University of Petroleum

This study examines the coupling coordination between energy consumption and carbon emissions in China’s agricultural sector and explores its implications for the low carbon transition. Using a coupling coordination model and provincial panel data from 2012 to 2021, the study assesses the level of energy carbon coordination in China’s agricultural sector and analyzes its key determinants. Fixed effects models evaluate the impact of terminal energy structure, while mediation analysis tests whether agricultural carbon emission intensity transmits these effects. Additionally, out of sample validation and short term scenario projections are used to examine temporal stability. The results show that energy carbon coordination in China’s agricultural sector is generally at a moderate to good level, indicating progress toward green transformation, although substantial regional disparities remain. Terminal energy structure is a key determinant, higher coal and diesel shares significantly reduce coordination, whereas a higher natural gas share improves it. Mediation analysis further shows that agricultural carbon emission intensity partly transmits these effects. Regional heterogeneity is observed, low GDP provinces are mainly constrained by coal dependence, while high GDP provinces face stronger diesel and electricity related emission pressures. The out of sample validation and short term scenario projections provide additional support for the temporal stability of these findings. These findings highlight the need for region specific decarbonization strategies in China’s agricultural sector, including accelerated clean energy substitution, power sector decarbonization, stronger carbon monitoring, and improved cross sector policy coordination.

A series of mononuclear-aromatic des-A-triterpenoids (MADATTS) and des-A-triterpenoids (DATTS) were simultaneously detected in terrestrial oils from the Baiyun Sag, Pearl River Mouth Basin, South China Sea. Besides five previously reported MADATTS, i.e., C23 mononuclear-aromatic des-oleanane (C23MADAO), C23 mononuclear-aromatic des-ursane (C23MADAU), C24MADAO, C24MADAU, and C26MADAU, this study identified a novel molecular marker in these oil samples, namely C26 mononuclear-aromatic des-oleanane (C26MADAO). C26MADAO elutes before C26MADAU in m/z 187 and 352 → 187 chromatograms, with a molecular ion at M+·352, base peak ion at m/z 187, and diagnostic ion fragment at m/z 199, 213, and 337. MADATTS with the same carbon number and identical substitutions on the B-ring exhibit strong positive correlations in their chromatographic peak abundance but show no evident correlation with the DATTS counterparts that share corresponding similar molecular skeletons, indicating that the formation mechanisms of the two series of compounds are different. In addition, the chromatographic peak abundance of both MADATTS and DATTS shows strong correlations with the C29 diasteranes/C29 regular steranes (C29-DiaSt/C29-St) and C30 rearranged oleananes/C30 hopane ((I + II + III)/C30H), which possibly reflects that their formation process is jointly controlled by terrestrial organic inputs and the catalysis of acidic clay minerals. Based on the sedimentary facies characteristics, conventional biomarker parameters, and distribution patterns of MADATTS and DATTS, this study finely reclassifies the Baiyun Sag oils into five groups and provides a comprehensive interpretation of their origins across different subregions.

Summary Accurate assessment of reservoir health is essential for efficient and sustainable oilfield development. In this study, we develop a data-driven approach to evaluate and classify health status in conventional sandstone reservoirs using machine learning and interpretable analysis. The categorical boosting (CatBoost) ensemble model predicts cumulative oil production based on 15 key development indicators, trained and validated on a data set comprising 42 real blocks and 158 simulated cases from a mature oil field. K-fold cross-validation yields high predictive accuracy [coefficient of determination (R2) = 0.964; root mean squared error (RMSE) = 0.3096]. Shapely additive explanations (SHAP) analysis quantifies feature importance, identifying remaining recoverable reserves per well as the dominant positive driver and water-cut increase rate as the least influential. An enhanced radar chart, integrated with a quantified risk coefficient, effectively visualizes reservoir health status and categorizes warning levels as green (safe), yellow (light warning), and red (heavy warning) in accordance with established industry standards for conventional sandstone reservoirs. The framework effectively detects anomalous development trends, enabling proactive adjustment of injection/production strategies. This interpretable data-driven framework supports rapid decision-making and near-real-time health assessment based on periodically updated field data.

The pore network system, defined by Pore Size Distribution (PSD), geometry, and connectivity, plays a crucial role in controlling the storage and transport behavior of shale gas. Despite extensive investigations of shale formations across various Indian basins, pore characterization studies in the Lesser Himalaya remain largely unexplored. In particular, the Neoproterozoic Krol shale from the northwestern Lesser Himalaya represents one of the untapped units with promising gas-bearing characteristics. In this study, shale samples from the Krol Formation were systematically analyzed for pore characteristics across nano- to macro-scales using a combination of Low-Pressure Gas Adsorption (LPGA) with N2 and CO2, Small-Angle X-ray Scattering (SAXS), Field Emission Scanning Electron Microscopy (FE-SEM), and Micro-Computed Tomography (Micro-CT). LPGA N2 adsorption exhibited Type IIB isotherms with H3 hysteresis loops, indicating slit- and wedge-shaped mesopores, while CO2 adsorption revealed Type I isotherms, reflecting micropore filling. PSD derived from LPGA showed bimodal and multimodal patterns using Barrett–Joyner–Halenda (BJH) and Density Functional Theory (DFT) models, respectively. SAXS, sensitive to both open and closed pores, effectively captured the PSD, and the Porod–Debye Scattering Profile (PDSP) model confirmed the dominance of mesopores. FE-SEM observations revealed irregular to elongated nanopores (37–54 nm) and a moderately complex pore structure, as supported by fractal dimensions derived from LPGA, SAXS, and FE-SEM. Micro-CT analysis further indicated the presence of macropores and fractures (≥103 nm), with a mean Feret diameter of 0.07 mm and a pore volume of 0.02 mm3. Porosity values estimated from SAXS, FE-SEM, and micro-CT were 4.98%, 5.64%, and 4.55%, respectively. This integrated multiscale characterization, bridging nano to macroscale pore systems, provides new insights into the storage and transport behavior of gas and emphasizes the Krol shale as a future shale gas formation in the Lesser Himalaya.

This study combines a systematic review (in accordance with PRISMA) and bibliometric mapping (VOSviewer & Bibliometrix) to explain how upstream gas portfolio decisions are optimized when fiscal rules limit annual cash flow. The Scopus 2005–2025 corpus of Title–Abstract–Keywords searches (1,149 records) confirm two pillars of the method: stochastic/robust optimization for portfolio sequencing and real options for flexibility in investment time and scale. Trend analysis, intellectual networks, and keyword evolution show the dominance of analytical-decision and reservoir planning themes; conversely, fiscal overlays such as withdrawal caps, cash-flow/budget constraints are still rarely formalized in mainstream models. The SLR synthesis shows that explicitly coding multi-period liquidity constraints materially changes project selection, sequencing, peak cash, and break-even timing compared to the baseline without constraints. The policy implication is a shift from hard caps to multi-period designed caps (with liquidity floors and countercyclical elements) integrated directly into the optimization framework. To balance value and fiscal stability, we recommend a gas-centric and modular portfolio that pairs ENPV with downside risk control objectives (e.g., CVaR/peak cash minimization). The main contribution of this study is to map fiscal gaps in the optimization literature and propose a research direction towards an integrated stochastic–real options framework with an overlay of fiscal rules that enables policy frontier reporting (value vs. stability).

Abstract Characterizing microscopic pore structures and analyzing oil-water two-phase flow behavior are critical for optimizing development strategies in carbonate reservoirs. This study constructed the digital core model using computed tomography (CT) scanning and threedimensional (3D) digital reconstruction technology, achieving precise characterization of pore structure features. Three connectivity models were established: pore-type (S1), fracture-poretype (S2), and fracture-type (S3), with further investigation into the effects of wetting angle (θ), capillary number (lgCa), and viscosity ratio (M) on oil-water flow simulation. Results indicated that the 3D pore structure model exhibited a surface porosity range of 4.48% to 11.21% and a fractal dimension of 2.21, reflected irregular pore geometry and high spatial complexity. The stress distribution demonstrated significant stress concentration phenomenon, where stress values at pore corners and terminations were approximately 1-2 orders of magnitude higher than those in the rock matrix. Flow velocity increased sharply at pore throat constrictions, with the most prominent velocities observed in preferential flow channels. Fractures in S2 and S3acted as high-permeability preferential pathways, both exhibiting superior connectivity compared to S1. The oil recovery efficiency for S1-S3 showed an inverse correlation with θ and M but a positive correlation with lgCa. Residual oil of S1 primarily existed as porous and corner-shaped forms, while S2 and S3 predominantly exhibited blind-end types. Pore structure was identified as the main factor causing differences in oil recovery efficiency. The contributions of the three factors to recovery efficiency, ranked from highest to lowest, followed the order: M > θ > lgCa.

The methane hydrate formation rate and storage capacity seriously restrict hydrate storage and transportation technology development. In this study, the methane hydrate formation process in a 0.30 wt % sodium dodecyl sulfate (SDS) and mixed systems of 0.30 wt % SDS with saturated sodium sulfate (Na2SO4) was investigated. The effects of SDS concentration and initial pressure in the mixed systems on the methane hydrate formation rate and amount were studied. It was found that the methane hydrate formation process can be divided into four stages: the first rapid formation stage, the nucleation aggregation stage, the second rapid formation stage, and the hydrate stable stage. The precipitated Na2SO4 crystals provided seeds for methane hydrate formation, which can improve the hydrate formation rate and storage capacity. The Na2SO4 crystals mainly affect the first rapid formation stage and the nucleation aggregation stage. The methane hydrate formation rate in the first stage under the synergistic effect of 0.30 wt % SDS+Na2SO4 is 1.2 times higher than that of SDS alone. When increasing the formation pressure from 6 to 10 MPa, the hydrate formation rate under the synergistic effect of 0.30 wt % SDS+Na2SO4 in the first stage increased by 8 times. In the SDS+saturated Na2SO4 system, the methane hydrate formation rate and storage capacity increase with the increase of SDS concentration. SDS concentration mainly affects the first rapid formation stage. This paper is helpful to propose a new method for the methane hydrate rapid formation and is crucial for the development of hydrate storage and transportation technology.