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 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.

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.

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.

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

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

The 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.

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.

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.

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.

Total organic carbon(TOC)content is a crucial geochemical parameter for assessing reservoir quality and hydro-carbon generation potential of source rocks.The accurate prediction of TOC content is important for optimizing the exploration and development processes of shale oil and gas.With the rapid development of artificial intelligence technologies,individual machine learning algorithms have been increasingly applied to evaluate TOC content in shale.Despite the promising results of the individual machine learning algorithms,they are often subject to several challenges including overfitting,underfitting,and getting trapped in local optima of objective function.To address these limitations,the ensemble learning models are developed.Ensemble learning models leverage the strengths of multiple individual intelligent algorithms to enhance prediction accuracy and stability.Among them,combination strategy is one of the key factors in optimizing the ensemble learning models.Arithmetic average method as the simplest combination strategy fails to fully use prediction performance of the best individual intelligent model,and it can be severely affected by the individual intelligent model with a large prediction error,which can interfere with prediction outcome of overall model.In comparison,weighted summation method as a common combination strategy assigns the weights to different individual intelligent models according to their performance on training data.This method will perform excellently on training set,but it tends to have a poor performance when applied to test set.This paper develops an ensemble model based on an intelligent matching technology(IMTEM).The proposed method utilizes a set of robust intelligent algorithms including extreme gradient boosting,random forest,support vector machine,and extreme learning machine as algorithm modules to initially process input data.Then,the processed feature information combined with original log responses is fed to feedforward neural network layer for nonlinear transformation and feature learning,thereby enabling accurate and continuous estimation of TOC content in shale.To validate effectiveness of the IMTEM,the proposed method is applied to the prediction of TOC content in the Longmaxi Formation shale in the Sichuan Basin.Test results indicate that,compared to two ensemble models,five baseline models,and the ΔlogR method,predictions of the IMTEM exhibit higher consistency with measured TOC content.This demonstrates that the IMTEM is more suitable for predicting TOC content in shale.

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

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

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

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

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.

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.

Volcanic reservoirs are important fields for oil and gas exploration in China. Affected by multiple volcanic activities and complex diagenesis and tectonic processes, these reservoirs exhibit diverse lithology, well-developed fractures, and strong micro-heterogeneity, which restrict the understanding of such reservoirs. Clearly defining the reservoir characteristics under dual-media conditions is of great guiding significance for the evaluation of storage capacity, optimization of development strategies, and identification of sweet spots in volcanic rock oil reservoirs. To address these challenges, this paper proposes a quantitative classification and evaluation approach for dual-media reservoirs constrained by productivity. Firstly, based on the pore structure characteristic parameters obtained from mercury injection tests (mean pore radius, sorting coefficient, skewness, kurtosis, and coefficient of variation), the K-means clustering method is employed to establish the classification criteria for the matrix, then the Shapley value is incorporated to improve the interpretability of the classification results. Secondly, fracture classification criteria are established using the K-means clustering method based on fracture density derived from electrical micro-imaging (EMI) logs. Finally, by integrating the fracture and matrix classification results, we employ the particle swarm optimization algorithm to optimize the dual-media reservoir classification criteria. The optimization objective is to minimize the total sum of squared deviations of maximum monthly production among different reservoir types within the study area. The classification results are further validated through production characteristic analysis of different reservoir types based on Arps production decline model. The results show that four types of reservoirs with gradually deteriorating physical properties, namely, micro-fracture type, condensed tuff type/tuffaceous sandstone type, tuff type, and tight matrix type, are mainly developed in the study area. The factors influencing the matrix reservoir classification results are, in descending order of significance: permeability, mean pore radius, skewness, and porosity. The fractured reservoirs are classified into four categories based on fracture density thresholds of 11.35, 6.14, and 3.03 fractures/m. Comprehensive classification results reveal that both seepage capacity and storage capacity significantly impact reservoir performance. The Pearson correlation coefficient between the fracture category and comprehensive category is 0.63, while that between the matrix category and comprehensive category is 0.69. Production analysis demonstrates that all wells follow Arps’ exponential decline model, showing a positive correlation between initial production and decline rates. As reservoir quality declines, oil wells exhibit both lower initial production and slower decline rates. This paper not only provides a quantitative understanding of the classification of volcanic rock reservoirs but also offers a methodological reference for characterizing other dual- and triple-media reservoirs requiring multi-dimensional evaluation approaches.

Under high operational costs and selective applicability constraints, time-lapse (4D) seismic monitoring requires a rigorous feasibility assessment before implementation. The critical aspect of this analysis involves predicting post-production changes in formation elastic parameters, which are influenced by complex factors including reservoir temperature variations, pore pressure changes, and alterations in rock frame properties during hydrocarbon production. To achieve a quantitative evaluation of time-lapse seismic feasibility, a methodological framework based on rock physics modeling has been established, using several shallow, unconsolidated sandstone reservoirs in the Bohai Bay Basin as representative examples. The targeted sandstones exhibit high porosity and weak cementation, requiring specialized modeling approaches. Based on the geological background of the target areas, sedimentary environment analysis and burial history reconstruction are integrated with rock physics techniques to build geologically constrained models. These models facilitate the quantitative description of petrophysical characteristics under varying burial and diagenetic conditions. Based on the constructed rock physics models, evaluations of rock frame stiffness and pore fluid properties are conducted, and elastic parameter variations caused by reservoir production are predicted. Forward seismic modeling is then applied to assess the detectability of time-lapse seismic signals under different acquisition scenarios. Given the limited thickness of the target reservoirs, wedge-shaped model simulations are employed to analyze the sensitivity of time-lapse seismic responses to changes in formation thickness. The overall feasibility of time-lapse seismic application is assessed from three dimensions: geological conditions, rock physical properties, and seismic detectability. Results indicate that most studied reservoirs exhibit favorable geological frameworks and competent rock skeletons. However, the nature of the pore fluids, especially hydrocarbon composition and phase behavior, emerges as the critical factor influencing the effectiveness of time-lapse seismic monitoring. Light oil reservoirs show greater potential for successful monitoring due to more significant impedance contrasts. Among the studied cases, the reservoir exhibiting the highest suitability for time-lapse seismic monitoring is selected for further analysis using existing seismic datasets. Time-lapse seismic data matching processing and 4D response evaluation are performed to validate the reliability of the feasibility assessment framework. The findings demonstrate that the proposed approach can provide robust support for evaluating the feasibility of time-lapse seismic monitoring in shallow, unconsolidated sandstone reservoirs in the Bohai Bay Basin. These insights contribute valuable guidance for future applications of 4D seismic in similar geological settings, offering meaningful implications for both petroleum geology and reservoir engineering disciplines.

As a critical technical approach for shale reservoir development,dynamic imbibition displacement during the fractur-ing stage has emerged as a focal point in reservoir engineering research over recent years.In light of global energy demands and ongoing exploration of unconventional oil and gas resources,the significance of this technology in enhancing the exploitation of shale oil reservoirs cannot be overstated.However,the specific mechanisms of dynamic imbibition process in shale oil reservoirs influenced by various factors still aren't unclear,and it's difficult to accurately quantify their impact on imbibition oil production efficiency.These uncertainties significantly hinder further improvement in the development efficiency of shale oil reservoirs,lead to higher development costs and bring huge challenges to sustainable resource development. Aiming at the unclear dynamic imbibition mechanisms and action laws of shale oil reservoir,a core-scale numerical simulation model was established,and the control variable method was adopted to set up 15 simulation schemes.By these methods,the mechanisms of displacement pressure difference,capillary radius,wetting angle and oil-water viscosity of dynamic imbibition displacement effect,and the change laws of fluid seepage were revealed.The effects of displacement pressure difference,capillary radius,wetting angle,and oil-water viscosity on the effectiveness of dynamic imbibition oil recovery,and the laws of fluid seepage changes were clarified in this study.The results show that:During dynamic imbibition,as the capillary radius increase from 0.1 μm to 10 μm,capillary force decrease and fluid seepage rate accelerates,leading to 8.0%increase in imbibition recovery.Along with the displacing pressure difference increases from 0 MPa to 3 MPa,the imbibition upgrades from static to dynamic,and the imbibition recovery degree increases by 7.9%.It is considered that the displacing pressure difference and the recovery degree are in accordance with the power function relationship,and there is an optimal displacing pressure difference.With changes in rock wettability from hydrophilic to neutral or oleophilic,extraction degree decreases from 48.9%for water-wet conditions to 33.9%for oil-wet conditions.As crude oil viscosity decreases from 53.3 mPa·s to 13.99 mPa·s,imbibition recovery rate increases by 9.1%;the higher the viscosity of water phase,the smaller the initial imbibition velocity,but the better the imbibition displacement effect.In oil field operation,by optimizing injection pressure,selecting suitable fracturing fluid and surfactant,the hydrophilic degree and displacement phase viscosity can be improved,and the dynamic imbibition process can be improved to increase the oil displacement efficiency.In the future,the complexity of multiphase flows and the heterogeneity of reservoirs should be further considered to study the influence of various factors on the dynamic imbibition process of shale from different scales.

In carbon dioxide enhanced oil recovery and storage (CCUS) projects, sustained annular pressure is commonly attributed to packer sealing failure; however, pressure testing confirms the integrity of packer seals. This study proposes that this phenomenon arises from additional stress accumulation induced by temperature-pressure-phase coupling during CO₂ injection, leading to equivalent loads exceeding the packer’s sealing limit. By integrating the conservation laws of mass, energy, and momentum in fluid mechanics with the EXP-RK equation for phase behavior analysis, a coupled temperature-pressure-phase-stress multi-field model was developed to systematically quantify the synergistic effects of injection temperature, pressure, volume, and duration on wellbore stress fields. Validation using field data from a CO₂ injection well in the Changqing Oilfield demonstrated model accuracy, with a bottomhole pressure prediction error of 2.83% and temperature error of 1.75%, significantly outperforming conventional single-field models. Key findings include: (1) The injection temperature and injection volume are the main controlling factors for phase state evolution. The liquid-supercritical phase transition interface decreases significantly with the reduction of injection temperature and the increase of injection volume. (2) The injection temperature, pressure, and volume govern stress accumulation in the packer through thermal expansion, ballooning effects, and fluid friction. Critical threshold analysis revealed that equivalent loads exceed the unsealing limit (60 kN) when the target block meets any one of the following critical conditions: injection temperature≤6.5 ℃, pressure≥18.06 MPa, or volume≥2.61 t/h, causing packer unsealing.

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

Distributed fiber optic monitoring in adjacent wells has increasingly become an essential technique for fracture surveillance during hydraulic fracturing of unconventional oil and gas reservoirs. Developing forward models for distributed fiber optic strain response in adjacent wells is of significant importance for understanding the mechanism of fiber response and for the inversion of fracture geometries. However, existing forward interpretation models face limitations from insufficient flexibility in the selection of fracture propagation models, and by computational inefficiency caused by grid-based discretization, especially when high precision is required. To address these limitations, this study presents a semi-analytical stress-displacement field model for simulating fracture propagation with arbitrary aperture and geometric shape. Based on this, a forward modeling framework for adjacent well distributed fiber optic strain response is established. Using a penny-shaped fracture as a representative example, the stress field around the fracture is calculated and benchmarked against the classical Sneddon analytical solution. A forward simulation of fiber optic strain response for a scenario where a horizontal adjacent well monitors a vertically oriented elliptical fracture is conducted. The results are compared with the forward-modeled strain response from the Displacement Discontinuity Method(DDM). The results reveal strong consistency between the semi-analytical model and both the analytical and DDM solutions in classical benchmark cases, confirming the model’s validity and applicability. The model is further coupled with various fracture propagation models and applied to the interpretation of real field data. In particular, distributed fiber optic monitoring results from Stage 19 of Well B1H and Stage 20 of Well B2H in the Hydraulic Fracturing Test Site 2 (HFTS-2) project in the United States are analyzed. The modeling results show that the proposed approach accurately reproduces the characteristic patterns observed in field fiber data. For Stage 20 of Well B2H, which exhibits higher-complexity response characteristics, the model provides a closer match to observed details and temporal evolution compared with the DDM-based approach. In conclusion, this study establishes a semi-analytical forward modeling approach for fiber optic strain in adjacent wells under arbitrary fracture aperture and geometry, significantly reducing computational cost and improving efficiency. The model’s flexibility enables seamless integration with a variety of fracture propagation models, enhancing its capacity to accurately capture complex fracture behaviors observed in field monitoring. This provides a powerful tool for detailed interpretation and analysis of distributed fiber optic data in adjacent well applications.

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

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.

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.

The real-time and accurate monitoring of the production well fluid profile is crucial for guiding dynamic adjustments in oilfield development. It plays a critical role in evaluating the proportion of fluid produced from different production layers, optimizing parameters for horizontal well fracturing, and adjusting production dynamics. In recent years, fluid profile testing based on distributed acoustic sensing (DAS) technology has emerged as a new method with high accuracy and strong real-time capabilities. This technique is particularly suitable for the high-temperature, high-pressure, and narrow-complex downhole environments commonly found in oil and gas fields. However, most current research on distributed fiber-optic profile monitoring is focused on theoretical studies and laboratory experiments, with limited application to actual production conditions of the mining field operations. In actual production, downhole fiber-optic signals are often subject to interference from complex noise, and their response characteristics can be highly variable. Furthermore, there is a lack of mature analysis processes and models for using DAS technology to analyze downhole fluid events and calculate production profiles. This paper proposes a model and calculation process for fluid profile analysis that can be applied to mining field production scenarios. The method involves deploying distributed fiber-optic acoustic sensing to collect fiber-optic data under various operational conditions. Frequency analysis is then performed to identify effective frequency bands, and the fluid profile is calculated from the perspective of acoustic energy. This approach addresses the challenge of limited analysis methods for calculating production profiles from distributed acoustic fiber-optic data. To validate the proposed model and process, the paper analyzes data from three wells in a mining field. The results indicate that in the 400-800 Hz frequency range, the maximum difference in Fiber-Based Energy (FBE) energy occurs during well switching. This allows the system to filter out most background and environmental noise while retaining important flow-related information. After opening the wells, all three wells showed a delay in energy response. During the production phase, the production layers did not extend to all depths, and a dominant influx region was observed. However, after the second well opening, the overall fluid profile distribution became more uniform. Additionally, the intensity of the FBE energy varied between the first and second well openings, with stronger absolute FBE energy observed during the first production phase. These findings provide valuable insights into optimizing oilfield operations and improving the accuracy of fluid profile monitoring through distributed acoustic sensing technology.

Well logging curves are essential for recording the physical parameters of formations during drilling,providing vital information for analyzing rock properties,evaluating hydrocarbon reservoirs,and understanding reservoir distribution.As oil and gas exploration continues to progress,the complexity of subtle and hidden reservoirs has increased,posing challenges for traditional exploration techniques.Despite their importance,conventional well logging data suffer from low resolution,which significantly limits their ability to address the requirements of detailed reservoir characterization.In particular,the inability to precisely identify modification points in thin interbedded reservoirs remains a critical bottleneck in reservoir analysis.To overcome these limitations,developing high-resolution interpretation methods for well logging data has become an urgent priority in the field of reservoir analysis and geological exploration.This study proposes a novel reservoir prediction model based on the ResNet50 regression algorithm.By integrating vertically continuous optical thin-section data,which can capture fine-scale and complex vertical geological features,with five conventional well logging parameters,the proposed model aims to improve the resolution and accuracy of reservoir analysis.This combination leverages the strengths of image-based geological analysis and traditional well logging to deliver a more precise interpretation of subsurface formations.The model was validated using data collected from five intervals of the Permian formation in a specific well area.A total of 570 continuous geological image samples,combined with their corresponding well logging data,were utilized for model training and prediction.The results demonstrate that the model effectively enhances the resolution of well logging data,improving it from the traditional 12.5 cm to 6.25 cm.This significant improvement not only increases the precision of well logging interpretation but also provides a more detailed under-standing of reservoir characteristics.The model's performance was rigorously evaluated using three widely recognized metrics:the coefficient of determination(R2),root mean square error(RMSE),and mean absolute error(MAE).The results revealed that the model excels in predicting parameters such as acoustic time(AC),compensated neutron(CNL),resistivity(RT),and gamma ray(GR),achieving an average prediction error below 0.094.This highlights the model's reliability and superior performance in reservoir prediction tasks.However,challenges remain in predicting density(DEN),where the model's accuracy is impacted in intervals with significant lithological heterogeneity or complex geological conditions.

Shallow water deltas are widely developed in lake environments. Under the modification of waves, offshore beach-bar deposits can be formed at the delta front. The shallow water delta-offshore beach-bar system in the lacustrine basin can be an important oil and gas reservoir type, and its architectural characteristics are still unclear. In this paper, the sand bodies of the 2nd submember of the 2nd member of the Eocene Shahejie Formation (Es₂) in the 2nd Block of Shengtuo Oilfield, Dongying Sag, Bohai Bay Basin were finely dissected by using core and abundant well data. Combined with the results of sedimentary numerical simulation based on Delft3D, the architectural characteristics and formative mechanism of the shallow water delta-offshore beach-bar system in the lacustrine basin were explained. The study believes that the shallow water delta-offshore beach-bar system has obvious facies differentiation characteristics along the provenance direction, showing the evolution law of shallow water delta front to small-scale beach sand to large-scale bar sand to small-scale beach-bar sand. The shallow water delta front and large-scale offshore bar sand were formed simultaneously, while the small-scale beach sands developed later in areas between them. The front of the shallow delta shows the characteristics of wave influence. The number of distributary channel branches is small and the distributary channel is deeply cut in the middle of the mouth bar. The mouth bar is in the shape of a fan. After the distributary channel has extended a certain distance, it avulses and forms a new mouth bar laterally. Large-scale lenticular thick bar sands are formed proximal to the surf zone, initially developing on both sides of delta-front shoreline sands through the growth and coalescence of two small bar units. These composite features display biconvex thickness maxima (dual-thick centers) with a characteristic humpbacked morphology. The scale of the mouth bar and the large-scale bar sand is relatively large and the aspect ratio is high. The length-to-width ratio of the former is low, while the length-to-width ratio of the latter is large; the scale of the proximal and distal beach sand is small, and the aspect ratio is also small. This study provides reference for the fine-scale potential tapping of remaining oil in the Shengtuo Oilfield of the Dongying Sag.

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.

Shale oil is one of the most potential and strategic alternative oil resource in China.It's of great significance to clarify the fluid distribution and evolution laws in porous media for enhancing the recovery of shale oil during the fracturing-soak-ing-producing process.In this work,a multi-component multiphase lattice Boltzmann model was adopted to study the shale oil flow mechanism during fracturing-soaking-producing process.Firstly,the accuracy of the model was verified using Laplace's law,contact angle,and stratified flow.Then,based on the scanning electron microscope(SEM)image of Jiyang shale,the struc-ture of the shale porous medium was constructed including the distribution of fracture and matrix pores.Subsequently,the lattice Boltzmann model was used to simulate the fracturing-soaking-producing process of shale porous media,and the fluid distribution characteristics at different stages were analyzed.Then the effects of different soaking time,reservoir wettability and drainage rate were explored further.The results show that the fracturing fluid will seep into the matrix pore and replace the oil phase under the action of capillary force during the soaking stage,and with the increase of soaking time,the backflow rate of fracturing fluid return tends to decrease;the water-wet core has a better development effect than the neutral and oil-wet cores,and the utilization rate of fracturing fluid and the degree of crude oil utilization in the matrix are higher;the higher drainage rate will make the pore pressure drop rapidly,which is not conducive to the development and production of the shale oil.The fluid flow mechanisms during the shale oil fracturing-soaking-producing process are investigated from a pore-scale perspective,which provides support for the formulation of a reasonable production schedule for shale oil wells.

Coiled tubing drag acidizing is a commonly used diversion technique in carbonate reservoir horizontal wells. However, due to challenges such as long wellbore length, strong formation heterogeneity, and uneven damage distribution, predicting acidizing effectiveness remains uncertain. To address this, a radial dual-scale wormhole propagation model and a numerical model of coiled tubing drag acidizing were developed to simulate the evolution of acid etching patterns and the mechanism of damage removal under varying injection parameters. Field data from the X oilfield in Iraq were integrated for process optimization. Simulation results show that as the acid injection rate increases, acid etching morphologies evolve from conical pores to dominant wormholes, branched wormholes, and eventually uniform dissolution. Dominant wormholes require the least acid volume and exhibit the highest propagation efficiency. Wormhole length increases significantly with acid volume intensity, extending from 0.55 m to 1.42 m as intensity increases from 0.2 to 0.8 m³/m. Greater damage depth and lower permeability contrast lead to poorer acidizing outcomes. Based on the analysis, when the damage depth is less than 0.5 m, an injection rate of 0.5 m³/min and an acid intensity of 0.3~0.4 m³/m are recommended. For damage depths around 1 m, the recommended rate is 1 m³/min with an acid intensity of 0.4~0.7 m³/m. Field results confirm that combining coiled tubing drag acidizing with near-heel pinpoint injection can enhance well productivity by 2~5 times, outperforming other acid placement strategies. This study provides theoretical and practical guidance for optimizing acidizing parameters and operational design in carbonate long horizontal wells.

In drilling engineering, the analysis of wellbore stability is directly related to the design of wellbore structure, the design of drilling fluid density window and the safety of drilling operations. Traditional strength criteria, due to mostly linear assumptions and the neglect of the intermediate principal stress effect, are difficult to accurately characterize the nonlinear failure characteristics of rocks under deep and complex geological conditions. Therefore, the selection of an appropriate strength criterion is the key to accurately evaluating wellbore stability. This paper uses an improved three-dimensional Hoek-Brown strength criterion and combines theoretical research with model application to establish a wellbore stability evaluation model that is more in line with actual working conditions. The research results show that this criterion not only retains the nonlinear characteristics of the Hoek-Brown strength criterion in the meridian plane, but also considers the influence of the intermediate principal stress on rock strength. The yield surface of the improved three-dimensional criterion in the principal stress space is a conical surface, and the yield curve satisfies the properties of being outwardly convex, smooth and closed. Through the comparative analysis of the true triaxial test data of six different rocks, it is proved that this criterion has good applicability to various rocks. When this criterion is applied to the wellbore stability analysis of the Jialingjiang Formation limestone strata in the Tongluoxia Gas Field in the eastern Sichuan Basin, the calculated collapse pressure equivalent density is 1.16 g/cm³. When drilling along the direction of the maximum horizontal principal stress, the collapse pressure equivalent density is the lowest, which is in line with the actual working conditions and is superior to the other four traditional strength criteria. The improved three-dimensional Hoek-Brown strength criterion provides a more accurate and practical method for wellbore stability analysis.

In the context of increasing global oil price volatility and escalating supply chain risks, enhancing the stability and self-sufficiency of crude oil supply has become a strategic priority for ensuring national energy security. Extra heavy oil, characterized by abundant reserves and low cost, holds significant potential to be converted into high-quality synthetic light crude through hydrogen-based deconstruction using green hydrogen. However, the economic viability of this pathway is highly sensitive to fluctuations in key factors such as extra heavy oil procurement prices, green hydrogen production costs, and renewable electricity prices. Therefore, it is essential to develop a quantitative evaluation and optimization model under multi-dimensional uncertainty to support investment decision-making. Building on conventional techno-economic analysis frameworks, this study incorporates the effects of multi-dimensional stochastic variables by employing parameter perturbation and Monte Carlo simulation methods to construct a breakeven price model under uncertainty. Based on the volatility characteristics of extra heavy oil prices, a feedstock procurement and storage optimization model is further developed, forming a systematic evaluation and optimization framework. The results show that feedstock procurement costs and green hydrogen production costs are the primary factors influencing the distribution of breakeven prices. Under the baseline scenario, the breakeven price exhibits a right-skewed distribution with a skewness coefficient of 0.76, and the 10%~90% quantile range spans from 5537 to 7601 CNY/ton. The distribution has a long-tail characteristic, with a 5% probability of exceeding 8000 CNY/ton. Mechanistically, price increases raise marginal costs and shift the breakeven point to the right. Meanwhile, greater price volatility amplifies cost uncertainty, thickens the distribution tail, and increases the risk of extreme outcomes. Under a procurement and storage optimization strategy, annual cost savings of approximately 266 million CNY can be achieved, and the breakeven price distribution transitions from right-skewed to approximately normal. Compared to the baseline scenario without optimization, the probability of extreme high breakeven prices is significantly reduced, and the 10%~90% quantile range narrows by 1519 CNY/ton. Assuming a synthetic light crude market price of 7000 CNY/ton, the probability of profitability increases from 74% to 99%. Storage optimization effectively lowers the probability of high breakeven prices and enhances profit stability. In addition, reductions in green hydrogen production costs will further improve economic performance. The study also finds that investors’ varying risk preferences significantly affect their tolerance for return uncertainty, suggesting that policy design should account for such heterogeneity to enhance plan adaptability. This research provides scientific decision-making support for investment in extra heavy oil deconstruction via green hydrogen and offers a novel and practical pathway for enhancing energy security and achieving low-carbon transition under China’s carbon neutrality goals.

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.

Fractured-vuggy carbonate reservoirs differ from conventional reservoirs by exhibiting characteristics such as discontinuous spatial distribution of reservoir bodies, significant variations in effective storage space scale, complex internal structures and fracture-cavity connectivity, heterogeneous oil-water distribution relationships among different reservoir units, and complicated hydrocarbon flow mechanisms. Water flooding and gas injection serve as important methods for efficient development of fractured-vuggy carbonate reservoirs. However, the unclear timing for switching between water flooding and gas injection in fractured-vuggy carbonate reservoirs leads to difficulties in determining the optimal gas injection timing during field gas injection operations. Meanwhile, existing physical simulation models for fractured-vuggy carbonate reservoirs worldwide can only achieve either visualization or high-temperature-high-pressure conditions, resulting in insufficiently comprehensive research perspectives. To address these issues, this study independently developed a high-temperature-high-pressure two-dimensional visual physical model of fractured-vuggy reservoirs based on similarity principles. Using this model, we conducted water flooding and gas displacement experiments to investigate oil-gas-water flow mechanisms during the displacement process in fracture-cavity systems, evaluate the effects of different water-gas switching timings and various water injection positions on displacement efficiency, clarify the relationship between water and gas injection, and determine the optimal gas injection timing and spatial distribution characteristics of remaining oil. The experimental results show that: 1) Through water flooding followed by nitrogen injection experiments in the visual fractured-vuggy model, we confirmed that this method represents an effective development approach for fractured-vuggy reservoirs. Under the synergistic effects of water flooding characteristics and nitrogen gas-cap drive mechanisms, a high displacement efficiency of 67.67% was achieved. 2) Comparative experiments on different injection-production methods demonstrated that during water flooding development of fractured-vuggy reservoirs, the low-injection-high-production method provided larger water flooding sweep area and more effective water flooding performance compared with the high-injection-low-production method, resulting in 2%~4% higher overall displacement efficiency. 3) By comparing oil displacement effects under different nitrogen injection timings, the optimal development method was determined to be low-injection-high-production with nitrogen injection initiated after 1 PV of water flooding, which yielded the highest displacement efficiency. 4) After water flooding development in fractured-vuggy reservoirs, remaining oil primarily distributed as “oil films,” “attic oil,” and “bypassed oil.” Following gas flooding, the main remaining oil distribution patterns were “interfacial oil,” “bypassed oil,” and “oil films.” These research findings contribute to understanding the enhanced oil recovery mechanisms of water and gas injection in fractured-vuggy carbonate reservoirs, clarifying the optimal switching timing and remaining oil distribution patterns, and providing theoretical guidance for optimizing gas flooding development plans and remaining oil potential exploitation in fractured-vuggy reservoirs.