中国石化

The Beibuwan Basin is a Cenozoic extensional basin whose Leiqiong region hosts the youngest intraplate volcanic field in South China. Although a mantle plume has been proposed for this magmatism, its location and timing, along with the crustal melting condition, remain controversial. The tectono-thermal evolution of this basin, critical for understanding geodynamic processes and geothermal genesis, remains poorly constrained. Here we employ a multi-episodic finite extension model to quantify lithospheric stretching factors, strain rates, and thermal structure along three profiles and 19 wells. Results show total stretching factors ranging from 1.04 to 1.76, with peak strain rates during the Paleocene, middle Eocene, and Oligocene. Basement heat flow increased progressively from 60 to 62.5 mW/m2 at 65 Ma to 67-85.5 mW/m2 at 23.5 Ma, subsequently decreasing to 65-82 mW/m2 at present, indicating incomplete lithospheric thermal equilibration. Present-day Moho temperatures vary between 534 degrees C and 735 degrees C, with average Curie interface depth and thermal lithospheric thickness of 22 and 78 km, respectively. Mean present-day mantle heat flow is 45 mW/m2, constituting 69% of the terrestrial heat flow. Model results indicate that Paleogene extension greatly enhanced mantle heat flow and temperatures, promoting potential crustal melting at 25-35 km depth. Limited anomalous post-rift subsidence, accompanied by localized uplift during the early Miocene and Quaternary, indicates the presence of hot mantle upwelling that provided an additional heat source. These findings offer geothermal evidence that the interplay between lithospheric extension and hot mantle upwelling drove the late Cenozoic magmatism in the Leiqiong area.

The aerobic oxidation of HMF to FDCA offers a promising pathway for biomass utilization, especially for producing the biobased polymer PEF. Herein, a series of Mn2O3 with various specific surface areas were prepared via a facile two-step process. The catalytic results demonstrated that Mn2O3 exhibited a higher intrinsic activity (3.3 mg(FDCA)& centerdot;m(-2)& centerdot;h(-1)) than common manganese oxides, such as Mn3O4 and different crystalline phases of MnO2 (alpha, beta, and delta). Under optimized conditions, the Mn2O3-140-A catalyst achieved an FDCA selectivity of 91.3% at similar to 100% HMF conversion, with a carbon balance of 97.8%. Combined characterization and catalytic testing revealed a clear positive linear correlation between specific surface area and activity, attributed to the increased concentration of accessible active sites associated with oxygen vacancies. Kinetic analysis identified the slowest steps in the two reaction pathways (DFF path and HMFCA path) and their dependence on reaction temperature. These findings demonstrate that Mn2O3 can act as a promising catalyst for HMF aerobic oxidation to FDCA, and provide valuable insights for the design of advanced Mn2O3 catalysts.

0 INTRODUCTION Shale gas reservoirs possess considerable resource potential and economic viability,making it attractive tar-gets for development (Zou et al.,2023;Hughes,2013). In the past 15 years,China has achieved major break-throughs in marine shale gas exploration and develop-ment within the Middle-Upper Yangtze Platform. Five shale gas fields with proven reserves exceeding 100 bil-lion cubic meters,including Fuling,Weiyuan,Changning,Weirong and Qijiang,have been proven in the Sichuan Basin (Guo et al.,2016). Among these shale gas field,Fuling shale gas field is the first large-scale shale gas field in China,with its cumulative production exceeded 66.5 billion cubic meters by June 2025. The exploration and discovery of these large-scale shale gas fields confirm the immense resource potential of marine shale gas in China.

Paired electrochemical valorization reactions (EVRs) offer a sustainable route for co-producing value-added chemicals, yet their efficiency is hindered by overlapping potential windows with water electrolysis reactions (WER). Here, an asymmetric CuOx@Ag electrocatalyst platform is proposed that decouples both anodic and cathodic EVRs from WER via Ag-mediated oxophilicity modulation. Atomic-level Ag incorporation suppresses *OH/H2O adsorption and widens the potential windows to 422 mV (anode) and 502 mV (cathode) in a glycerol oxidation | 4-nitrophenol reduction pair. The system achieves > 90% Faradaic efficiency and > 70 mmol h(-1) cm(-2) productivity. In situ and DFT analyses reveal that Ag-induced weakening of Cu-O interactions underlies the WER suppression mechanism. Techno-economic and life-cycle evaluations indicate 31.6% cost reduction and 57.4% lower CO2 emissions versus thermochemical routes. The CuOx@Ag platform thus provides a scalable and general strategy for low-carbon electrosynthesis across diverse redox systems.

China targets carbon peak by 2030 and neutrality by 2060. Catalytic CO2 hydrogenation to methanol using renewable H2 is a promising route for carbon recycling and emission reduction. However, developing efficient catalysts is hindered by complex structure-performance relationships. This study addresses this by constructing a dataset (112 samples and 28 features: composition, processing, and properties) and applying machine learning to predict Cu-based catalyst performance. Six models-random forest, XGBoost, LightGBM, gradient boosting, SVR, and DNN-were compared for predicting CO2 conversion, CH3OH selectivity, and yield. Optimal models varied as follows: SVR best predicted CO2 conversion (test R 2 = 0.245), XGBoost excelled for CH3OH selectivity (test R 2 = 0.922 and MSE = 46.508), whereas CH3OH yield prediction was poor (LightGBM best and R 2 = 0.006). SHAP analysis revealed key nonlinear feature contributions (e.g., Cu content, GHSV, and temperature). Focusing on CH3OH selectivity, XGBoost was optimized as follows: multialgorithm voting and stepwise elimination identified 12 key features (e.g., Zn salt type, In content, and drying time). Bayesian hyperparameter tuning boosted performance (test R 2 = 0.9352). SHAP provided interpretability and design guidance. Bootstrap resampling validated reliability (95% CIs). An online prediction platform enhanced screening efficiency. Despite strong test performance, cross-validation R 2 (0.7473) indicates a need for larger datasets. This work provides a robust data-driven framework for optimizing CO2-to-methanol catalysts, demonstrating ML's potential in catalysis research.

During multi-layer commingled production of coalbed methane (CBM), fluid interference induced by interlayer pressure differences is a major constraint on productivity, representing a dynamic coupling process of reservoir pressure, temperature, and deformation. To elucidate this mechanism, we constructed a four-layer superimposed reservoir physical model using a self-developed large-scale true triaxial multi-field coupling test system, which reflects the geological conditions of the Eastern Yunnan and Western Guizhou region. We precisely regulated interlayer pressure differences and monitoring multi-physical parameters in real time to analyze the dynamic evolution of reservoir temperature, pressure, and deformation fields. The findings reveal that: (1) Increased interlayer pressure difference intensifies fluid interference in low-pressure reservoirs, causing abnormal pressure buildup. For example, when the pressure difference rose from 0.2 MPa to 0.6 MPa, the maximum pressure increase in Reservoir I grew from 1.03 MPa to 1.13 MPa. (2) The high-pressure reservoir (Reservoir IV) remained largely unaffected throughout production, with its temperature decline rate consistently correlated positively with pressure difference, indicating a distinct response behavior. (3) Reservoir deformation correlates positively with initial pressure. When the initial pressure of Reservoir II increased from 1.2 MPa to 1.6 MPa, its volumetric strain rose from 1.81 parts per thousand to 2.21 parts per thousand, attributable to the combined effects of matrix shrinkage, elevated effective stress, and desorption-induced thermal cooling. This study demonstrates how interlayer pressure differences regulate the coupled evolution of reservoir pressure, temperature, and deformation, providing experimental evidence and theoretical support for identifying interference mechanisms and optimizing development strategies in CBM commingled production.

Data-domain least-squares reverse time migration is an imaging method based on optimization theory that performs iterative optimization. In recent years, many researchers have continuously optimized the least-squares reverse time migration method, with the hybrid conjugate gradient method being the mainstream optimization algorithm for solving least-squares reverse time migration. However, the least-squares reverse time migration based on this method still suffers from issues such as slow convergence speed, leading to poor stability throughout the computational process. To address the above problems, this paper proposes a method that uses the Gauss-Newton direction as a guide and employs a weighted combination of dual conjugate parameters to improve the convergence stability of least-squares reverse time migration. The improved method dynamically weights and combines two gradient parameters, leveraging the fast convergence of the quasi-Newton algorithm and the stable convergence of the hybrid conjugate gradient method. Through testing on models and real data, compared with conventional methods, the proposed algorithm achieves better imaging results under the same number of iterations, while also demonstrating good stability and high computational efficiency.

As a classical method to solve the ill-posed problem in tomographic inversion, model regularization effectively enhances inversion stability and adaptability to complex geological structures by incorporating prior information. Focusing on depth-domain tomographic velocity modeling, this paper systematically compares the differences in the application between Tikhonov regularization and preconditioned regularization, optimizes the model regularization method suitable for depth-domain model building, and further proposes a preconditioned model regularization algorithm based on the structure tensor. First, the structure tensor is utilized to extract structural prior information, which is then combined with the preconditioned regularization to construct a prior constraint term. Subsequently, the conjugate gradient (CG) method is employed to efficiently solve the tomographic matrix equations. Synthetic data tests demonstrate that the proposed method can accurately locate geological boundaries and fault positions in complex geological models, and precisely invert velocity anomalies in structural zones. In field data applications, the accuracy of the velocity model inverted by this method is significantly improved, leading to a remarkable enhancement in imaging quality for complex structural areas. These results indicate that the structure tensor-guided preconditioned regularization method possesses both high resolution and strong robustness, thereby providing more reliable technical support for depth-domain tomographic velocity model building in complex structural regions.

Aviation risk analysis can be a useful empirical foundation using narrative incident reports gathered by the Aviation Safety Reporting System (ASRS), but due to its long-form format, class imbalance, and domain-specific semantics, automated modelling can be a challenging problem. To respond to these challenges, this study develops a domain-adapted deep learning model built upon the Robustly Optimized Bidirectional Encoder Representations from Transformers pretraining approach (RoBERTa) for multi-label identification of contributing factors in aviation safety reports. The proposed model improves multi-label classification performance by integrating four modules: instruction-based large language models (LLMs) data augmentation to reduce imbalance, a merging module to jointly model the narrative text and metadata, a composite loss to strengthen robustness in case of label imbalance, and domain adaptive pretraining on corpora. The experimental results indicate that the model achieves reliable improvements, while ablation experiments further clarify impact of each module. Based on the predicted contributing factors, an N-K model is constructed to quantify interaction strength, and a Bayesian network is used to model directed risk propagation. By accounting for both structural coupling and propagation probability, the framework identifies and ranks risk pathways that correspond to plausible accident developments. A case study demonstrates that the proposed approach can extract high-order, multi-domain propagation paths from narrative data, enabling structured interpretation of plausible accident evolution patterns. Taken together, the proposed framework provides a pipeline that converts incident narratives into actionable safety information, offering a scalable and structured basis for proactive aviation risk analysis.

Seismic full-waveform inversion (FWI) stands as a cornerstone technique in subsurface imaging, offering unparalleled insights into subsurface structures. However, to nonlinearity and ill-posedness, particularly in handling inaccurate initial models and uncertainty of observations. To address these issues, a novel approach termed multiscale neural decoding and weighting for seismic FWI (MNDW-FWI) was proposed. Our method leverages a multiscale decoding neural network to effectively reparameterize velocity models and capture diverse scales of subsurface features. Furthermore, a flexible weighting mechanism was introduced to assign distinct weights to individual branches of the decoder networks, thereby enabling tailored emphasis on different scales of the velocity models. The integration of these advancements into a recurrent neural network-based FWI (RNN-FWI) framework yields significant improvements in accuracy and robustness. By incorporating the MNDW strategy and automatic differentiation (AD) technique, our approach offers superior performance in capturing intricate subsurface structures from an inaccurate initial model and mitigating the effects of the presence of noise and the absence of low-frequency components in seismic records. Through comprehensive experimental evaluations on synthetic examples with noisy and limited bandwidth observations, the efficacy of our proposed method in enhancing the accuracy and flexibility of inversion was demonstrated.

In this study, a self-healing lignin-based polyacrylamide/polyvinyl alcohol (SL-cPAM/PVA) hydrogel featuring excellent high temperature and salt resistance was successfully synthesized via a two-step method. Sodium lignosulfonate (S-Lignin) was first incorporated into a chemically crosslinked polyacrylamide (cPAM) network, followed by the formation of dynamic boronate ester linkages among polyvinyl alcohol (PVA), catechol groups, and borax to construct a dual crosslinked structure. The rheological, thermal, and mechanical properties of the hydrogel were comprehensively characterized. The results indicate that the introduction of S-Lignin improved the initial decomposition temperature from 150 degrees C to 178 degrees C, and enabled the hydrogel to achieve self-healing behavior at 130 degrees C, supported by reversible borate ester bonds, hydrogen bonding, and ionic interactions. The hydrogel maintained structural integrity in high-salinity environments (up to 21 x 104 mg/L) and demonstrated remarkable recovery in mechanical strength and microstructure after damage. Even after 30 days of thermal aging at 130 degrees C, brine-healed hydrogel remained intact without signs of hydrolysis. These findings indicate the excellent potential of SL-cPAM/PVA hydrogel as a water-plugging material in high-temperature, high-salinity oilfield environments, providing a sustainable and functional approach for enhanced oil recovery.

To address the issues of low experimental pressure and the difficulty in accurately reflecting reservoir conditions in studies on the stress sensitivity of shale fracture network reservoirs, this study employed a whole-process simulation experiment method from low pressure to actual reservoir stress to systematically evaluate the stress sensitivity of shale fracture network reservoirs and quantify its impact on fracture seepage through theoretical analysis. The stress sensitivity curve of shale fracture networks exhibited four-stage characteristics, which became more pronounced as permeability decreased. The overall permeability retention rate increased with the increase of fracture core permeability, reaching a maximum of 25.46%. Shale fracture network cores under compression underwent four stress response stages successively: plastic deformation, pseudo-plastic deformation, elastic deformation, and rigid deformation. The most significant reduction in fracture core permeability occurred in the first stage, with a maximum decrease of 73.63%. The fracture cores exhibited a stress hysteresis effect. Cores with higher fracture permeability demonstrated greater compressibility and required higher initial net stress to enter the same stress stage.Under actual reservoir stress conditions, most cores were in the elastic deformation stage, while a small portion entered the rigid deformation stage during later development phases. The actual reservoir stress sensitivity curves could be divided into two types: "L"-shaped type and quasi-linear type. Under actual reservoir conditions, the permeability loss of shale fracture cores was generally less than 25%, indicating weak stress sensitivity. In actual production, under low pressure difference conditions, the impact of stress sensitivity on flow rate was less than 5% and could be considered negligible. However, under high pressure difference production, its degree of impact exceeded 10%, with a peak of 17.59%. If stress sensitivity was neglected in the productivity evaluation of shale fracture network reservoirs, the results would be significantly overestimated. These findings provide a technical basis for the efficient development of unconventional oil and gas resources.

The seismic resilience of liquefied natural gas (LNG) storage tanks is critical for public safety and economic stability. This study accesses the safety of a 30,000 m3 double-steel-wall full-containment LNG storage tank under seismic and leakage conditions, employing the two-way fluid–structure interaction and added mass methods to analyze the seismic time history response and evaluate the tank's seismic load resistance throughout its lifespan. The results showed that sloshing height, stress, and displacement responses of the storage tank remained within safe limits under seismic loading. The fluid's impact on the inner tank wall was most significant at the bottom, where axial stress increased the most. Under the combined effects of leakage and aftershock, hydrodynamic pressure notably affected the middle and lower sections of the outer tank wall, increasing the displacement gradient along the tank's height and raising the risk of elephant's foot buckling at around 1/10 of the wall height. After leakage from the inner tank, hydrostatic pressure from the leaking liquid caused a substantial increase in stress, especially in the circumferential direction.

Seismically active faults may control fluid flow while affecting regional geohazards. Here, we analyzed fluid migration processes along a large fault in a carbonate and evaporite sequence of the western Sichuan basin, adjacent to the Mw 7.9 Wenchuan earthquake. This study focuses on the architecture of the Pengxian fault (PXF), which is a dominant blind thrust at the frontal zone between the Sichuan basin and the Longmen Shan range. The PXF is ~55 km long with a damage zone thicker than 1 km, which apparently affected the regional migration of hydrocarbons. For the analysis, we used 3D seismic survey data, geochemical data, and core samples from a gas reservoir in the western Sichuan basin. The results indicate: (1) the PXF damage zone is separated into two vertical mechanical blocks by a ductile detachment zone of Triassic anhydrite; (2) bitumen occurrences and carbon isotope analyses indicate that gas and fluid migration across the anhydrite of this detachment zone occurred along the PXF damage zone; (3) the PXF damage zone served as a preferential conduit for subsurface fluid flow and acted as a fault valve in a seismic cycle. We anticipate that fluid communication along fault damage zones could lead to fault weakening and associated hazards in seismically active regions.

Low-permeability, water-sensitive extraheavy oil reservoirs with abundant reserves in China remain unapproved for commercial development using thermal recovery or other existing technologies due to their carbon emission problems and/or techno-economic limitations. This study proposes injecting microemulsions via fracturing-flooding to unlock the production potential of these challenging reservoirs. Alkyl polyglucoside and sodium dodecyl diphenyl ether disulfonate were used to formulate multifunctional microemulsions specifically tailored for harsh reservoir conditions. High-rate/high-pressure fracturing-flooding technology was used to establish significant pressure gradients for enhanced oil displacement. Experimental results demonstrated that the bicontinuous microemulsions effectively cleaned the oil sands and inhibited clay swelling, thereby improving permeability. The key mechanism for improving extraheavy oil mobility involved two sequential steps: (1) the oil phase of the microemulsions reduced the extraheavy oil viscosity to below 5,000 mPa & centerdot;s through dilution, followed by (2) the surfactant components subsequently emulsifying the diluted crude oil into oil-in-water droplets with viscosity <500 mPa & centerdot;s. Coreflooding tests revealed that moderately elevated injection rates enhanced pore-throat connectivity and achieved higher oil recovery, whereas excessive rates reduced chemical retention time, leading to a sharp decline in ultimate oil recovery. A development strategy centred on microemulsion-assisted fracturing-flooding, comprising four key steps, was implemented in a marginal heavy oil reservoir of Sinopec Shengli Oil Field, achieving a multifold increase in crude oil production and being assessed as economically viable at oil prices above 48 USD/bbl. This milestone achievement represents a groundbreaking advancement in the development of marginal heavy oil resources under escalating carbon emission regulations.

Large-scale CO2 Enhanced Oil Recovery and Storage (CO2-EORS) constitutes a cornerstone of global energy transition and subsurface carbon management strategies. However, high-fidelity simulations are essential but currently hindered by the complexity of rigorous three-phase equilibrium. Existing algorithms fail to balance speed and accuracy, thereby restricting large-scale industrial application. This study introduces an efficient “Stem-Branch” computational framework designed to optimize the computational workflow, thereby balancing calculation efficiency and thermodynamic accuracy. The architecture prioritizes a high-efficiency “Stem” path for standard cases where the aqueous phase is readily separated, while an adaptive “Branch” path activates to ensure robustness under challenging thermodynamic conditions. Additionally, a scaled trust region method is integrated to mitigate Hessian ill-conditioning, stabilizing convergence. The framework undergoes a comprehensive multi-scale validation, bridging the gap between static analysis and dynamic engineering applications. Following rigorous standalone phase-equilibrium testing, the algorithm is embedded into a reservoir simulator to assess its performance in complex flow fields. Through 2D CO2-EOR and 3D carbon storage scenarios, the simulation results demonstrate the model’s exceptional capability in capturing dynamic phase behavior and flow characteristics with high precision. Benchmarking confirms that the method maintains thermodynamic accuracy while significantly enhancing performance—improving phase-equilibrium efficiency by 84.12% and 77.14%, and reducing total simulation time by 58.33% and 47.06% in the 2D and 3D scenarios, respectively. Consequently, this work elevates the standard for multiphase compositional simulation, providing a robust, high-efficiency tool that supports both academic research and practical engineering applications in sustainable energy development.

The seepage mechanism of fishbone wells is complex and it is difficult to accurately predict productvity. Most of the existing productivity prediction models are based on single-phase seepage theory and have limited applicable conditions. In order to accurately predict fishbone well productivity under oil-water two phase seepaging conditions, this paper first deduces the calculation method of oil-water distribution in the near-wellbore region of fishbone wells based on the tubing method combined with the micro-unit idea and the principle of potential superposition. Then, the mathematical models of reservoir seepage and wellbore flow are established. By considering the varying mass flow in the wellbore and the interference between different producing segments of branched wells, semi-analytical models for coupling reservoir seepage and wellbore flow are constructed to predict oil-water two phase productivity of fishbone wells. The calculation results of the established model are compared with numerical simulation results using field parameters from an actual block, and the error is within 10%.

Seismic PS images can complement conventional PP images, especially where PP images cannot effectively illuminate, e.g., below gas pockets. However, building reliable Vs migration velocity is crucial to achieve high-quality PS images, which is not trivial. Here we propose a raytracing-based PS tomography workflow to produce Vs velocity models with subsurface common-offset gathers. First, a double difference objective function is constructed due to the unknown target depths. Second, the raytracing process handles negative and positive offset gathers respectively because of the asymmetrical nature of PS rays. Third, after the PS gathers are flattened, we propose a depth-varied cross-correlation method with similarity scores to automatically pick depth shifts between PP and PS images, which are then used to invert for residual Vs velocity errors. Numerical examples show large heterogeneous Vs velocity errors can be solved for PS migration.

Optimizing post-fracturing shut-in and flowback is critical for maximizing productivity and minimizing sand production in shale gas wells. Current practices, however, often rely on generalized, static guidelines, lacking the adaptability to address well-specific heterogeneity and dynamic downhole conditions. In this paper, we present a data-driven advisory workflow that integrates machine learning (ML) with physics-based control charts to guide flowback operations in real time. The methodology consists of three core components: First, a geological analysis identifies key reservoir characteristics (e.g., fault proximity and clay content) influencing optimal shut-in duration; second, an extreme gradient boosting (XGBoost) model predicts downhole flowing pressure from readily available surface data with an error of less than 1%; third, these predictions enable the calculation of two novel, normalized parameters (in-perforation net pressure and apparent single-perforation velocity), which are plotted against field-calibrated safe operating envelope (SOE) charts. The system advises choke adjustments to maintain the well’s operating trajectory within a “reasonable zone” on these charts, thereby balancing production acceleration with sand control. Field validation in the Dingshan block demonstrated the workflow’s efficacy: In a controlled test, a well following the advisory system’s recommendation successfully increased rate without sand production, while an offset well that deviated from the advice experienced immediate and severe sanding. This work provides a practical and scalable framework for derisking flowback operations through real-time, data-informed guidance, moving beyond rigid, one-size-fits-all protocols.

The flow mechanism for the elastic development of shale oil is complex， and the fluid distribution and mobilization mechanism during hydraulic fracturing， shut-in， and development stages are unclear. It is difficult to evaluate the contribution of different energy sources to oil production， and the influence of pore structure and imbibition is unclear. Therefore， the online nuclear magnetic resonance-assisted core experimental method for the entire elastic development process of shale oil was improved， revealing the flow mechanism for elastic development of cores， imbibition mechanisms， and the influence of pore structure. Moreover， an evaluation method for the contribution rate to oil production was established to quantify the contribution rate of different energy sources to oil production. Research has shown that ① fracturing fluid is mainly distributed in large pores， after hydraulic fracturing injection volumes are large， and the artificial energy replenishment is sufficient in fractured and laminated cores. ② During the shut-in stage， the synergistic effect of residual pressure difference and capillary force promotes the fracturing fluid in large pores to replace shale oil in small pores， increasing the degree of shale oil mobilization， and the imbibition efficiency is positively correlated with the development of laminae and fractures. ③ During the development stage， multi-scale pores and fractures are gradually activated， with the fracturing fluid in the bedding fractures and large pores first produced， resulting in high water cut； as small pores gradually supply liquid， the water cut decreases and tends to stabilize. In addition， the contribution rate of submicron-micron pores in laminated cores to oil production exceeds 60%， and the characteristic of a high contribution rate of large pores to oil production is obvious. ④ The final recovery of different lithofacies cores is 8.7%-14.7%， and the contribution rates of rocks and fracturing fluids to oil production are 43.7%-54.9% and 23.0%-30.0%， respectively. The fractures and laminated structures improve the recovery and contribution rate of fracturing fluid to oil production.