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.

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.

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.

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.

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 deep coalbed methane (CBM) resources (buried deeper than 1500 m) in North China exceed 30 trillion cubic meters, with significant breakthroughs in exploration, making it a new frontier for natural gas production growth. However, it faces key issues, including complex preservation conditions, unclear controls on enrichment and high productivity, and large production variations. In order to clarify the control factors of preservation conditions of deep CBM, the coal quality, lithology combination, and structural preservation characteristics of typical deep CBM exploration wells in North China are systematically analyzed with multidisciplinary analysis methods of geology, geochemistry, and geophysics. This study reveals that the differential preservation of deep CBM is controlled by the dynamic coupling mechanism of adsorption self-sealing, physical property sealing, and structural preservation system: (1) Favorable coal quality is the basis of strong adsorption. Coal with low ash content and a high evolution degree is characterized by a large Langmuir volume, a great critical depth, and strong self-sealing ability. (2) Tightly lithology combination is a barrier to deep free gas dissipation. Limestone and mudstone exhibit low porosity, a small throat radius, and high breakthrough pressure, resulting in good physical sealing conditions and a high total retained gas content. (3) Continuous and stable structural preservation is the key to free gas enrichment. Late-stage uplift with low amplitude and weak tectonic activity is conducive to free gas accumulation, resulting in a high-pressure coefficient. In summary, areas characterized by deep burial, low-ash coal, limestone/mudstone roof rocks, and structural stability are favorable for deep CBM preservation. The study of differential preservation of deep CBM deepens the understanding of enrichment and high productivity, and provides a theoretical basis for optimizing favorable exploration and development targets.

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.

The ongoing Fourth Industrial Revolution, characterized by artificial intelligence (AI), is driving a wave of intelligent transformation across the oil and gas industry. International oilfield service and operating companies in regions such as the United States, Norway, and the Middle East are investing heavily in digital and intelligent transformation to secure a competitive advantage in the future landscape. As a cutting-edge technology in oil and gas engineering, intelligent drilling and completion is poised to yield disruptive and leapfrog innovations, empowering the development of new quality productive forces. This paper elaborates on the conceptual framework of intelligent drilling and completion technologies and provides a comprehensive review of the current domestic and international development status and technical disparities across five key areas: surface equipment, measurement-transmission-conduction tools, drilling fluids, cementing, and software. It further identifies four major existing problems and four key challenges. Furthermore, it proposes the “1244” development direction: focusing on one goal—empowering technological innovation and industrial upgrading through “Drilling and Completion + AI”; concentrating on two major fields—intelligent drilling and intelligent cementing; tackling four key technological directions—equipment, tools, fluids, and software; and realizing four typical application scenarios—fully automated wellsite operations, autonomous drilling control, intelligent drilling fluid regulation, and intelligent cementing operations. Simultaneously, the article advocates building a technical system with the geology-engineering knowledge base as the foundation, large/small AI models as the backend support, and “Drilling and Completion + AI” as the front-end application carrier. This aims to accelerate the transition of intelligent drilling and completion technology from unit-level closed-loop to global closed-loop and remote-controlled operations.

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.

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.

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.

This paper systematically reviews the current technical framework and implementation approaches for drilling digital twin modeling, focusing on key challenges such as multi-source heterogeneous data fusion during wellbore drilling operations, the coupling of physics-based and data-driven models, and intelligent decision feedback mechanisms. First, targeting the objects and levels of drilling data fusion, the fusion mechanisms for multi-temporal and multi-spatial scale data and conflict resolution methods were explored. Second, a comprehensive digital twin architecture suitable for drilling operating conditions was established, and methods for implementing joint physics-based and data-driven modeling were summarized. Then, diagnostic methods for drilling anomaly data features were proposed, enabling the establishment of multi-perspective decision feedback mechanisms between the physical and digital entities. Finally, the application potential of wellbore digital twin technology was prospected in the areas of high spatiotemporal resolution cognition, edge deployment, and model credibility assurance. The research results can provide theoretical support and methodological guidance for achieving drilling state recognition and efficient control under complex operating conditions.

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 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.

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 enrichment patterns of natural gas in the tight sandstone reservoirs of the Xujiahe Formation in the northeastern Sichuan Basin are complex, and the fault system exerts a crucial control on hydrocarbon accumulation. To clarify the structural characteristics of the faults and their impact on natural gas enrichment in this area. Based on three-dimensional seismic data and drilling data, integrated with artificial intelligence fault identification technology and structural analysis, this study identifies a NE-SW trending en echelon strike-slip fault zone in the Xujiahe Formation of the Wubaochang Area, northeastern Sichuan Basin. The structural deformation characteristics, evolutionary history, and reservoir-controlling and accumulation-controlling effects of this fault zone are investigated. The results indicate that the fault zone exhibits vertically layered deformation characteristics: it manifests as thrust faults in the strata from the Upper Ordovician to the Lower Triassic Jialingjiang Formation, while in the Upper Triassic Xujiahe Formation, it appears as a strike-slip fault zone composed of a series of small NNW-SSE trending thrust faults arranged in a right-stepping en echelon pattern. The fault zone underwent two stages of tectonic evolution. During the Indosinian-Yanshanian period, under continuous SE-NW oriented tectonic compression, it exhibited thrust fault activity. In the Himalayan period, the regional tectonic stress field shifted, and under a NE-SW oriented horizontal compressional stress field, the pre-existing major faults underwent sinistral strike-slip movement, forming a series of right-stepping en echelon strike-slip faults in the Xujiahe Formation. The faults in the Xujiahe Formation are interconnected with the deep-seated major faults, linking the Xujiahe Formation reservoir, the Longtan Formation source rocks, and deep fluids. This connection establishes a “strong below and weak above” transport system and a “dual-source hydrocarbon supply” model. Simultaneously, it exerts a dual effect on the tight sandstone reservoirs of the Xujiahe Formation, namely “dissolution and porosity enhancement” and “cementation and destruction,” providing favorable dynamic conditions for the differential evolution of tight reservoirs and the development of sweet spot reservoirs. Consequently, the fault zone serves as a favorable site for natural gas enrichment and preservation in the tight sandstone reservoirs of the Xujiahe Formation. The research demonstrate that strike-slip fault zones are advantageous enrichment belts for natural gas in Triassic tight sandstone reservoirs in the northeastern Sichuan Basin, and greater emphasis should be placed on the evaluation and exploration of such target areas.

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.

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.

The goal of “carbon peak and neutrality” is driving China’s energy system to accelerate transition to clean, low-carbon development. As an important new clean energy source, natural gas hydrate (NGH) exhibits high energy density, wide distribution, and substantial resource potential. Therefore, accelerating its industrial production is key to achieving a reduction in pollution and carbon emission. NGH production involves Thermal-Hydrological-Mechanical-Chemical (THMC) multi-physical field coupling. Traditional experiments and production tests fail to fully reveal underlying mechanisms, making numerical simulation—with high functionality, flexible methods, and low cost—an essential research tool. This study systematically reviews theories, technologies, and applications of numerical simulation for natural gas hydrate production to provide theoretical support for safe, efficient extraction and advance the translation of simulation technologies to engineering practice. Specifically, it clarifies evolution laws of seepage parameters (e.g., porosity, permeability) during hydrate dissociation; identifies evolution of mechanical parameters (e.g., shear strength, cohesion) with hydrate saturation, revealing the core mechanism by which hydrates dominate reservoir mechanical property evolution via decomposition behaviour; outlines approaches to constructing THMC multi-physical field coupling models; summarizes functions and advantages of major global simulators (e.g., TOUGH+Hydrate, SuGaR-TCHM), and validates applications at typical pilot sites. Current numerical simulation research has limitations: multi-phase flow models insufficiently account for continuous pore structure evolution and impacts of hydrate saturation on relative permeability; characterization of mechanical properties and sand production risk responses in unconsolidated clayey silt sediments is inadequate; and capacity to predict long-term mechanical stability risks (e.g., land subsidence, submarine landslides) induced by production is limited. Future work should establish “micro-macro” cross-scale parameter models, refine elastoplastic constitutive models for clayey silt sediments, and develop integrated geological engineering simulation tools to advance simulation technologies from mechanistic interpretation to engineering decision support.

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.

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.

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.

To address the challenges of poor sorting, strong heterogeneity, and unclear overburden pressure-dependent variations in porosity and permeability in glutenite reservoirs, where existing conventional sandstone correction methods are inadequate, this study investigates the Paleogene Kongdian Formation glutenite reservoirs (burial depth: 3800~4100 m) in the BZ area. Using overburden pressure porosity-permeability experiments and rock mechanics test data from 13 core plugs covering the main porosity-permeability range, we reveal the unique variation patterns and establish a targeted correction model. The results show that this is a low-porosity, ultra-low-permeability reservoir, with matrix porosity of 5%~15% and permeability of 0.1~6 mD under ambient conditions. The pore space is dominated by intergranular and intragranular dissolution pores, with occasional microfractures, indicating significant heterogeneity. Unlike conventional sandstone reservoirs, the porosity and permeability of these glutenite reservoirs decrease following a power-law function with increasing overburden pressure. The decline gradient varies with the initial porosity and permeability at ambient pressure. Thus, the overburden pressure-dependent porosity/permeability is a bivariate function of the overburden pressure and its initial value at ambient conditions. By fitting the experimental data, we derived the quantitative relationship between the power-law coefficients and the ambient-pressure porosity/permeability, constructing a triaxial overburden pressure correction model. Incorporating rock mechanics data, a uniaxial overburden pressure bivariate correction model for in-situ conditions was also established. Accuracy verification shows that the triaxial model achieves an MSE of 0.369% for porosity and 0.281 mD for permeability. Compared to the traditional univariate model, accuracy improves by 67% and 43%, respectively, providing reliable characterization across both high and low porosity-permeability ranges. The proposed bivariate correction method fills a gap in overburden pressure porosity-permeability correction for glutenite reservoirs and solves the problem that traditional methods cannot adequately account for their strong heterogeneity. This method provides an essential correction tool for accurate reserve evaluation and productivity prediction, and offers a theoretical basis for designing efficient development strategies.

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.

Drilling digital twin technology, as a core method for virtually mapping actual drilling processes and enabling visual monitoring and intelligent decision-making, has become one of the inevitable trends in the future development of intelligent drilling. Drilling digital twins primarily involve two aspects: surface drilling rigs and subsurface wellbores. While surface rigs have entered systematic research phases globally due to their relatively stable nature and abundant mature experience, subsurface wellbores present significant challenges. These include invisibility, tangibility issues, high uncertainty, complex operating conditions, and the involvement of multi-medium and multi-physics coupling phenomena. This paper systematically elaborates on the core theories, key technologies, and development trends of constructing and applying digital wellbore models for drilling operations. It aims to provide a reference for subsequent research and engineering practice in this field.

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.

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.

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.

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.

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.

Temporary plugging agents are widely used in temporary plugging and steering fracturing in horizontal wells, playing a critical role in enhancing reservoir stimulation efficiency. Current study on temporary blocking agents conducted both domestically and internationally are mostly limited to experimental methods, and there is a lack of macroscopic simulation research on the migration and plugging processes of temporary plugging agents. In this paper, a numerical simulation method based on the coupling of Computational Fluid Dynamics (CFD) and Discrete Element Method(DEM) was used to simulate the downhole migration and plugging process of temporary plugging agents during the fracturing temporary plugging process of horizontal wells. In the simulation, the plugging agents were regarded as the discrete phase composed of individual particles, while the fracturing fluid was regarded as the continuous phase. Separate mathematical models were established for the discrete phase and the continuous phase, and the interaction between the discrete phase and the continuous phase was coupled at the same time, so as to realize the fluid-solid coupling in the multi-phase system of temporary plugging agent and fracturing fluid. For the migration process of temporary plugging agent from the wellhead to the target plugging well section, the wellbore model, “wellbore-borehole-single fracture” and “wellbore-borehole-multiple fractures” models were established. The effect of the temporary plugging agent particle size, concentrations, fracturing fluid’s viscosity and pumping rate to the migration integrity was investigated, and the effect of fracture shape to the plugging effect was also explored. The results indicate that the concentration of the temporary plugging agent, fracturing fluid’s viscosity and pumping rate are important factors affecting the integrity of the temporary plugging system. The particle size and concentration of the temporary plugging agent is the key factor that determine whether the temporary plugging system could effectively plug fractures. If the particle size of the temporary plugging agent is above 20 mesh, the change in mass concentration of the temporary plugging agent will only affect the length of the blocking section, but will not affect the effectiveness of the temporary plugging in the fracture. Once the width at the end of the fracture reaches 4 mm, it is difficult to completely plug the fracture in the direction of the fracture height using a temporary plugging agent with a particle size of 20-70 mesh. The study provides reasonable theoretical guidance for the selection of process parameters and construction parameters for the temporary plugging fracturing in horizontal wells.

Gravel packing is an effective completion technique to meet sand-control and productivity-enhancement needs of natural gas hydrate reservoirs. Friction at gravel-gravel interfaces and their bonding with hydrates directly affect the packing process and the mechanical properties of the gravel-pack structure. However, particle-scale studies that systematically clarify the mechanisms of friction and bonding between hydrates and packing gravel are still lacking, and quantitative characterization of inter-particle micro-forces under the coupled effects of “wetting-temperature-contact type” remains insufficient. In this study, a customized high-precision micro-force testing apparatus, combined with in-situ high-resolution microscopic observation, was employed to investigate inter-particle interactions at the single-particle contact scale. Three wetting states-dry, partially wetted, and fully wetted-and multiple contact configurations, including bare contact, ice-coated contact, and hydrate-coated contact, were clearly distinguished. The friction coefficients and bond strengths among tetrahydrofuran (THF) hydrate, ice, and gravel particles were systematically measured. The results show that: (1) THF-hydrate particles exhibit overall higher friction coefficients than ice, primarily due to their roher surfaces and the higher propensity for brittle micro-fracture during sliding; (2) interfacial friction varies nonlinearly with water content: compared to dry particle contacts, a modest amount of liquid water reduces the friction coefficient due to thin-film lubrication, whereas excess water increases the friction coefficient again through viscosity and capillary-bridge effects; when gravel is coated by ice or hydrate, the friction coefficient decreases markedly; (3) at low temperatures, pronounced bonding occurs at both hydrate-gravel and ice-gravel interfaces, with failure dominated by interfacial brittle fracture, indicating that rupture preferentially occurs at the particle-hydrate interface rather than within the hydrate itself. These particle-scale findings elucidate how wetting, temperature, and contact configuration govern friction coefficient and bond strength among hydrates, ice, and gravel, and they provide a basis for parameterized modeling of gravel-packing operations and for reliability assessment in sand control and productivity enhancement.

The Lucaogou formation of the Permian System in the Jimsar Sag, Junggar Basin, represents a typical example of mixed siliciclastic-carbonate continental shale oil in China. After more than a decade of exploration and pilot development, remarkable technological and production breakthroughs have been achieved, confirming its substantial resource potential. Nevertheless, the current depletion-based development mode continues to face critical challenges such as rapid formation energy loss, sharp production decline rates, and persistently low recovery factors. The key technical direction for improving oil recovery at the later stage of development therefore remains unclear and urgently requires systematic investigation. To address these issues, a coupled numerical model of hydraulic fractures and the shale matrix was established based on the actual geological model of a well platform located in the lower sweet-spot interval of the Jimsar Sag. The model was validated through history matching by optimizing key reservoir and fracture parameters. On this basis, an inter-well gas injection displacement scheme involving three horizontal wells was designed, in which gas is injected through the central well and oil is produced from the two adjacent wells following an initial depletion phase. Simulation results show that, for Jimsar shale oil characterized by an ultra-tight matrix and poorly developed natural fractures, the degree of communication between horizontal wells through hydraulic fractures is the key factor determining the effectiveness of gas injection development. Comparative analyses of different injected gases (CO₂ and CH₄), injection timing, and injection pressure were performed to clarify their influence on the displacement process. Based on these results, an optimized operation strategy was proposed, involving cyclic reinjection of produced gas after the initial breakthrough of the injected gas at the production wells. Compared with depletion development, the designed inter-well gas injection scheme increases the cumulative oil production by approximately 45%, while the oil utilization ratio-an indicator of carbon and energy efficiency-nearly doubles. The proposed cyclic reinjection of produced gas further enhances carbon utilization efficiency and reduces the external CO₂ supply demand, thereby achieving a dual objective of improving oil recovery and promoting effective carbon recycling. Overall, the developed coupled model and the derived optimization strategy provide a technically feasible and scientifically grounded approach for enhancing oil recovery in continental shale oil reservoirs. The findings clarify the mechanisms and governing parameters of inter-well gas displacement under ultra-tight conditions, and the proposed cyclic reinjection mode offers a practical pathway for synergistic oil production enhancement and CO₂ geological utilization. This study provides important theoretical support and engineering reference for large-scale application of gas injection displacement and CO₂ sequestration in the late-stage development of continental shale oil fields in China.

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.

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.

Under the “dual-carbon” target, geothermal energy has become an important direction for energy transition due to its characteristics of being clean and efficient, having abundant reserves, and being low-carbon and environmentally friendly. Aiming at the seepage-heat transfer evolution characteristics and well-pattern optimization of geothermal fields under multi-well production and reinjection conditions, this study takes the geothermal field in the Xiong’an high-speed railway area as the research object. A three-dimensional coupled seepage-heat transfer numerical model was constructed based on COMSOL Multiphysics, and parameter calibration was carried out by combining field monitoring data of production-well water temperature and groundwater level to ensure consistency between numerical results and actual operation conditions. On this basis, twenty additional geothermal wells were designed, including ten production wells and ten reinjection wells. By simulating three well-pattern schemes—linear, staggered, and triangular—the variations in production-well water level, temperature, and dynamic recoverable geothermal resources under different schemes were analyzed, and the influences of different schemes on reservoir stability and heat-exchange efficiency were systematically compared. The results show that the linear well pattern has a simple structure and is easy to implement; however, its thermal mobilization range is limited, the overall recoverable geothermal resources are the lowest, and boundary production wells are prone to forming localized groundwater drawdown cones. The staggered well pattern exhibits a more uniform distribution of production and reinjection, which can effectively suppress localized overexploitation, delay cold-water breakthrough, and maintain relatively high reservoir thermal stability, resulting in the most stable evolution of dynamic recoverable geothermal energy. The triangular well pattern shows the strongest thermal mobilization capacity and the highest recoverable geothermal resources during the early stage of operation, but the risk of cold-water intrusion increases in areas with high production-well density, leading to a tendency of localized thermal attenuation in the later stage of operation. On a 50-year timescale, the total recoverable geothermal resources of both the staggered and triangular well patterns are more than 1% higher than those of the linear well pattern. Among them, the staggered well pattern achieves the best balance between resource utilization efficiency and system stability and is recommended as the preferred well-pattern scheme for the efficient and sustainable development of the geothermal field in the study area. The research results can provide theoretical references for well-pattern optimization, development performance evaluation, and sustainable operation of multi-well production and reinjection systems in mid- to deep-depth hydrothermal geothermal fields under similar geological settings and operating conditions.