phenomenon. As the number of alternating stages increases, the competitive development and mergence between the adjacent fingers happens. The effective acid penetration distance continues to increase with the number of alternating injection stages. However, when it is beyond a certain critical stage number, the growth rate of acid penetration distance slows, and further increasing of the alternating injection stages primarily only enhances the acid-etched width within the existing viscous fingering regions. Therefore, for a given fracture geometry and acid system, there is an optimal range of alternating stages, which simultaneously maximizes differential etching and the acid penetration distance. This model provides an effective simulation tool for the optimization of multi-stage alternating injection acid fracturing and offers theoretical guidance for the design of field treatment.

To address the challenges of polymer-induced plugging and reservoir damage caused by drilling and completion fluids, a novel nanocarrier-based immobilized enzyme plug-removal agent was developed in this study. α-Amylase was covalently immobilized on aminated nano-silica particles, resulting in a structurally stable biocatalyst with uniform particle size (~183 nm) and enhanced catalytic efficiency. Key preparation parameters, including enzyme-to-carrier ratio and crosslinker concentration, were systematically optimized. Comparative characterizations were conducted to analyze the molecular structure and binding mechanisms of the native and immobilized enzyme systems. Mechanistic studies have revealed that amylase undergoes a dehydration condensation reaction with the primary amine groups on the surface of aminated nano-silica, resulting in the formation of a Schiff base structure and achieving the covalent immobilization of amylase on the surface of the nanoparticles. Laboratory simulation experiments demonstrated that the developed plug-removal agent exhibited rapid and efficient removal of polymer blockage within API filter cakes and artificial sandpack, increasing the filtrate backflow volume to approximately 120 mL, with an average removal efficiency exceeding 90%. Under high-temperature (95 °C) and 3.5 MPa conditions, the permeability recovery reached 75.62%, significantly outperforming the pure enzyme formulation. The plug-removal mechanism of the novel biological enzyme-based plug-removal agent mainly involves the enzymatic hydrolysis of α-1,4 or α-1,6 glycosidic bonds within polymers. This process cleaves long-chain molecules into short-chain oligosaccharides or monosaccharides, leading to a significant reduction in viscosity and ultimately achieving efficient plug removal in the near-wellbore zone and flowback of wellbore fluids. Additionally, the novel biological enzyme-based plug-removal agent can be easily recovered through simple centrifugation from polymer degradation products, thereby demonstrating potential for multiple recycling and reuse. This work provides a novel strategy and technical solution for enhancing the adaptability and stability of enzyme-based agents for oilfield applications in complex reservoir environments.

High pressure water jet technology has been widely used in oil and gas well descaling and unblocking because of its advantages of high efficiency, cleanliness and low cost. With the gradual development of oil and gas exploration and development to deep and ultra-deep depths, the application depth of water jet technology has increased significantly, and the unblocking effect of jet tools becomes worse under the condition of high confining pressure in deep wells. Although researchers have conducted extensive studies on jet flow fields and jet performance under confining pressure, the research methods remain relatively limited. Furthermore, as experimental confining pressures are typically confined to below 30 MPa, the mechanism by which high confining pressure affects jet application efficacy remains unclear. Aiming at the key problem of poor application effect of jet descaling and unblocking under high confining pressure, this paper uses the self-developed confining pressure jet comprehensive test system to carry out rock-breaking and axial dynamic pressure experiments under constant flow rate condition of confining pressure 0~100 MPa, analyzes the influence law of high confining pressure on jet rock-breaking effect and impact force. The influence mechanism of high confining pressure on the unblocking effect of water jet is revealed by combining the basic theory of water jet. Application recommendations are proposed for the jet unblocking under high confining pressure conditions in deep wells. Results: Keep the flow rate and jet distance constant, the rock-breaking depth, rock-breaking volume and jet impact force all decrease with the increase of confining pressure, and the decreasing trend decelerates with increasing confining pressure. From normal pressure to 100 MPa, the rock-breaking depth decreases by about 72%, the rock-breaking volume decreases by about 90%, and the jet impact force decreases by about 50%~60%. The decrease of jet axis dynamic pressure is due to the joint action of nozzle cavitation and the “damping effect” caused by high confining pressure environment. The main reason for the poor application effect of high confining pressure water jet is the decrease of jet impact force under confining pressure. To achieve high-efficiency jet unblocking under high confining pressure in deep wells, it is recommended to enhance jet performance under such downhole conditions and to implement a combined unblocking approach that integrates jetting with mechanical or chemical methods. This research is expected to provide a fundamental theoretical support for enhancing the application effect of water jet technology in descaling and unblocking under high confining pressure conditions in deep wells.

Deep and ultra-deep oil and gas resources, characterized by vast potential but low proven rates, become a key target of exploration and development in China presently. However, evaluating their resource potential still faces a series of scientific and technological challenges, such as high thermal evolution degree of source rocks, strong diagenetic modification of reservoirs, multi-stage adjustment, transformation and effective preservation of oil and gas reservoirs. Recently, new breakthroughs have been made in ultra-deep exploration in the Yuanba Area, with the discovery of natural gas reservoirs in the fourth section of the Dengying Formation at a depth of nearly 9000 meters, revealing promising exploration prospects for ultra-deep layers in the northern Sichuan Basin. Based primarily on the latest drilling data of YS1 well, combined with peripheral drilling, outcrop and analysis testing data, this study systematically investigates the key control elements of source rocks, reservoirs and oil and gas accumulation processes in the Dengying gas reservoir in the study area, aiming to provide reference for the exploration and evaluation of ultra-deep oil and gas reservoirs. The results show that: (1) The YS1 gas reservoirs of the fourth member of the Dengying Formation were derived from the Cambrian Qiongzhusi Formation source rocks. These source rocks entered a low maturity stage during the Silurian, then reached a medium high maturity stage for the main oil generation and early cracking during the Late Permian-Triassic, and reached a high over maturity stage for main cracking gas generation during the Middle Jurassic-Early Cretaceous. (2) The YS1 gas reservoirs are consist of the microbial dolomites deposited on the platform margin, which have undergone long-term compaction, pressure solution, and deep burial cementation, resulting in currently low porosity and low permeability characteristics. (3) In northern Sichuan Basin, the platform marginal mound-shoal reservoirs are adjacent to the high-quality deep-water facies source rocks of the Qiongzhusi Formation, and has favorable source and reservoir configuration conditions of “source generation in slope facies with reservoir accumulation in margin facies” and “upper source feeding lower reservoir", which provides the material basis for paleo-oil reservoir formation. (4) The gas reservoir in Member 4 of the Dengying Formation underwent multistage modifications. During the paleo-oil stage, located on the central Sichuan paleo-uplift slope, it formed large-scale lithologic paleo-oil reservoirs sealed by tight inter-shoal layers. During oil-gas conversion and gas reservoir stages, influenced by the Micang Mountain uplift, subtle structural highs developed on the Micang uplift slope, forming structure-lithology composite paleo-gas reservoirs. In the late stage, the Himalayan compression caused basin-margin uplift, adjusting the paleo-gas reservoir to form current reservoirs, with YS1 well in the favorable overlap zone. Exploration should target large paleo-oil reservoirs, identify key-period paleo-structures, and focus on areas combining effective preservation with paleo-present structural overlap as preferential enrichment zones.

Low salinity water flooding is a new technology for enhancing oil recovery by adjusting the ion composition or con-centration of injected water.However,the applicable reservoir conditions and enhanced oil recovery mechanism of low salinity water flooding have not yet reached a consensus.In this paper,a series of laboratory experiments of wettability control-based low salinity flooding are carried out with plunger rock samples from marine carbonate reservoirs in the Middle East as the research object.Based on the theory of Derjaguin-Landau-Verwey-Overbeek theory(DLVO),an interfacial reaction model of a typical crude oil/brine/rock system is established,and the contact angle and total separation pressure are calculated simultaneously with the augmented Young-Laplace formula.The reliability of the model is verified by the literature experimental data,and the effects of ion concentration and ion type on the separation pressure curve and contact angle are clarified.The results show that in low salinity environments,the pore surface of carbonate rock is more water-wet under the action of fluid flushing,the oil displacement efficiency is higher,and the low salinity water improves the crude oil recovery by 3.2%;under the assumption of constant charge,the mathematical model established based on the DLVO theory for the crude oil/brine/rock system can accurately predict the change of contact angle;compared with the ion concentration,ion type has a greater impact on separation pressure and contact angle.Among divalent ions,Mg2+ions exhibit a more pronounced influence on wettability control compared to Ca2+ions.When the water film thickness is minimal,van der Waals force is the main force affecting the separation pressure.As the thickness of water film increases,the electric double layer force gradually becomes the main force.This study contributes to a deeper understanding of the wettability control mechanism of low salinity water flooding for enhanced oil recovery.

The practice of oil and gas exploration and development shows that the transformation from “outside source” to “inside source” is an inevitable choice for the sustainable development of petroleum industry. Recently, the breakthrough of unconventional oil and gas in semi-deep lacustrine facies shale in the Qingshankou Formation (K₂qn) in the northern Songliao Basin has proved that it has broad resource prospects. The sedimentary paleoenvironment controls the accumulation of organic matter and the distribution of lithofacies, which is the basis for the prediction of shale oil desserts. In this paper, by means of experimental methods of biomarkers and element geochemistry, parameters such as paleoproductivity, paleoreoxidation, and paleosalinity of the lake basin in the northern Songliao Basin were recovered to clarify the paleoclimate evolution characteristics during the formation of Qingshankou Formation, and to compare the paleoenvironment with that of other major shale oil and gas resource enrichment basins in China. The biomarker compounds in the Qingshankou Formation samples predominantly exhibit a unimodal distribution of n-alkanes, with major peaks at nC₁₈, nC₁₉, nC₂₀, and nC₂₁. The Pr/Ph ratio ranges from 0.44 to 1.31, with an average value of 0.87, indicating a general dominance of phytane. Among the major elements, CaO, Na₂O, and P₂O₅ are relatively enriched, while among trace elements, Sr shows the highest enrichment, with Ba, V, Cr, Ni, Cu, Rb, and Y being relatively depleted. The research results indicate that the Songliao Basin developed under warm and humid paleoclimate conditions. Among the sub-basins, the Gulong Sag was relatively more humid compared to the Sanzhao Sag. The lower section of the Qingshankou Formation exhibited warmer and more humid characteristics compared to the middle and upper sections. Influenced by the transgression of the Paleo-Pacific Ocean from the east, the salinity of the lake basin water was relatively high, with a higher degree of salinization observed in the eastern Sanzhao Sag. During the depositional period of the Qingshankou Formation in the northern Songliao Basin, overall paleoproductivity levels were high. The basin predominantly exhibited a dysoxic to anoxic reducing environment, which provided favorable conditions for the accumulation and preservation of organic matter.

With the increasing demand for natural gas, the stable operation and safety of the natural gas pipeline network system need to pay more and more attention. Reliability management, as a further refinement of integrity management, provides a more comprehensive evaluation and management system for the functional status of both local and overall system components. It can promote the safe and stable operation of large-scale natural gas pipeline networks. Reliability allocation is an indispensable component of reliability management. By allocating system reliability indicators to each unit and clarifying the reliability requirements for each unit, it can provide reliability assurance and guidance for the operation management and maintenance of natural gas pipeline network system. But no effective reliability allocation method has been proposed for the natural gas pipeline network system with complex topology. According to the structure and process characteristics of the natural gas pipeline network system, the analytic hierarchy process (AHP) is improved to propose the hierarchical reliability allocation method for the complex structure natural gas pipeline network system in this work. Based on the functional level of “pipeline network system-pipeline system-unit”, the hierarchical model of the natural gas pipeline network system is established. The importance of supply security and the severity of consequence have been incorporated as influence factors for system gas supply reliability. Through quantitatively evaluating the factors affecting the reliability of system gas supply, the reliability allocation index of the natural gas pipeline network system is allocated to each unit step by step, to determine the reliability allocation plan for complex structure natural gas pipeline network systems. Finally, the proposed method is applied to an actual natural gas pipeline network system. Results indicate that the value of reliability allocation for compressor station units is lower than that for adjacent pipeline segment units. The value of reliability allocation for pipeline segment units is related to the distance from the demand point. Pipeline A exhibits the highest reliability requirements, with relatively balanced unit reliability allocation values and fewer critical gas supply impact units. Pipeline B has the lowest overall reliability requirements. Pipelines C and D are similar, featuring significant variations in unit reliability allocation values and a large number of critical gas supply impact units. This study proposes a hierarchical reliability allocation method suitable for complex structured natural gas pipeline systems, which improves the theoretical system of pipeline system reliability and provides the scientific basis for enhancing the reliability of pipeline systems.

The increasing complexity of energy systems in oilfields necessitates advanced approaches to monitor, analyze, and optimize energy usage. Traditional methods are often inadequate for processing the vast amounts of data generated from diverse sources, leading to inefficiencies in identifying and resolving energy consumption anomalies and making it difficult to achieve optimal energy utilization. To overcome these limitations and achieve the intelligent decision-making for energy management and control in oilfield gathering and water injection systems, an intelligent assisted decision-making method for abnormal energy consumption was proposed based on knowledge graph, addressing the challenges posed by massive multi-source heterogeneous data. Specifically, the abnormal energy consumption records and operation manuals were utilized as the primary data source, and the comprehensive knowledge framework for energy management and control was established. This framework serves as the foundation for organizing and integrating multi-source data, ensuring systematic and efficient data utilization. Additionally, the BiGRU-CRF (Bidirectional Gated Recurrent Unit-Conditional Random Field) model was applied to extract entities from the textual data, identifying key concepts such as equipment, parameters, and anomalies. And the BiGRU-ATT (Bidirectional Gated Recurrent Unit-Attention) model was adopted to extract relationships between entities, capturing the complex interdependencies within the oilfield gathering and injection systems. The extracted energy consumption knowledge is stored and visualized using the Neo4j graph database, providing a robust platform for data querying and analysis. Its structured representation lays the foundation for the efficient utilization of data in subsequent stages. Finally, based on the constructed knowledge graph, an energy management and control visualization platform was developed, providing a user-friendly interface that enables operators to explore energy consumption data and knowledge in an intuitive manner, significantly enhancing the usability of the operational system. The platform provides actionable recommendations at both the data and knowledge levels, supporting energy consumption control effectively. The field application results in oilfields demonstrate that the proposed intelligent decision-making method, based on knowledge graphs, effectively integrates multi-source heterogeneous data for abnormal energy consumption detection in oilfield gathering and injection systems. Timely, comprehensive, and intelligent decision-making recommendations are provided for energy consumption anomaly events in the gathering and injection processes, guiding operators in achieving rapid and effective energy consumption control. The time required for decision-making is significantly reduced through this method. This study offers a novel and impactful approach for the construction of energy management and control systems in oilfields, which provides valuable guidance for the management of abnormal energy consumption in other oilfields.

The tight conglomerate reservoir in the Junggar Basin exhibit complex multi-scale pore-throat structures, which obscure the dominant controls on fluid occurrence, hinder reservoir classification, and complicate the quantitative evaluation of graded reserves. To clarify the relationship between microscopic pore-throat characteristics and fluid occurrence states and achieve effective evaluation of highly heterogeneous tight conglomerate reservoirs, this study takes the Upper Urho Formation (P₃w) in the CO₂ flooding pilot area of the Mahu 1 well block as an example, conducting comprehensive workflow of “pore-throat structure-fluid occurrence-reservoir classification-graded reserves”. Integrating methods including cast thin sections, high-pressure mercury intrusion, and nuclear magnetic resonance (NMR) logging were integrated to characterize the lithomechanical properties and pore-throat structure parameters of different sub-layers within the main reservoir interval. Grey relational analysis further identified movable-to-total porosity (weight 0.913) and clay mineral content (weight 0.805) as the key factors controlling effective oil saturation. Based on NMR T₂ spectral morphology, three-component index, IB value, and permeability, a classification standard dividing the reservoir into three types (Ⅰ~Ⅲ) was established, A methodology for calculating tiered pore-volume reserves was proposed, achieving a systematic evaluation from pore-throat structure to classified reserves. Results indicate that in the main producing interval P₃w₂², the lower sublayer exhibits significantly better reservoir quality than the upper. The lower section has a micron-scale pore proportion of 25.48%, an average permeability of 5.59 mD, and an effective oil saturation 15%~20% higher than the upper section. Nano-scale pores (<100 nm) represent the dominant storage space in the pilot area, containing reserves of 784.3 thousand tons (61.7% of the total). From the perspective of reservoir classification, type Ⅰ reservoirs, with the highest proportion of micron-scale pores (25.48%) and excellent oil-bearing capacity. Type II reservoirs show reduced micron-scale pores and moderate oil-bearing capacity; Type III reservoirs suffer from poor oil retention due to strong confinement by nanopores. The results of this study reveal the controlling factors of fluid distribution, establish a classification standard and tiered reserve characterization system for tight conglomerate reservoirs constrained by pore-throat structures, and provide theoretical support and technical support for the optimal selection of CO₂ flooding target zones and the identification of “sweet spots” in the Mahu conglomerate oilfield.

In the process of fracturing in tight reservoirs,the imbibition and displacement of crude oil in reservoir pores by fracturing fluids has gradually become a key research field of enhanced oil recovery technology.However,the production characteristics and mechanism of pore crude oil at different scales in the process of imbibition are still unclear,which seriously restricts the optimal design of fracturing fluid system and the reasonable selection of mining technology.Taking the Chang 7 member tight reservoir in the Ordos Basin as the research object,the amphoteric surfactant(EAB-40)was used as the main agent of the clean fracturing fluid system,combined with T1-T2 two-dimensional nuclear magnetic resonance and wettability test,the influence of surfactant concentration on reservoir interface properties and fracturing fluid imbibition and displacement efficiency was systematically studied,and its microscopic mechanism was revealed.The experimental results show that EAB-40 signifi-cantly enhances the capillary driving force and crude oil desorption efficiency by synergistically reducing the oil-water interfacial tension(up to the order of 10-2 mN/m)and inducing the wettability reversal(the contact angle is reduced from 147° to 57.34°).The comprehensive oil displacement effect of the fracturing fluid system is optimal when the concentration of surfactant is 0.1 wt%.During the imbibibibition process,the wettability inversion is caused by the concentration of water-wet minerals in the small pores,and the diffusion of surfactants causes the wetting inversion,which drives the crude oil to migrate efficiently from the small pores T2<1 ms to the middle(T2 is between 1 and 100 ms)and large pores T2>100 ms.Polymer molecules improve the rheological properties of the fracturing fluid system and promote the deep utilization of residual oil in bound oil and blind end pores.Realize the triple synergistic imbibibibibition mechanism of"IFT reduction-wetting inversion-viscoelastic flow control".

The conical polycrystalline diamond compact (PDC) cutter demonstrates significant advantages in deep hard rock formations due to its excellent impact resistance, wear resistance, and efficient rock-breaking capabilities. To further enhance its rock-breaking efficiency in deep hard rock conditions, systematic optimization of the rock-breaking parameters of the conical PDC cutter is required. This paper focuses on the penetration-cutting combined rock-breaking process of the cutter during actual drilling, establishes a numerical calculation model for the conical PDC cutter breaking granite, and designs an orthogonal experimental scheme to conduct numerical simulations. Based on the results of the orthogonal experiments, the stepwise regression method is employed to fit and analyze key rock-breaking parameters such as cutting force, penetration ability, and mechanical specific energy. Subsequently, a multi-objective optimization method based on the non-dominated sorting genetic algorithm is applied to optimize and select the geometric parameters (cutter diameter, cone apex angle, cone apex radius) and operational parameters (inclination angle, weight on bit) of the conical PDC cutter. The research results indicate that, under the conditions of this study, the optimal geometric parameter combination for the conical PDC cutter is a bit diameter of 16 mm, a cone apex angle of 60°, and a cone apex radius of 1 mm, while the optimal operational parameter configuration is a bit inclination angle of 36° and a weight on bit of 925.65 N. The optimized mechanical specific energy is reduced by 10.99% compared to the initial combination. This study can provide theoretical and practical guidance for the optimization design of conical PDC bits.

The successful development of shale oil and gas formations mainly depends on engineering measures such as extended-reach horizontal drilling and high-volume fracturing, which enable high-quality geological sweet spots in long horizontal shale oil and gas formations to extract more industrial production capacity. Among them, drilling is the most effective and direct technical means to communicate engineering and geology. The drilling geological environment is a significant factor influencing the drilling engineering process, including both geological factors of the formation and the mutual influence factors between drilling environment and geological environment. In order to improve the high-quality sweet spot sweet-spot encounter rate, facilitate fracturing, and reduce engineering risks, this paper proposes wellbore trajectory optimization control technology for shale oil and gas formation production increase, safety, and efficiency. By analyzing the geological environmental factors of shale oil and gas formations, the models of geological sweet spot evaluation, geological risk identification, and geological engineering integration application have been formed. Based on these models, three drilling wellbore trajectory optimization control technologies have been proposed: ① Control the horizontal drilling position according to the changes of geological sweet spots in the formation space, and form trajectory control technologies that optimize drilling encountering sweet spot layers and enhance initial production (IP) rates; ② optimize the drilling direction based on the fracturing properties of engineering sweet spots, and trajectory direction optimization technology to improve the fracturing response characteristics of shale oil and gas formations; ③ To mitigate drilling risks, trajectory control techniques are developed to ensure the safety of drilling in shale oil and gas formations and reduce drilling engineering risks. Wellbore trajectory optimization control technology is one of the key technologies for achieving geo-engineering integration, which improves the efficiency of fast drilling and completion of long horizontal wells and high-volume fracturing, as well as increase the sweet-spot encounter rate rate and development efficiency of geological sweet spots.

Unconventional shale oil is characterized by nanoscale pores, heterogeneous pore structures, diverse mineral compositions, non-uniform wettability, and multiple fluid types, resulting in complex multiphase flow behaviors in shale porous media that require further investigation. In this study, a nanoscale multicomponent and multiphase lattice Boltzmann method is employed to simulate oil-water two-phase flow in heterogeneous porous media with heterogeneous wettability and structure. The effects of transverse/longitudinal structural heterogeneity, capillary number and nanoscale effects on the oil-water flow and relative permeability are investigated. The results indicate that due to the higher capillary resistance in the transverse porous media, the relative permeability of the water phase in the transversely heterogeneous porous media is lower than that in the longitudinally heterogeneous porous media. As the capillary number decreases, capillary resistance becomes dominant, and the viscous driving force is insufficient to overcome the capillary forces, making it difficult for oil and water to flow. Consequently, the relative permeabilities of both oil and water phases decrease, and more fluid becomes trapped due to capillary resistance. The presence of an oil film on the solid wall induces liquid-liquid slip, which significantly enhances water flow. This enhancement effect outweighs the weakening effect caused by viscosity heterogeneity. As a result, when nanoscale effects are considered, the relative permeability of the water phase increases.

This study addresses the challenges of poor real-time performance and low accuracy in drilling condition identification by introducing an innovative intelligent recognition method. The proposed approach integrates a one-dimensional convolutional neural network (1dCNN) for local feature extraction, a bidirectional gated recurrent unit (BiGRU) to capture sequential dependencies, and a multi-head attention mechanism to emphasize critical information. This fusion enables efficient discrimination among 13 drilling conditions, including rotary drilling, slide drilling, whipstocking, and reverse whipstocking. In the model design phase, comprehensive ablation studies were conducted to evaluate the contributions of each module—1dCNN, BiGRU, self-attention, and multi-head attention—as well as their serial and parallel configurations. The performance was further optimized using the Optuna framework for automatic hyperparameter tuning. Experimental results demonstrated that the model achieved an accuracy of 96.22% on time-domain data from a single well. Additionally, in both intra- and inter-block transfer tests, the overall accuracy ranged from 94% to 97%, with each drilling condition exceeding an 80% recognition rate. Real-time testing on field data also showed a high degree of consistency with actual operational conditions. Overall, the proposed method provides a robust technical framework for real-time monitoring and optimization of drilling operations.

The Linxing gas field was selected as the research object, where weak bedding planes represent typical features and significantly influence hydraulic fracture propagation. This study provides valuable insights on hydraulic fracture propagation in bedded shale formations and offers guidance for optimizing fracturing techniques. The characteristics of shale featuring bedding planes were examined by utilizing rock mechanics experiments and direct shear tests. Considering the cementation strength and friction properties of bedding planes, a computational subroutine was developed to characterize the contact behavior for bedding planes. A 3D Finite Element Method-Cohesive Zone Model (FEM-CZM) has been established for multi-field coupling analysis of stress-damage-fluid flow, specifically incorporating bedding planes. This model incorporates a contact constitutive relationship that accounts for both friction and cementation strength of the bedding. A comprehensive and systematic quantitative analysis is conducted to investigate the influence of various factors on bedding shear slip and the propagation of hydraulic fractures. These factors include the initial opening of bedding fractures, friction coefficient, cementation strength, number of bedding planes surrounding the wellbore, and fracturing operation parameters. The results indicate that the presence of weakly bonded bedding planes leads to complex fracture propagation patterns involving both tensile and shear fractures. Bedding plane apertures serve as preferential flow pathways for fracturing fluid, significantly inhibiting fracture propagation. When the bedding aperture increases to 300 μm, the fractures are unable to cross bedding planes, which limits the fracture scale. Compared to the bonding strength, the bedding friction coefficient plays a more dominant role in determining whether fractures penetrate. Higher friction coefficients facilitate the penetration, regardless of whether the bedding planes are bonded or not. The penetration probability increases exponentially as the friction coefficient rises. In contrast, with lower friction coefficients and weakly bonded bedding planes, fractures are intercepted, while higher cementation strength allows for effective penetration. Furthermore, with a rise in the number of bedding planes, the shear fractures along these beddings expands considerably, which results in a more intricate fracture pattern. The shear failure of multiple bedding planes restricts the development of tensile-dominated fractures, which reduces the efficiency of reservoir stimulation. Optimizing fracturing fluid injection, increasing high-viscosity fracturing fluid volumes, and raising injection rates can enhance vertical fracture propagation and improve the stimulated reservoir area. Further validation of the influence of bedding planes on fracture propagation is provided by analyzing distributed temperature sensing (DTS) profiles, as well as the post-fracturing performance in Linxing.

Lost circulation is one of the most frequent and hazardous complications in drilling operations, and its accurate prediction plays a vital role in ensuring the safe and efficient exploration and development of hydrocarbon resources. However, conventional prediction methods largely rely on historical drilling and logging data combined with empirical analyses, while often neglecting critical geological risk elements such as structural features. These methods suffer from delayed predictions and limited spatial applicability, making them insufficient for pre-drilling risk assessment in complex geological environments. To address these challenges, this study proposes a seismic-guided prediction method for geological lost circulation risks based on a “four-element hazard” (FEH) model. Utilizing multi-scale data from representative offshore drilling blocks, the method integrates well-logging data, drilling parameters, and 3D seismic information. Through geological statistics and analysis of typical well sections, four major geological factors are identified as the primary triggers for lost circulation: fault zones, volcanic conduits, lithologic discontinuities, and abnormally overpressured formations. These factors form the foundation of the FEH model framework. Guided primarily by seismic data and constrained by well and drilling information, the method extracts multi-source sensitive seismic attributes to establish identification workflows for each of the four risk types. Specifically, a multi-attribute Bayesian fusion model is used to estimate fault-related risk probabilities; joint amplitude-variance analysis delineates the boundaries of volcanic conduits; lithologic interface indicators are optimized based on response features; and abnormal overpressure zones are predicted by integrating seismic velocity and pore pressure inversion. Field applications in the Bohai A Block and South China Sea B Block demonstrate strong consistency between predicted risk zones and actual lost circulation events. In particular, the model successfully forecasted 80% of the loss intervals in the Bohai H1 well, including several composite-origin zones, with a maximum instantaneous loss rate of 90 m³/h. These results validate the model’s capability for forward-looking and effective risk prediction in structurally complex formations. In summary, this research develops a three-dimensional, seismic-guided, pre-drilling risk identification workflow targeting structurally complex zones. The method provides essential technical support for well placement optimization, trajectory design, and proactive drilling risk management.

Predicting the rate of penetration (ROP) plays a significant role in optimizing drilling parameters, improving drilling efficiency, and reducing costs. Although intelligent algorithms have achieved promising results in ROP prediction, existing methods generally ignore the impact of drill bit wear on ROP. To address this technical bottleneck, this study proposes a ROP prediction model incorporating drill bit wear, which establishes a dual neural network architecture for predicting drill bit wear coefficients and ideal ROP. This architecture enables modeling of the complex nonlinear relationships among drilling parameters, wear states, and ROP. Aiming at the scarcity of real-time drill bit wear labels, a pretraining mechanism is proposed to obtain wear coefficients through a two-step training process. Comparative experiments based on measured data from Blocks A and B in Bohai oilfield show that: (1) The prediction accuracy of the porposed model for ROP is improved by 100% and 27% in Blocks A and B, respectively, compared with traditional machine learning methods, and by 14% and 7.6% compared with BP neural network models, significantly surpassing the performance of traditional data-driven models. (2) The proposed model demonstrates improvements in prediction performance in the shallow strata with complex lithology (Block A) than in the deep strata with stable lithology (Block B). (3) The proposed pretraining mechanism enables the model to predict drill bit wear coefficients without real-time wear labels and simultaneously improves the prediction accuracy of mechanical ROP by 24% and 10% in the two blocks, respectively. The coupled model and pretraining mechanism developed in this study not only provide a more accurate method for mechanical ROP prediction but also offer an effective means for real-time monitoring of drill bit wear states, providing practical guidance for drilling operations.

Unconventional oil and gas resources serve as vital replacement energy in China's hydrocarbon portfolio,and their efficient development is of great significance for safeguarding national energy security.The implementation of staged multi-clus-ter hydraulic fracturing in horizontal wells,along with the optimization of intra-stage cluster design parameters,is critical to maximizing the production potential of unconventional reservoirs.Clarifying fracture propagation mechanisms and quantifying the relationship between fracture geometry and well productivity is key to optimize intra-stage multi-cluster fracturing strategies.In this study,a phase-field method is employed to simulate the competitive propagation morphology of multiple fractures within a fracturing stage.A fracture morphology identification technique is integrated to construct a two-dimensional equivalent fracture model,which can characterize the stimulated flow pathways.Equivalent physical parameters after stimulation are extracted and transferred-together with geometric descriptors-as input for a discrete fracture flow model.This enables automatic coupling and data transfer between the geometric and flow models,thereby facilitating quantitative evaluation of production performance under different fracturing scenarios and ultimately achieving fully coupled fracture propagation-fluid flow simulation.The accuracy and feasibility of the dual-model coupling method are verified through comparison with laboratory-scale physical simulation experiments and field fracturing data.On this basis,the effects of intra-stage cluster number and cluster spacing on fracture morphology and production response are further investigated.The results show that,as the cluster spacing increases from 15 m to 25 m,the fracture deflection point shifts farther from the wellbore,and the tip deflection angle decreases from 30° to 24°.Meanwhile,the pressure gradient around the fracture tip is reduced,weakening the fluid driving force and significantly diminishing inter-fracture fluid interference.This change leads to a decline in peak daily oil production and stabilized production rate,with daily and cumulative oil output decreasing by 35.88%and 35.89%,respectively.In contrast,when the number of clusters per stage increases from 3 to 5,the deflection angle at the tip of the outer fractures increases from 30° to 34°,while the coverage of the induced stress field expands from 36.74%to 42.46%.This results in a higher pressure gradient surrounding the fractures,enhancing the fluid driving force and significantly improving oil mobilization.Consequently,peak daily and cumulative oil production increased by 40.49%and 45.467%,respectively.Therefore,optimizing the intra-stage cluster spacing and cluster number can effectively balance the degree of fracture interference and enhance single-well productivity,thereby improving the overall effectiveness of staged multi-cluster hydraulic fracturing in horizontal wells.

The submarine fan is an important reservoir for oil and gas in deep water areas. The differences in reservoir quality have a significant impact on the differential accumulation and exploitation of oil and gas. Previous studies have conducted extensive research on the differences in reservoir quality of submarine fans. However, the characteristics and distribution patterns of reservoir quality differences within submarine fans under a steep continental slope background are still unclear. This paper takes the Oligocene submarine fan reservoir in the X gas field of the Rovuma Basin in East Africa as the research object. By integrating core, well logging and seismic data, an in-depth study has been carried out on the control of reservoir quality differences and distribution patterns of submarine fan sedimentary microfacies and lithofacies under the steep continental slope background. The results show that the changes in reservoir quality within the submarine fan are mainly controlled by rock texture, lithofacies (association) and sedimentary microfacies under the circumstance of weak diagenesis. Grain sorting and clay content mainly control the porosity and permeability of the reservoir, respectively, but the relationship between grain size and reservoir properties is very complex. In sand-rich lithofacies, fine sandstones have the highest porosity due to their good sorting, and massive gravel-bearing coarse sandstones have the highest permeability due to their low clay content. Under the steep continental slope background, the submarine fan sedimentary microfacies are arranged in the order of muddy channel-sandy channel-lobe main body-lobe edge along the source direction, resulting in the source-directional differences in reservoir quality in the order of “poor, good, and poor”. The proximal muddy channel consists of fine-grained and clay-rich lithofacies, with overall poor physical properties. In the middle position, the sandy channel and lobe main body change to massive gravel-bearing coarse sandstone lithofacies and medium-coarse sandstone lithofacies, with low clay content and improved to good physical properties. Among them, the reservoir quality of the sandy channel is better than that of the lobe main body. The internal high porosity and high permeability zones of the sandy channel are in the form of elongated lenses, while the relatively high porosity and high permeability areas of the lobe main body are in the shape of lobes. The distal lobe edge changes to fine-grained lithofacies (fine-medium sandstone, fine sandstone) with increased clay content and gradually deteriorated physical properties.