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Aiming to enhance understanding of the measured strain by distributed fiber optic sensing, a universal fiber strain decomposition model and interpretation framework have been proposed according to tensor analysis. And then the strict solutions to strain decomposition equations within elastic small deformation situation have been also given that adopted to most problem scenario covering fiber deployment in plane and cylindrical surface. Specifically addressing the issue in rock mechanics where there is an unclear understanding of the decomposition of helical fiber strain in uniaxial and triaxial compression tests, starting from the geometric definition of strain and using the law of cosines, this study for the first time derived the elastic small-deformation solution of the fiber strain decomposition equation consistent with the tensor projection method, and dialectically discussed the currently published strain decomposition methods. In addition, it also proved the existence of a critical helical rising angle that can make the measured fiber strain zero, and pointed out that fiber deployment strategies near the critical helical angle can relax the measurement limits within the fiber strain range. Finally, starting from the engineering strain geometric definition and combining with the law of cosines, the study for the first time fully presented the fiber strain decomposition equation solution applicable to geometric large deformations. The work emphasizes that in actual fiber deployment and interpretation processes, it is necessary to clarify the problem scenario requirements and assumptions, and to adopt an appropriate fiber deployment geometry and corresponding strain decomposition method.
Bedding shale is prone to forming secondary fractures along bedding weak planes during hydraulic fracturing, and the dynamic propagation behavior of these fractures plays an important role in fracture network complexity and reservoir stimulation effectiveness. However, conventional monitoring methods are unable to continuously and accurately capture the full process of secondary fracture initiation and propagation at high spatial resolution. To address this issue, an indoor experimental method for investigating the dynamic propagation of bedding-induced secondary fractures in shale was established based on optical frequency domain reflectometry (OFDR) distributed fiber-optic strain monitoring technology. Comparative constant-pressure fluid injection tests were conducted on two typical shale samples with bedding orientations parallel and perpendicular to the injection direction. The results show that OFDR technology can effectively identify the spatiotemporal evolution characteristics of strain during the initiation and propagation of bedding-induced secondary fractures in real time. Under the parallel-bedding condition, distinct bedding fractures were formed inside the sample, and the fiber-optic strain contour maps exhibited positive strain bands progressively propagating along the bedding direction, accompanied by negative strain zones around the fractures and local diffusion signals. Under the perpendicular-bedding condition, the sample was dominated by an overall negative strain response, with no obvious macroscopic fractures formed; only a weak positive strain diffusion zone appeared near the injection end. Significant differences were observed between the parallel- and perpendicular-bedding samples in terms of fracture morphology, strain field evolution, and pressure-flow response, indicating that bedding orientation exerts a strong control on the initiation and propagation of secondary fractures. These findings provide an experimental basis for the dynamic identification of secondary fractures and the analysis of their propagation mechanisms during hydraulic fracturing of bedded shale.
Laboratory-scale physical simulation experiments of hydraulic fracturing make it possible to reproduce fracture initiation, propagation, and closure under controllable conditions. By representing these sequential stages in a controlled experimental environment, such experiments provide an important means of investigating the evolution of fracture geometry, the redistribution of stress associated with fracture growth, and the mechanisms governing interactions between fractures. However, conventional monitoring approaches used in physical simulation experiments rely largely on point sensors, particularly acoustic-emission transducers and strain gauges. Because the number of measuring points is limited and their spatial distribution is discrete, the resulting monitoring signals are spatially sparse and discontinuous. It is therefore difficult to continuously capture the spatiotemporal evolution of the strain field during fracture propagation or to identify the corresponding response patterns throughout the fracturing process. Distributed fiber-optic sensing provides high-density spatial sampling, continuous measurement along the sensing fiber, and real-time response. These capabilities offer a new technical route for refined monitoring and mechanism-oriented interpretation in physical simulation experiments of hydraulic fracturing. Focusing on laboratory-scale physical modeling of hydraulic fracturing, this paper systematically reviews three categories of fiber-optic sensing technology: quasi-distributed fiber Bragg grating sensing (FBG), distributed strain sensing based on optical frequency-domain reflectometry (DSS-OFDR), and distributed acoustic sensing based on optical time-domain reflectometry (DAS-OTDR). For each category, the sensing mechanism, characteristic signal responses, and principal interpretation methods are summarized. Representative studies conducted in China and abroad are further compared with respect to their experimental systems and key observational indicators, thereby clarifying how different fiber-optic techniques have been incorporated into laboratory hydraulic-fracturing experiments. At the level of experimental methodology, typical fiber-deployment approaches and experimental paradigms under different specimen materials and loading conditions are reviewed. These include embedded fiber arrangements in transparent-medium visualization experiments, where fracture development can be observed directly and compared with the measured fiber-optic responses. The review also covers fiber anchoring and bonding in cement-based rock-like specimens under true-triaxial loading, together with equivalent multiwell deployment concepts designed to represent the spatial relationships among different wells in a controlled physical model. These experimental arrangements demonstrate how fiber-optic monitoring can be adapted to different materials, loading systems, and observation objectives. With further emphasis on advances in interpretation techniques, this paper summarizes methods for characterizing fracture geometry from offset-well strain monitoring and analyzes the characteristic fiber-optic strain responses associated with fracture evolution in true-triaxial physical models. It also outlines the identification of microseismic events from fiber-optic measurements and the application of these events to monitoring the dynamic hydraulic-fracturing process. The reviewed studies show that distributed fiber-optic monitoring is progressing from the recognition of basic fracture responses toward refined characterization of fracture geometry and interpretation of dynamic fracture evolution. Future research should strengthen experimental calibration and methodological standardization, promote the joint interpretation of strain and acoustic responses, and advance the quantitative inversion of fracture parameters. These developments will provide support for the design of physical simulation experiments, the interpretation of monitoring data, and the validation of related models.
Accurately monitoring the propagation of orthogonal fractures in shale reservoirs is of great significance for optimizing construction parameters and improving economic benefits. In response to the difficulty of evaluating the propagation of orthogonal fractures in on-site fiber-optic monitoring, a mechanical model for adjacent well fiberoptic monitoring orthogonal fracture propagation considering the tensile mechanical behavior during the fracture propagation process is established. Physical simulation experiments are conducted on horizontal well fracturing of shale specimens using fiberoptic monitoring. The mechanism of adjacent well fiber-optic monitoring orthogonal fractures is revealed, and the evolution characteristics of adjacent well fiber-optic strain induced by orthogonal fracture propagation are elucidated. A method for evaluating the propagation of orthogonal fractures based on adjacent well fiber-optic strain data was constructed. Research has shown that orthogonal fracture propagation induces five stages of strain enhancement stage, strain contraction convergence stage, strain linear stage, compressive strain contraction convergence stage, and strain stabilization stage in horizontal adjacent well optical fibers; orthogonal fractures induce three stages of strain enhancement stage, strain linear convergence stage, and strain divergence stage in vertical adjacent well optical fibers; the distance between the adjacent well optical fiber and the injection point determines the strength of the fiber-optic strain response signal. If the distance between the adjacent well optical fiber and the wellbore is too far, it will lead to the absence of the fiber-optic strain evolution stage. In physical simulation experiments, fiber-optic strain data can determine the number of hydraulic fractures and fractures initiation time. Competitive propagation phenomena may occur during orthogonal fracture propagation, and there is mutual influence between the vertical and horizontal sensing segments of the optical fiber. This study provides important reference for the effective monitoring and construction parameter optimization of orthogonal fractures in shale oil and gas reservoirs.
Shale hydration constitutes a critical mechanism governing wellbore instability during deep oil and gas drilling, yet its spatiotemporal evolution remains challenging to characterize with conventional monitoring techniques. This study introduces distributed optical fiber strain sensing based on optical frequency domain reflectometry (OFDR) to establish a continuous dynamic monitoring methodology for hydration-induced strain in shale. Laboratory-scale physical simulation experiments are conducted to quantitatively characterize the directional evolution of hydration strain. Results reveal that shale hydration-induced swelling exhibits distinct stage-dependent behavior: a rapid initial swelling rate followed by gradual stabilization, with inhibitive solutions significantly reducing both the magnitude and rate of swelling. Bedding inclination exerts a dominant control on hydration swelling, as preferential water migration along bedding planes causes the swelling strain perpendicular to bedding to prevail. A quantitative relationship between moisture content and hydration-induced strain is established by integrating fiber optic strain measurements with water uptake monitoring, revealing a logarithmic growth pattern that asymptotically approaches saturation. Furthermore, scanning electron microscopy observations demonstrate that hydration not only induces clay mineral swelling but also promotes the dissolution and detachment of minerals such as dolomite and potassium feldspar, facilitating the development of fractures and dissolution pores. These findings provide robust experimental evidence and technical support for elucidating hydration-induced wellbore instability mechanisms and optimizing engineering strategies for wellbore stability control.
Integrity management of oil and gas production systems is a core component for ensuring safe equipment operation, environmental compliance, and production continuity throughout the life cycle of oil and gas engineering. With the widespread adoption of digital technologies in the oil and gas industry, data volumes have increased, yet connections among heterogeneous data remain insufficient. Conventional artificial intelligence methods have therefore shown limitations in data integration, knowledge utilization, and adaptation to complex operating conditions. The development of a new generation of artificial intelligence technologies represented by large language models has provided new technical support for multimodal perception, semantic understanding, causal reasoning, and decision generation, while opening new pathways for the intelligent evolution of integrity management. Against this background, this paper focuses on large-model-driven transformation in integrity management. It examines the capability boundaries of conventional artificial intelligence methods in multimodal data integration, knowledge utilization, and few-shot generalization, and systematically reviews an intelligent management architecture driven by large models. A five-layer pyramid framework is used to organize the integrity management process, establishing corresponding relationships among data fusion and perception, intelligent identification and diagnosis, risk assessment and prediction, adaptive evaluation, and intelligent decision-making. The framework further clarifies information transfer across layers by linking data input with condition identification and transmitting risk analysis results to the evaluation and decision-making stages, thereby forming an information flow for integrity management. The paper also analyzes progress in cross-modal integration, causal modeling, and emergent capabilities, and discusses the influence of large models on integrity-management modeling from the perspectives of knowledge representation and reasoning and few-shot transfer. Based on representative cases from China and other countries, it summarizes the use of large models in safety monitoring, accident tracing, and report generation. It further reviews the application of retrieval-augmented generation, causal reasoning, and intelligent agents in scenarios such as compliance review and leakage monitoring, discusses the coordination of large models with domain knowledge and other model components, and analyzes the functional roles of different technical modules in data processing, condition analysis, and decision support, together with their integration into existing technical systems. Finally, the paper emphasizes human-machine collaboration and discusses development pathways in terms of feasibility, interpretability, and sustainability. Focusing on engineering issues including model hallucination, deployment resources, data security, multimodal adaptation, and standardization, it examines the constraints on model validation and decision execution when large models are introduced into oil and gas applications. The study provides a theoretical reference and practical basis for developing more reliable and efficient integrity management systems in the oil and gas industry.
Driven by the “dual carbon” strategy and the green, low-carbon transition of energy, China’s natural gas pipeline network has evolved into a one network nationwide infrastructure paradigm led by PipeChina. As the network topology becomes increasingly complex and control dimensions continuously expand, the traditional control mode relying on manual experience can no longer meet the demands for high-quality industrial development. Therefore, there is an urgent need to construct a new-generation intelligent control technology system for natural gas pipeline networks. This paper defines the core concept of intelligent control for natural gas pipeline networks and constructs a closed-loop technological framework encompassing the entire process of intelligent perception, intelligent cognition, intelligent decision-making, and intelligent execution. It systematically reviews the technological breakthroughs and engineering application achievements within these four major segments of China’s natural gas pipeline network, and conducts an in-depth analysis of the core bottlenecks in the current technological system. The functional positioning and logical correlations of the four core segments of intelligent control are clarified. The paper comprehensively summarizes a series of engineering achievements in China’s natural gas pipeline network across domains such as data foundation construction, intelligent forecasting and early warning, operational optimization and decision-making, and automatic closed-loop execution. Furthermore, it identifies key issues, including fragmentation of the technology system, the absence of a full-process intelligent closed loop, and insufficient capacity for independent and controllable development in core fields. Focusing on four major technical directions, this paper proposes an implementation path for a full-process, independently controllable intelligent control technology for natural gas pipeline networks. The research findings provide theoretical support and practical references for the intelligent upgrade of natural gas pipeline networks under the pattern of one network nationwide.
As the national pipeline system is gradually established and relatively open to society, natural gas from different shippers and sources can be blended in the pipeline network. To ensure the fairness of custody transfer metering, traditional volumetric metering should be superseded by calorific metering or energy measurement. This paper proposes an operational simulation method for multi-source gas pipeline networks with gas composition tracking, which can provide more accurate gas composition data for Class B and Class C metering systems that are not equipped with online gas chromatography. A transient mathematical model for multi-source gas pipeline networks was constructed, incorporating the momentum, energy, and composition continuity equations along with their corresponding initial and boundary conditions. The numerical simulation method was applied to track gas composition in the pipeline network over time and space, conducted in a time-level recurrence mode. To reduce the simulation’s computational capacity, the gas flow-state parameters to be solved at each discretized grid node are decoupled from the gas composition at each time step. The fundamental idea of decoupling is that the whole composition continuity, momentum, and energy equations were evaluated by the method of characteristics (MOC) coupled with the gas composition from the previous time level. Then, based on the flow-state parameters calculated by the MOC, the gas composition continuity equation was addressed by the finite volume method (FVM). Hence, the gas composition of each discretized grid node at the current time level is obtained. In order to avoid undershoot/overshoot and local oscillation phenomena in the time domain, the convective term of the composition continuity equation was discretized using a high-order bounded scheme, and the deferred correction approach was employed to incorporate it into source terms. The combined algorithm was applied to a branched network to verify its accuracy and feasibility. The results of the case study show that the maximum absolute deviation (MAD) in composition was 0.3% compared with TGNET.
Currently, China oil transmission pipelines are accelerating their transformation into a nationwide integrated network following the “X+1+X” framework. Empowered in depth by artificial intelligence technologies, pipeline operation regulation is presented with transformative opportunities, and relevant regulation technologies are evolving toward digitalization and intellectualization, serving as a core guarantee for safe and efficient energy transportation. To clarify the development context of pipeline regulation technologies, this paper systematically reviews the research progress and practical application performance of key regulation technologies for oil transmission pipelines. The covered core technologies include crude oil transportation technologies such as the transportation of easily condensable and highly viscous crude oil, optimal operation of crude oil pipelines, and frictional resistance prediction along pipelines, as well as core technologies for refined oil transportation consisting of batch scheduling formulation, oil interface tracking, mixed oil cutting and control. Auxiliary supporting technologies such as pipeline leakage detection and operating condition identification are also summarized. These technologies have effectively addressed the bottlenecks in highly viscous crude oil transportation, improved the delivery efficiency of refined oil products, and strengthened the safety guarantee of pipeline operation. On this basis, a full-cycle business loop and business architecture for intelligent regulation of crude oil pipelines are constructed, which rely on four vital links: perception, cognition, decision-making and execution. A closed-loop full-process workflow is realized, including real-time data perception, intelligent scheme generation, accurate operating condition identification and automatic decision-making & disposal. Finally, the prospect of digital and intelligent pipeline development is outlined, and three priority research directions are proposed. First, digital upgrading shall be promoted based on existing SCADA systems. Combined with the construction of intelligent pipelines and smart stations, a full-factor digital twin system will be established to realize comprehensive intelligent perception of pipeline operation status. Second, existing technical achievements will be integrated to build data-mechanism hybrid models and professional databases, and develop large-scale industrial operation models to support real-time prediction and early warning of operating parameters. Third, the intellectualization level of operating condition identification and leakage monitoring will be elevated to ensure stable transition of working conditions and rapid emergency response to leakages. This paper intends to provide reasonable suggestions and technical guidance for the advancement of key intelligent regulation technologies, and lay a solid foundation for the development of high-level intelligent regulation systems for oil transmission pipelines.
Against the backdrop of the strategic imperative for carbon peak and carbon neutrality, the global energy infrastructure is undergoing a comprehensive restructuring toward cleaner, low-carbon, efficient, and secure energy architectures. Hydrogen energy has emerged as a critical secondary energy vector, playing an increasingly vital role in facilitating the transition to sustainable energy systems. The integration of hydrogen into natural gas pipelines represents a technologically and economically viable pathway, allowing the repurposing of existing infrastructure for large-scale, cost-efficient hydrogen transportation. However, the permeation and interaction of hydrogen with pipeline steels can induce hydrogen embrittlement, a degradation mechanism that significantly compromises structural integrity and operational safety. A fundamental understanding of hydrogen embrittlement mechanisms, along with the development of effective mitigation strategies for pipeline materials in hydrogen-blended service environments, is therefore essential to ensure the reliability and safety of energy infrastructure throughout this transitional phase. This study systematically evaluates the hydrogen embrittlement behavior of X70 pipeline steel welds in hydrogen-blended natural gas systems by means of slow strain rate tensile tests and fatigue crack propagation experiments. The experimental matrix encompasses hydrogen blending ratios of 10%, 20%, and 100%, in conjunction with hydrogen charging durations of 12 hours, 24 hours, and 48 hours, to comprehensively assess their effects on mechanical properties, fracture mechanisms, and hydrogen embrittlement susceptibility in the weld region. Results reveal that under cyclic loading, the weld zone displays markedly higher susceptibility to hydrogen embrittlement relative to the base metal. A non-monotonic dependence of embrittlement sensitivity on hydrogen concentration is observed, with a critical threshold identified at 20% hydrogen blending ratio, where the hydrogen embrittlement coefficient attains a maximum value of 15.22%. The fracture morphology transitioned from typical ductile dimples to a quasi-cleavage mixed mode. This phenomenon is attributed to the irreversible segregation of hydrogen at defects such as grain boundaries. The segregated hydrogen reduces the local interatomic cohesion through the hydrogen-enhanced decohesion mechanism, thereby inducing brittle fracture and exacerbating the material’s embrittlement tendency. Furthermore, the study identified 24 hours as the most sensitive time node for the response of X70 pipeline to the hydrogen environment. This research reveals the performance degradation mechanism and micro-damage mechanism of X70 steel welds in hydrogen-blended environments, providing an important theoretical basis and experimental data support for the safe operation control, life assessment, and integrity management of hydrogen-blended natural gas pipelines.
The domain-specific large language models (LLMs) are recognized as a key technology for driving the intelligent transformation of industries. However, general-purpose LLMs, as their starting point, still face fundamental challenges in vertical scenarios with high-reliability requirements, such as insufficient depth of knowledge and the risk of hallucinations. To systematically evaluate the practical capabilities of general-purpose LLMs in typical high-risk industrial domains such as natural gas pipelines, and to provide a scientific basis for the subsequent development and deployment of specialized models, this paper constructs a standardized evaluation benchmark for the domain, named PipeMind-Bench. It covers 5 major capability dimensions and 36 fine-grained technical areas, comprising 1,580 questions with definitive answers. Through multiple prompt designs, it investigates the impact of contextual guidance on model performance. Based on this benchmark, we conducted a systematic evaluation of 16 mainstream domestic LLMs, yielding the following key findings: (1) Model type and parameter scale are not decisive factors for performance: Top open-source and closed-source models exhibit comparable overall performance, and reasoning-specialized models do not significantly outperform non-reasoning-specialized models. Some medium-scale models (e.g., 14 B, 32 B) achieve a good balance between accuracy, response efficiency, and deployment costs, representing a cost-effective choice for current domain applications. (2) Task capabilities are highly imbalanced: Models perform well on general tasks like “fundamentals of natural gas,” but their performance declines significantly in highly specialized subfields such as process design, equipment specifications, and safety standards. This finding provides empirical support for differentiated investment in data resources: reducing training data redundancy for foundational tasks while prioritizing the targeted supplementation of high-quality annotated data in weak specialized areas. (3) Structural shortcomings exist in reasoning and computational capabilities: Although question-answering capabilities remain relatively robust, models exhibit clear deficiencies in logical reasoning and numerical computation. Reasoning ability can be partially improved through domain-specific fine-tuning. However, constrained by the inherent weakness of the architecture in supporting numerical operations, computational capability is difficult to enhance effectively through conventional training. Thus, it is recommended that complex numerical tasks be delegated to dedicated computational modules in engineering practice. (4) Hallucination issues concentrate on three high-risk error types: Factual contradictions, fabrication of facts, and logical errors constitute the most common forms of hallucination in oil and gas pipeline tasks, posing serious threats to decision-making safety. Future training of domain-specific models should prioritize the construction of high-confidence datasets covering these risks, combined with mechanisms such as constrained generation, retrieval augmentation, or post-hoc verification, to achieve precise suppression of critical hallucination types. This study reveals that current general-purpose LLMs are still inadequate to meet the stringent demands for reliability and expertise in high-risk industrial scenarios like natural gas pipelines. Structured knowledge injection and task-decoupled design should become the core directions for the next phase of research and development. The proposed evaluation framework not only provides a reproducible benchmark for model selection, fine-tuning, and iteration in the industry but also offers methodological references for exploring trustworthy AI applications in other critical infrastructure domains.
As critical arteries for energy transmission, the structural integrity of natural gas pipeline welds is paramount to national energy security and public safety. Conventional non-destructive testing (NDT) methods, such as manual film interpretation or single-modality instrument inspection, often exhibit inherent limitations in feature extraction completeness and cross-modal information fusion reliability when dealing with the complex geometry and harsh service conditions of pipeline girth welds. These limitations hinder their effectiveness in addressing feature mismatches and false alignment-a phenomenon in multi-modal data fusion where non-correlated features are incorrectly matched in space due to registration errors-primarily caused by data source heterogeneity, high-intensity environmental noise, and complex microstructural backgrounds. To overcome these challenges, this research developed and validated an intelligent diagnostic system for natural gas pipeline weld quality based on deep learning. This paper proposes an innovative closed-loop processing architecture: “Perception-Alignment-Detection-Reconstruction-Enhancement-Fusion.” The architecture commences with an image quality perception module, which generates pixel-level noise interference distribution maps via local signal-to-noise ratio assessment to quantify input data reliability. Subsequently, a robust feature alignment module utilizes these maps to guide a confidence factor-modulated deformable convolution mechanism-an operation that adaptively learns sampling locations to enhance feature extraction capability-achieving precise spatial registration of multi-modal data within credible regions. The system further integrates mapping deviation detection and alignment path reconstruction modules, forming an internal error correction loop to dynamically rectify residual misalignments. Furthermore, a prior-driven enhancement module is introduced, embedding physical priors-such as heat-affected zone (HAZ) boundaries, characteristic defect morphologies, and material microstructures-into the deep learning framework to bolster feature representation in critical regions. Finally, a defect identification and fusion module consolidates all optimized multi-modal features through an adaptive weighting strategy, outputting definitive defect classification and localization results. Experimental results on an ASME standard-compliant test set demonstrate that our system achieved a defect identification accuracy of 97.6%. Compared to the industry-leading OmniScan X3 system, our system significantly reduced the incidence of misleading false alignment errors from 15.8% to 4.3%, representing a substantial reduction of 72.8%. Under stringent low signal-to-noise ratio conditions (SNR < 5 dB), the system maintained a high recall rate of 92.4%, attesting to its exceptional noise robustness. In field validation within actual pipeline construction projects, the system performed consistently, sustaining an identification accuracy above 92% while drastically reducing the time required per inspection compared to traditional manual methods, underscoring its significant engineering application value. The principal conclusion of this study is that the proposed intelligent diagnostic system, through its closed-loop, perception-driven, and prior-infused architecture, effectively surmounts the core bottlenecks of traditional methods in multi-modal weld quality assessment. It thereby provides a transferable theoretical model and engineering paradigm for the reliable deployment of industrial artificial intelligence in complex industrial settings.
To address the key challenges in fault diagnosis of underwater temperature-pressure sensors in deep-sea extreme environments—including low signal-to-noise ratio (SNR), inadequate extraction of spatiotemporally coupled features, and imbalanced fault sample distribution—this study proposes a Generative Data-augmented CNN-LSTM Network (GD-CLNet). First, a two-stage feature selection strategy combining Spearman’s rank correlation coefficient and Random Forest (RF) is employed to extract seven core discriminative features, such as temperature (T) and pressure (P), thereby optimizing the model’s input dimensionality. Second, the Deep Convolutional Generative Adversarial Network (DCGAN) is utilized to augment sparse fault samples, effectively mitigating the problem of imbalanced data categories. On this basis, theGD-CLNet synergistically leverages the advantages of CNN in extracting local spatial features and LSTM in modeling long-term temporal dependencies to achieve precise identification of sensor health states. Experimental results demonstrate that theGD-CLNet achieves an accuracy of 0.920 and an F1-score of 0.892 on the temperature dataset, and an accuracy of 0.889 and an F1-score of 0.912 on the pressure dataset. Its performance is significantly superior to traditional machine learning and single deep learning models, providing a high-precision and reliable technical solution for the condition monitoring and predictive maintenance of equipment in complex underwater environments.
Aiming at the problems of equipment wear and jamming risks existing in traditional isolation technologies (such as rubber isolation balls and isolation plugs) during sequential pipeline transportation of refined oil products, as well as the high pumping pressure difference and poor pipe diameter adaptability of traditional gel systems, this study aims to develop a new type of oil-based gel. By testing its shear dilatability, structural recovery and gel breaking efficiency, the isolation performance under complex working conditions can be improved. Reduce oil mixing loss. Using diesel as the base liquid, thickener and crosslinking agent were added successively to prepare oil-based gels of different concentrations. The rheological properties of the gel were determined by a rotational rheometer. The real working conditions were simulated through indoor and outdoor circulation pipeline experiments to evaluate the shear recovery, interface stability and isolation effect of the gel. And after the experiment, the gel-breaking efficiency of the gel-breaking agent and the influence of its residue were studied. Experiments show that the viscosity of oil-based gels increases significantly with the increase of concentration. Moreover, after being sheared by the centrifugal pump, it still maintains the rheological properties of the gel. The 2% concentration gel forms a wedge-shaped interface, and the isolation efficiency is significant. After the pump was stopped for 24 hours and then restarted, the gel viscosity could return to the initial level. The selected gel breaker can complete the gel breaking within 1 hour. After the gel breaking, the viscosity is basically the same as that of diesel, and there is no residual pollution. The new oil-based gel demonstrates excellent shear recovery rate and interface stability in pipelines of different diameters. It is suitable for complex pipeline start-up and shutdown as well as high shear conditions, and can significantly reduce oil mixing loss, providing a reliable technical solution for the efficient isolation and transportation of refined oil pipelines.
To address the significant increase in monoethylene glycol (MEG) loss and abnormal operation of the regeneration system during high-load gas production from an underground gas storage facility, this study investigates the main loss mechanisms and develops an integrated process optimization strategy based on field data, compositional analysis, and Aspen HYSYS simulations. A steady-state model of the surface gas-production process was established and validated using representative operating data, and HYSYS Dynamics was further used to evaluate transient MEG entrainment during load variations. The model reproduced the pressure and temperature responses of the actual process with acceptable accuracy, providing a basis for subsequent mechanism analysis and optimization. The results show that increasing the gas production rate from 400×104 m³/d to 560×104 m³/d deteriorates the separation performance of the low-temperature separator because of higher gas velocity and shorter liquid residence time. Under steady-state conditions, the gas-phase MEG entrainment rate increases from 12.48 kg/h to 17.47 kg/h, while during the transient ramp-up process it reaches a peak of 26.80 kg/h, 53.4% higher than the final high-load steady-state value. Meanwhile, enhanced interfacial disturbance and reduced residence time promote oil-glycol emulsification and liquid-phase entrainment, causing part of the glycol-rich liquid and condensate to enter the regeneration system. Compositional analysis indicates that the condensate contains relatively high fractions of heavy hydrocarbons and aromatics, which can aggravate fouling, carbon deposition, foaming, and vapor-liquid entrainment in the regeneration section. Based on these findings, a coordinated optimization strategy is proposed by controlling the air-cooler outlet temperature, adjusting the export pressure-regulating valve, and utilizing the available pipeline pressure potential to enhance Joule-Thomson cooling. When the low-temperature separator temperature is reduced from -5 °C to -10 °C, the recovered MEG flow rate increases from 34.66 kg/h to 35.17 kg/h. The predicted hydrate formation temperature under the current MEG injection condition is approximately -13.32 °C, leaving a safety margin of about 3.3 °C at -10 °C. Further optimization of the regeneration system, including hydrocarbon removal and operating-parameter adjustment, can reduce the adverse effects of heavy components and impurities. Economic evaluation shows that, although the reboiler duty increases from 215 kW to 248 kW, the optimized scheme can still provide a net benefit of approximately 446-626 CNY/d. The results demonstrate that abnormal MEG loss under high production rates is caused by the combined effects of deteriorated front-end separation, transient load disturbances, and regeneration-system contamination. The proposed coordinated optimization strategy can effectively reduce MEG loss while satisfying hydrate-prevention and downstream pressure constraints, thereby providing technical support for the safe, stable, and economical operation of underground gas storage facilities under high-load production conditions.
To investigate the mixing evolution during batch transportation of methanol in refined oil pipelines, a pilot-scale sequential transportation trial of methanol and gasoline was carried out. The experimental system was equipped with a transparent observation section for real-time visualization of the mixing interface morphology, along with online density meters to continuously monitor concentration variations within the mixed zone. The study systematically evaluated the effects of operational pipeline length, flow velocity, and transportation sequence on the development of methanol-gasoline mixing oil length. In addition, the results were compared with those from gasoline-diesel batch transportation under identical operating conditions to identify both common features and differences between the two liquid-pair systems.
Experimental results indicated that under a constant flow velocity, the mixing oil length increased with increasing operational length for both transportation sequences, namely methanol pushing gasoline and gasoline pushing methanol. However, the growth rate gradually declined as the distance extended, revealing a nonlinear trend and implying that the mixing zone expansion approaches a saturation state over long-distance transport. Under a fixed operational length, when the flow velocity was raised from 0.8 m/s to 1.5 m/s, the mixing oil length exhibited a general decreasing trend, demonstrating that higher flow velocities effectively suppress mixing zone development by enhancing turbulent shear and interfacial disturbance. Morphological observations showed that both gasoline-methanol and gasoline-diesel mixing oils presented uniform and transparent diffusion-mixing states without observable stratification or abrupt interface transitions, confirming that turbulent diffusion serves as the dominant mixing mechanism in both systems due to the good miscibility of each liquid pair. Quantitatively, however, the gasoline-methanol mixing oil was consistently shorter than the gasoline-diesel mixing oil under all tested conditions, which can be attributed to the lower viscosity contrast and faster molecular diffusion equilibration between methanol and gasoline.
A significant analogy was identified between the macroscopic diffusion behaviors of gasoline-methanol and gasoline-diesel mixing oils. In both cases, the mixing zone developed symmetrically along the pipeline axis, with concentration profiles exhibiting a gradual transition from the leading liquid to the trailing liquid, in agreement with classical turbulent diffusion models. Increasing the flow velocity shortened the mixing oil length over the same transportation distance for both systems, and the trend remained consistent across all velocity conditions examined. This behavioral analogy suggests that the existing well-established predictive models for gasoline-diesel mixing oil length can be adapted to gasoline-methanol systems by substituting the experimentally determined effective diffusion coefficient for the methanol-gasoline pair into the model framework. The findings of this study provide essential experimental data and theoretical guidance for the engineering design, operational optimization, and risk assessment of methanol transportation in multiproduct refined oil pipelines, thereby supporting the broader application of methanol as a clean alternative fuel within existing pipeline infrastructure.
For the ultra-deep tight gas reservoir in the Keshen 8 block of the Tarim Basin, the invasion of edge and bottom water into nanopores can lead to the formation of water films on pore walls, which further evolve into water bridges, blocking gas migration pathways and causing the “water sealing gas” problem. In this study, molecular dynamics simulations were conducted by constructing a methane-formation water-quartz nano-slit model to systematically investigate the formation process, equilibrium occurrence state, and the effects of pore size, pressure, and salinity on the critical water saturation and water film thickness, as well as the thickening and rupture processes of water bridges. The results show that, in a hydrophilic nano-confined environment, water adsorption and evolution undergo five typical stages: water film formation, water film thickening, water bridge formation, water bridge thickening, and water bridge rupture. With increasing pore size, both the critical water saturation and critical water film thickness corresponding to the transition from water film to water bridge increase significantly. Pressure has a limited effect on the critical conditions for water bridge formation, whereas salinity mainly increases the critical water saturation required for water bridge formation by altering ion hydration, interfacial water structure, and solid-liquid interactions, while exerting only a limited influence on the critical water film thickness. Pressure reduction can weaken water bridge stability and promote its rupture, and the unsealing pressure difference can serve as an important parameter for characterizing the difficulty of water sealing gas release. These findings reveal the formation and evolution mechanisms of water sealing gas in nanopores from a molecular-scale perspective, providing a theoretical basis for analyzing water sealing gas occurrence, understanding microscopic mechanisms, and improving gas recovery in the Keshen 8 block and similar tight gas reservoirs.
In the Well Pen-1 West sag within the central depression of the Junggar Basin, the third member of the Permian Fengcheng Formation (Feng-3 Member) hosts a deeply buried clastic reservoir system that represents a key target for hydrocarbon exploration and reserve enhancement in the region. This study integrates core observations, well logs, cast thin sections, scanning electron microscopy (SEM), and high-pressure mercury intrusion (HPMI) analyses to systematically compare the petrological characteristics, reservoir properties, pore types, and microscopic pore structures of sandstone-conglomerate and siltstone reservoirs in the Feng-3 Member. The aim is to clarify the mechanisms controlling reservoir quality differentiation among clastic reservoirs with different grain sizes. The results reveal that sandstone-conglomerate reservoirs were characterized by relatively high initial porosity. Compaction was a primary cause of pore loss, while some samples were further modified by intensive cementation. As a result, subsequent dissolution contributed only limited porosity enhancement, and the average present-day porosity is approximately 7.14%. In contrast, siltstone reservoirs had relatively lower initial porosity and experienced stronger compaction. However, owing to weaker cementation, they underwent intense organic-acid dissolution driven by hydrocarbon-generation overpressure during deep burial, resulting in the development of a well-connected secondary dissolution pore system. The average porosity increase resulting from dissolution reached 8.27%, and the present-day porosity commonly exceeds 10%, significantly higher than that of the contemporaneous sandstone-conglomerate reservoirs. Reservoir quality in the Fengcheng Formation Member 3 is jointly controlled by rock fabric and diagenetic processes. Variations in depositional hydrodynamics produced distinct grain sizes, matrix contents, and grain-support structures, which determined the initial pore architecture and subsequent diagenetic evolution pathways. Siltstone reservoirs evolved along a “weak cementation-strong dissolution” pathway, generating abundant secondary pore space and substantially improving reservoir quality, whereas sandstone-conglomerate reservoirs followed a “strong cementation-weak dissolution” pathway and experienced progressive reservoir tightening. These findings reveal the intrinsic mechanisms governing the differential evolution of deeply buried clastic reservoirs with different grain sizes, enhance current understanding of reservoir enhancement in fine-grained clastic rocks, and provide a theoretical basis for reservoir evaluation and hydrocarbon exploration in the Fengcheng Formation and other analogous deep tight clastic reservoirs.
The lithofacies assemblages within fluvial point-bar sand bodies are highly complex, and prolonged waterflooding induces pronounced dynamic changes in reservoir properties and heterogeneity. Taking the point-bar sand bodies of the Guantao Formation in the Gudao Oilfield as the study object, this study integrates core analysis, mercury intrusion porosimetry, nuclear magnetic resonance, and displacement experiments with geological modeling and numerical simulation to investigate lithofacies differentiation, time-dependent variations in petrophysical properties, and their effects on reservoir heterogeneity. The results show that the point-bar sand bodies can be subdivided into point-bar medium-sandstone, fine-sandstone, and siltstone facies, which differ markedly in grain size, pore-throat structure, porosity, and permeability. After waterflooding to 200 pore volumes (PV), the proportion of pore throats larger than 20 μm increased in the medium-sandstone facies. In the fine-sandstone facies, the proportions of pore throats larger than 10 μm and smaller than 0.1 μm both increased, whereas the proportion of pore throats no larger than 4 μm increased significantly in the siltstone facies. Correspondingly, the permeabilities of the medium- and fine-sandstone facies increased to approximately 1.42 and 1.30 times their initial values, respectively, whereas that of the siltstone facies decreased to approximately 0.86 times its initial value. These changes indicate contrasting time-dependent responses characterized by pore-throat enlargement and permeability enhancement in highly permeable facies, but pore-throat refinement and clogging in low-permeability facies. Under the combined effects of lateral-accretion-layer baffling and lithofacies-dependent property evolution, zones of high water throughput gradually expanded from the lower part of the point bar toward its middle and upper parts. The permeability contrast increased from approximately 6.4 to 7.3, the breakthrough coefficient decreased from approximately 2.69 to 2.53, and the coefficient of variation initially increased and subsequently declined slowly. Reservoir heterogeneity therefore underwent a staged evolution from rapid enhancement to stabilization at a relatively high level. Dynamic pore-throat restructuring and progressive permeability differentiation concentrated injected water increasingly within the high-permeability facies, reducing the displacement efficiency of the low-permeability facies and zones baffled by lateral-accretion layers. Consequently, the remaining oil was mainly enriched in the point-bar siltstone facies, weakly swept fine-sandstone facies, and lateral-accretion-layer-baffled zones. Compared with the model that neglected time-dependent petrophysical properties, the time-varying-property model reduced the mean absolute error and root mean square error of the water-cut history match by 33.33% and 30.09%, respectively. These findings provide a basis for remaining-oil prediction and development adjustment in fluvial point-bar reservoirs during the high-water-cut stage.
Subaqueous distributary channel systems in fan-delta front settings are characterized by non-stationary evolutionary dynamics. Their multi-stage migration and stacking significantly enhance reservoir heterogeneity, thereby increasing the uncertainty of reservoir prediction. To address this issue, this study utilizes a dense well pattern dataset from the upper member of the Jiufotang Formation in the Bao 1 block. Integrated core, logging, and seismic data are used to conduct a detailed single-sand-body architectural analysis, aiming to quantitatively characterize channel evolution and identify its controlling factors. The results indicate that: The study area develops four sedimentary microfacies, including subaqueous distributary channels, interdistributary bays, mouth bars, and distal sheet sands. Channel systems can be classified into three architectural styles: camalgamated, juxtaposed, and isolated patterns. Subaqueous distributary channels evolve from a concentrated stage to a bifurcation-dominated stage and further to a dispersed wandering stage. During this process, channel count increases from 23 to 36, the proportion of trunk channels decreases from 39.1% to 13.9%, while that of secondary channels increases from 60.9% to 86.1%. The bifurcation stage exhibits the highest width-to-thickness ratio of trunk channels, indicating enhanced lateral migration capacity. Channel evolution is jointly controlled by sustained transgression, reduced substrate gradient, and variable hydrodynamic energy; secondary channel width decreases from 64.6 m to 40.7 m, while the coefficient of variation of width-to-thickness ratio increases from 0.58 to 0.86 for the main channel and decreases from 0.49 to 0.30 for the secondary channel, reflecting differential hydrodynamic responses among channel hierarchies. Sand-body architecture evolves from stacked to laterally connected and isolated patterns, with a progressive reduction in connectivity, forming a retrogradational fan-delta architectural framework. Compared with conventional well-pattern datasets, the dense well pattern shows a significantly improved match between well spacing and architectural element scale, with S/W ratios of 0.75 for trunk channels and 2.31 for secondary channels, providing stronger constraints on channel boundaries and spatial distribution.
The intelligent prediction of lithology based on seismic data is of great significance for the exploration and development of oil and gas. Generative Adversarial Networks (GANs) have demonstrated strong nonlinear modeling capabilities and pattern transfer abilities in geological model prediction, but they still face some challenges in handling geological constraints and fine structures in terms of prediction accuracy, structural connectivity, and modeling efficiency. This paper proposes a lithology intelligent prediction method based on multi-scale feature fusion (MSFF) and parallel convolution (PC) of Generative Adversarial Networks. This method designs a multi-scale feature fusion architecture to adaptively extract seismic features, achieving intelligent prediction and uncertainty quantification of lithology, and introduces a parallel convolution module to capture multi-scale features. This study combines multi-scale feature fusion with a parallel resolution generation strategy to iteratively improve prediction reliability. Using the LTAF Formation of the South Sumatra Basin as an example, we verify the method’s robustness through 2D and 3D data tests and compare it with traditional methods such as Multiple-Point Statistics (MPS), Support Vector Machine (SVM), and Random Forest (RF). This method can rapidly achieve high-resolution prediction of lithology, reduces the multi-solution nature of the prediction results, and provides a guiding basis for the selection of potential oil and gas enrichment areas in the next step.
Metal-organic frameworks (MOFs) are a class of crystalline porous materials self-assembled from metal ions/clusters and organic linkers. They feature high specific surface areas, tunable pore sizes, ordered pore channels, and designable surface chemistries, and have been widely investigated in gas storage and separation, heterogeneous catalysis, energy conversion, and environmental remediation. With the increasing demand for deep oil and gas exploration, efficient exploitation of unconventional reservoirs, low-carbon petroleum refining and petrochemical processes, and CO2 geological storage, petroleum science and engineering requires new materials and model systems capable of regulating fluid occurrence, interfacial interactions, molecular recognition, and multiphase mass transfer at the nanopore scale. Owing to their highly designable structures and surface properties, MOFs offer new opportunities for interfacial regulation, multiphase mobility control, selective adsorption and separation, and the construction of controllable porous-medium models in complex petroleum systems. This review summarizes the structural characteristics, synthesis strategies, and representative functions of MOFs. Instead of following a purely application-sector-based classification, it focuses on common scientific problems in petroleum science and engineering, including interfacial regulation and multiphase mobility control, molecular recognition and selective removal of complex components, confined-channel separation and interfacial selectivity, and structurally controllable porous-medium models for petrophysics and CO2 geological storage. The key challenges are further discussed, including the stability of MOFs under harsh reservoir and refining conditions, scalable synthesis and shaping, compatibility at rock/fluid interfaces, and the insufficient understanding of structure-property-process relationships. This review aims to provide a reference for translating MOFs from functional material validation to mechanistic studies and application-oriented exploration in petroleum science and engineering, while promoting interdisciplinary integration among porous materials, chemical engineering, petrophysics, and low-carbon energy technologies.
Fracture-fracture wave-induced fluid flow (FF-WIFF) is a key mechanism controlling seismic dispersion, attenuation, and hydraulic connectivity in fluid-saturated fractured rocks. Existing analytical models for intersecting fractures commonly assume two equal-length fractures, for which the normal oscillation problem can be decoupled by using a common support and exchange symmetry. Natural fracture networks, however, commonly contain intersecting fractures with unequal lengths, so that the symmetric/antisymmetric decomposition used in equal-length models is no longer directly applicable. Here we develop a frequency-dependent rock-physics model for unequal-length intersecting fractures within the framework of dynamic Biot poroelasticity and dilute fracture scattering. The fracture-fracture exchange is driven by the difference between the area-averaged pressures on the two fractures. The model distinguishes the total conductance, the reference area-normalized connectivity, and the area-normalized exchange coefficients entering the two fracture-continuity equations. Because the two fractures are defined on different radial supports, the normal response is formulated as a double-domain block system, and the short-fracture response is incorporated into the long-fracture main system through a Schur-complement feedback term. In the equal-length limit, the model degenerates to the existing equal-length intersecting-fracture model. Numerical analyses show that unequal fracture lengths can reshape P-wave velocity and attenuation spectra, but their effects depend jointly on fracture support, actual exchange capacity, and normal-response feedback. Under the short-fracture truncation protocol, increasing the length ratio breaks the equal-length geometric symmetry but simultaneously reduces the short-fracture compliance, fracture-density contribution, and total conductance. This leads to an overall increase in low-to-intermediate-frequency velocity and a weakening of intermediate-frequency FF-WIFF-related attenuation. The area-averaged pressure difference, actual exchange flux, and Schur feedback strength exhibit different frequency dependences, indicating that the pressure-driving term is not equivalent to macroscopic dissipation. Increasing the reference area-normalized connectivity shifts the FF-WIFF response to higher frequencies, whereas increasing fluid viscosity shifts the corresponding attenuation increment and Schur-feedback peaks to lower frequencies. Unequal lengths can also break the pressure-symmetry cancellation occurring in the equal-length case and reactivate the fracture-fracture pressure contrast. These results provide a theoretical basis for joint seismic rock-physics characterization of fracture-length contrast, hydraulic connectivity, and fluid effects in tight sandstone, shale, and carbonate reservoirs.
CO2 fracturing has advantages in reducing fracture initiation pressure, enhancing thermally induced cracking, and promoting the formation of complex fracture networks, and is regarded as one of the most promising waterless stimulation technologies for hot dry rock reservoirs. Existing CO2 fracturing commonly uses high-purity CO2 as the working fluid; however, its capture, purification, liquefaction, and transportation costs are high, which increases reservoir stimulation investment and restricts large-scale application. To reduce fracturing-fluid costs, this study proposes a new concept of directly using N2-bearing impure CO2, obtained from simply purified industrial emissions from refineries and power plants, for hot dry rock fracturing. Based on the fluid properties of impure CO2, a thermo-hydro-mechanical-damage coupled numerical model for fracture propagation in hot dry rock under impure CO2 fracturing was established to investigate fracture initiation and propagation under different CO2/N2 ratios, in-situ stress differences, and injection rates. The results show that: ①the CO2/N2 ratio is a key factor affecting the interaction between the main fracture and natural weak planes. Under high CO2-ratio conditions, the fluid has stronger pressure transmission and heat transfer capacities, and the main fracture is more likely to penetrate natural weak planes and maintain continuous propagation; ②as the N2 ratio increases, fluid compressibility increases, pressure transmission efficiency and fracture-tip effective driving force decrease, and fractures tend to deflect along or be captured by natural weak planes, resulting in an overall reduction in damage area; ③increasing the injection rate can enhance fluid supply at the fracture tip, partly compensate for insufficient pressure transmission under high N2-ratio conditions, and promote fracture propagation; ④the in-situ stress difference mainly controls fracture propagation direction and damage extension scale. As the in-situ stress difference increases, fractures tend to propagate directionally, while lateral branching and weak-plane extension are inhibited. These results provide a basis for the utilization of industrial-source impure CO2 and parameter optimization in hot dry rock fracturing.
Hydraulic fracturing is critical to the effective stimulation and commercial development of shale reservoirs, requiring comprehensive consideration of fluid mechanics, rock mechanics, chemistry, and geology. The strong heterogeneity and anisotropy of shale rock fabric, defined as the geometric morphology and spatial distribution of mineral grains, cements, organic matter, pores, and fractures, fundamentally govern the initiation, propagation paths, and ultimate fracture network geometry of hydraulic fractures. This paper systematically reviews the characteristics of mineral composition and distribution, organic matter occurrence, pore types and structures, bedding planes, and natural fractures, and analyzes their influences on fracture height, network complexity, conductivity, breakdown pressure, and fluid leak-off, while also comparing the fabric differences between marine and continental shales. The review demonstrates that the control of shale fabric on hydraulic fracturing follows a cross-scale transfer chain of “multi-scale fabric to anisotropy to fracturing response,” and that the spatial matching between physical anisotropy, dominated by minerals, organic matter, and pores, and structural anisotropy, dominated by bedding planes and natural fractures, is the core determinant of fracture network complexity. Conventional brittleness index-based evaluation methods fail to effectively assess fracability because they overlook this matching relationship. Finally, this paper identifies key research gaps in cross-scale quantitative characterization, differentiated evaluation systems for continental shale, and full-cycle multi-field coupling mechanisms, and proposes the development of a multi-parameter comprehensive fracability criterion centered on anisotropy matching degree.
Accurate prediction of post-fracturing production is essential for optimizing hydraulic fracturing parameters and evaluating reservoir stimulation effectiveness. However, the limited availability of field samples in practical reservoirs often causes conventional data-driven models to suffer from overfitting and insufficient prediction reliability under complex reservoir conditions. To address this issue, this study proposes a post-fracturing production prediction framework integrating data augmentation and transfer learning to improve prediction accuracy and stability under small-sample conditions. A multi-source dataset is first constructed by comprehensively considering reservoir geological characteristics, engineering geological parameters, and fracturing operation conditions. To overcome the limitation of insufficient training samples, a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) is employed to learn the distribution characteristics of real samples and generate high-quality augmented data, thereby improving the diversity and representativeness of training samples. Subsequently, a hybrid feature selection method combining Maximum Relevance Minimum Redundancy (mRMR) and Pearson correlation analysis is developed to identify dominant controlling factors affecting post-fracturing production and reduce the interference of redundant information. Based on the selected key features, an attention-based Bidirectional Gated Recurrent Unit (Attention-BiGRU) model jointly driven by data and knowledge constraints is established. By integrating reservoir engineering knowledge into the deep learning process, the proposed model enhances the rationality and stability of prediction results. Furthermore, transfer learning is introduced to alleviate the influence of data distribution differences between different reservoirs and improve the generalization capability of the model under limited target samples. The results demonstrate that the proposed model achieves an R² value of 0.93 on the testing dataset, outperforming conventional machine learning models and individual deep learning models. Comparative experiments and ablation studies further verify that each module contributes to improving prediction performance, showing clear synergistic effects among the integrated components. The proposed method effectively addresses the challenge of inaccurate post-fracturing production prediction caused by insufficient samples and provides a reliable approach for reservoir productivity evaluation and oil and gas production analysis.
CO2 flooding, as a widely applied enhanced oil recovery technique, has been extensively used in many oilfields. However, solid deposition that occurs during the flooding process can inflict severe damage on reservoir physical properties. To thoroughly investigate this phenomenon, we conducted a series of physical simulation experiments that mimic actual reservoir conditions. Both inorganic ion precipitation and heavy component precipitation were generated in situ within the core samples. The influence of solid deposits on reservoir porosity and permeability was systematically characterized by measuring the changes before and after deposition. The plugging conditions in different pores were quantitatively evaluated using nuclear magnetic resonance (NMR) technology based on transverse relaxation time (T2) distribution that directly reflects pore size distribution and the reduction in T2 amplitude indicates the degree of pore blockage. Both inorganic ion precipitates and heavy organic precipitates can occlude pore throats, thereby reducing effective flow area and causing a marked decrease in both permeability and porosity. The pressure gradient across the core drives fluids to bypass blocked regions and enter smaller pores, which further complicates fluid flow and may induce additional trapping. An elevation in salinity accelerates inorganic ion precipitation rate, as higher ionic strength promotes nucleation and crystal growth, thus permeability and porosity decline faster under high salinity. Notably, pressure effect is not monotonic. When pressure increases, certain inorganic salts may redissolve, partially recovering lost permeability and porosity, although the recovery is often limited and depends on mineral composition. In contrast, heavy component precipitation behaves distinctly. Unlike inorganic salts, heavy components interact strongly with crude oil and are governed by different mechanisms such as pressure depletion and composition changes. Under identical pressure conditions, CO2 flooding generally achieves a higher oil recovery when heavy component precipitation occurs, primarily because heavy components exhibit greater solubility in the crude oil, facilitating their removal with produced fluids and reducing net damage. With the pressure continues to rise, the solubility of heavy components in CO2 saturated crude oil may change unfavorably. Heavy components precipitation can cause more severe plugging in both large and small pores, further diminishing permeability and porosity, even more pronounced than inorganic precipitation at elevated pressures. This study serves as a valuable reference for predicting and mitigating reservoir damage during CO2 flooding. In field applications, engineers can adjust ionic composition of injected water and injection pressure to minimize adverse impacts, thus effectively suppressing formation damage and ultimately enhancing oil recovery.
Against the backdrop of data factor circulation, the oil and gas industry is accelerating its transition from traditional decentralized operation models to multi-agent collaborative models. However, oil and gas data often involves sensitive information such as national resource security and industry secrets. The security governance for its cross-domain circulation has become a key bottleneck restricting the full realization of data value. Traditional security governance methods centered on single control domains and static authorization are no longer adequate, necessitating an urgent shift toward dynamic governance models featuring cross-domain collaboration and process controllability. In response, this study analyzes the characteristics of oil and gas data factor circulation, including multi-source heterogeneity, high sensitivity, and stringent regulations, along with the requirement for full-process traceability, based on typical oil and gas business scenarios. A security circulation model for oil and gas data factors is constructed, anchored in typical industry business chains, to balance data sharing efficiency with compliance and security requirements. Furthermore, a distributed trusted data space architecture for the oil and gas sector is proposed to empower each business entity with autonomy in data security governance. The data security functions of the proposed architecture are elaborated, including identity authentication, data usage control, and evidence-based auditing, along with the key supporting technologies required for its implementation. Finally, considering the evolution of technologies such as semantic privacy protection, named data networking, and zero-trust architectures, the development trends of secure oil and gas data factor circulation are forecasted, providing a reference for advancing research in this field.
High-value CO2 utilization technologies are emerging low-carbon energy technologies, which constitute an important part of China’s carbon neutrality technology system. Since they are still in the early stage of development, they face systematic challenges such as high technological progress uncertainties, low market acceptance, and high economic costs. This study reviews the opportunities and challenges of high-value CO2 utilization technologies from the perspectives of technological, economic, and industrial landscape. Results indicate that: (ⅰ) From a macro-level perspective, the rising demand of high-value CO2 utilization may surpass the growing trend of existing supply capacity. Methane and methanol are the main products of high-value CO2 utilization technologies. Over the past 15 years, China’s end-use demand for natural gas and methanol has increased 13-fold and fourfold, respectively. (ⅱ) From technology perspective, four mature technology paths include CO2 hydrogenation to methanol, thermo-catalytic production of methane, biomass-to-methane, and biogenic methane synthesis. Green methanol production pathways depend on a reliable supply of renewable electricity and coordinated electricity-hydrogen integration. Their costs remain substantially higher than those of conventional methanol production, and achieving economic and technical feasibility will require the large-scale deployment of low-cost green hydrogen within the energy system. (ⅲ) From the perspective of resources potential, only 30% of the provinces have both high-level potential for biomass and CO2. Considering the potential of biomass and CO2 from the perspective of the spatial heterogeneity is essential during the regional planning of coordinated high-value CO2 utilization development policies. (ⅳ) From the perspective of future challenges, the economic accounting system, data monitoring system, and engineering construction system face the non-negligible challenges. Specifically, they face the lack of full-chain economic assessments and multidimensional evaluation frameworks, difficulties in monitoring primary data, and high engineering complexity, respectively. The study puts forward three policy suggestions. Firstly, the government should put emphasis on the national macro-level mechanism design and multi-level policy support for the high-value CO2 utilization industry, while providing targeted assistance for relevant technologies based on their technological maturity, regional differences, and the characteristics of green chemical production. Secondly, the construction of a cross-disciplinary education system and breakthroughs in core technologies should be accelerated, while recognizing the role of small and medium-sized enterprises and improving the quality of university-industry collaboration. Thirdly, developing a scientifically grounded, long-term, phased development roadmap and conduct dynamic, forward-looking assessments for advanced technologies. This study provides implications for other early-stage technologies to overcome the bottlenecks through technological, economic and industrial innovation efforts.
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