Search Results (6,309)

Accurate quantification of mineralogical composition in carbonate rocks is essential for reservoir characterization in the oil industry, directly influencing petrophysical properties such as porosity and permeability. However, traditional methods such as X-ray diffraction (XRD) are destructive and provide limited spatial sampling. The aim of this study was to develop and validate a workflow for the continuous quantification of calcite and dolomite in drill cores from the Brazilian pre-salt oil province by integrating short-wave infrared (SWIR) hyperspectral imaging (HSI) and Machine-Learning algorithms. A total of 80 m of cores were evaluated using 170 XRD-validated samples to calibrate linear, nonlinear, and ensemble models. The results showed that the combination of Multiplicative Scatter Correction (MSC) preprocessing with Multilayer Perceptron (MLP) and Support Vector Regression (SVR) achieved the best performance, reaching an $R^2$ of 0.84. Explainable Artificial Intelligence (SHAP) confirmed the relevance of diagnostic bands between 2330 and 2360 nm, improving geological interpretability of the predictions. The proposed methodology provides a non-destructive and high-resolution alternative for mineralogical profiling, supporting the evaluation of complex reservoirs and decision-making in the oil and gas industry. Although the workflow was validated using a specific pre-salt dataset, future studies should assess its transferability to other carbonate reservoirs and broader geological settings. Full article

Accurate flood simulation in regulated, low-lying river basins is crucial for forecasting and risk mitigation, but performance depends strongly on whether models represent floodplain hydrodynamics and human regulation. This study evaluates three coupled hydrological–hydrodynamic schemes in the Huai River Basin upstream of Bengbu Station using identical meteorological forcing and VIC-generated runoff: (I) a linear routing scheme (VIC–Routing), (II) a natural hydrodynamic scheme (VIC–CaMa-Flood), and (III) an extended hydrodynamic scheme that incorporates reservoir regulation and levee effects (VIC–CaMa-Flood with Dam). Results reveal clear spatial differences in scheme suitability. The linear routing scheme performs best in upstream reaches, with NSE and KGE generally exceeding 0.81, but tends to overestimate peak discharge in downstream lowland sections. Incorporating hydrodynamic processes and regulation representation further reduces peak flow bias. Scheme III achieves the most consistent downstream improvement, particularly for high flows (>2000 m³/s), with NSE exceeding 0.80 in long-term simulations and improved agreement with satellite-driven inundation patterns. However, simplified reservoir operating rules can increase uncertainty in water level dynamics. During the 2020 plum rain flood, Scheme II yielded more accurate water levels in some reaches, suggesting that generalized operation rules may introduce compensating errors even when discharge accuracy improves. Overall, reliable flood simulation in well-managed basins requires an explicit representation of both floodplain hydrodynamics and regulation, and scheme selection should be guided by the dominant controls along the river network. Full article

Carbonate reservoirs feature strong heterogeneity, significant anisotropy, and complex pore structures, making conventional logging insufficient to meet the fluid identification requirements for complex lithologic reservoirs. To address this issue, this study proposes a Bayesian-Optimized Weighted Ensemble (BO-WE) model, which combines three base models: Support Vector Machine (SVM), random forests (RF), and Light Gradient Boosting Machine (LGBM). Bayesian optimization (BO) is used to tune the hyperparameters of the base model and determine the optimal ensemble weights. The proposed method is applied to the study area and compared with the Hard Voting (HVT) and Stacking (STK) ensemble models. The results show that BO achieves a reasonable weight distribution of the base model in the ensemble model, and the BO-WE model predicts the independent test set through four indicators: accuracy, precision, recall, and F1-score. The four indicators are all greater than 91.65%, and the fluid-type is accurately predicted. This model provides an effective method for fluid identification in carbonate reservoirs during oil and gas exploration and development. Full article

The Linhe Formation of the Paleogene in the Linhua Well area of the Hetao Basin is a key target interval for hydrocarbon exploration, but strong heterogeneity caused by depositional and diagenetic modification complicates reservoir prediction. This study integrates core observations, thin-section petrography, SEM, clay mineral XRD, vitrinite reflectance (Ro), routine petrophysical data, and conventional well logs to characterize sedimentary microfacies and diagenesis, constrain the diagenetic stage and paragenetic sequence, establish a well-log-based diagenetic facies recognition model, and reveal the spatial distribution of diagenetic facies. The reservoirs are dominated by lithic arkoses and feldspathic litharenites with moderate compositional and textural maturity. Sedimentary microfacies mainly include a subaqueous distributary channel, front sheet sand, and interdistributary bay. The reservoirs are presently overall in mesodiagenetic stage A. Compaction and cementation are the principal destructive processes, whereas dissolution is the main constructive process. Quantitative evaluation shows that COPL ranges from 14.3% to 31.6% (average 25.2%), CEPL from 5.3% to 18.7% (average 12.7%), and ICOMPACT from 0.47 to 0.80 (average 0.66), indicating that compaction contributed more to porosity loss than cementation. Four diagenetic facies were identified: strongly compacted–weakly cemented, moderately compacted–strongly cemented, moderately dissolved–moderately cemented, and weakly compacted–weakly cemented. Fisher’s linear discriminant model based on GR, AC, DEN, and CNL logs achieved an overall recognition accuracy of 80.0%. Spatially, high-quality reservoirs are mainly developed in the central–southern subaqueous distributary channel belts dominated by the weakly compacted–weakly cemented facies and flanked by moderately dissolved–moderately cemented facies. High-quality reservoir development is controlled by the coupled effects of depositional microfacies, differential compaction–cementation, and local dissolution. Full article

The critical metal lithium (Li) is increasingly sourced from spodumene and petalite pegmatite deposits due to their relatively high grades, lower mining environmental impacts and widespread global distribution. However, there are numerous gaps in our understanding of their genesis and the formation of unzoned or poorly zoned Li pegmatites is particularly difficult to explain. To investigate this, both spodumene-bearing and non-mineralized pegmatites and aplites are studied in the Moylisha segment of the Leinster pegmatite belt of SE Ireland, which were emplaced within the East Carlow Deformation Zone (ECDZ). Trace element modeling suggests that granite melts can achieve Li concentrations high enough (~5000 ppm) to crystallize spodumene. However, once crystallization begins, Li levels will drop rapidly below this threshold. While Li could be replenished by incoming melts, there is no supporting textural evidence for this, such as internal magmatic contacts, crosscutting relationships, or mingling. We test the hypothesis that low viscosity, Li-rich fluids from underlying reservoirs, most likely almost fully crystallized granite magmas or mush, continuously migrate through the heterogeneously crystallizing pegmatite-forming melts by percolative reactive flow, refertilizing interstitial melt by diffusion under favorable geochemical gradients. The flow of fluids is likely maintained due to their low relative density and periodic shearing within the ECDZ. Fluids with >10,000 ppm Li, derived by >95% crystallization (Rayleigh fractionation) of a granite magma, are shown to be capable of refertilizing a pegmatitic crystal mush after its emplacement. Supporting evidence includes macro- and micro-textures indicative of paragenetically late spodumene crystallization along apparent fluid flow pathways in mineralized pegmatites and aplites. Similar features are common in spodumene pegmatites worldwide and suggest that Li upgrading by fluid flow through crystallizing spodumene pegmatites may be a key process in enhancing Li grades and in some cases in producing economically favored low-Fe spodumene. Full article

Orbital maneuver detection is a core component of space situational awareness. The multi-scale characteristics of satellite orbital behavior and sample imbalance issues lead to challenges in existing methods, including insufficient feature adaptation and limited detection accuracy. This paper proposes an Adaptive Spiking Gating Multi-Scale Liquid State Machine (ASG-MSLSM) for orbital maneuver detection based on variations in satellite orbital parameters. The method integrates multi-scale reservoir pools with different scale-dependent decay factors and Leaky Integrate-and-Fire (LIF) neurons to enhance multi-scale temporal feature extraction capability. A spiking gating network is designed to adaptively learn fusion weights for multi-scale features, replacing traditional fixed equal-weight fusion strategies. During training, weighted binary cross-entropy loss is employed to address class imbalance. Experimental results based on real satellite data demonstrate that the proposed method significantly outperforms baseline models in maneuver detection metrics, achieving higher recall, improving feature separability, and reducing both missed detections and false alarms. These results indicate that the proposed method provides a robust solution for orbital maneuver detection. Full article

Horizontal wells in bottom-water reservoirs are highly susceptible to water coning during production. Consequently, accurately evaluating the water-control performance of inflow control valves (ICVs) is critical for optimizing completion strategies. Conventional semi-analytical models often struggle to capture the transient dynamics of multiphase flow, while standard numerical reservoir simulators fail to explicitly resolve the complex geometries of completion hardware. To address these limitations, this study proposes a multiscale composite modeling framework tailored for bottom-water reservoirs. At the near-well scale, a semi-analytical model is developed to characterize wellbore hydraulics and the pressure drops induced by ICV completions. At the reservoir scale, a numerical model is employed to simulate multiphase fluid transport, with the two scales coupled via cross-scale pressure field mapping. Validation against NETool software under steady-state conditions confirms the physical consistency of the near-well model in determining zonal flow allocation. Comparisons with conventional equivalent well numerical models demonstrate that the proposed composite model offers superior resolution of ICV-induced flow redistribution, yielding distinct production performance profiles. Furthermore, the integration of a Particle Swarm Optimization (PSO) algorithm enables the dynamic optimization of ICV settings. Results indicate that this composite framework provides a robust theoretical and computational basis for designing and evaluating intelligent water-control completions in bottom-water reservoirs. Full article

Resistivity is a key parameter in shale reservoir characterization. Diverse micro-pore types and complex conduction mechanisms in shale result in poor accuracy when applying existed conductivity models. Establishing a high-precision conductivity response model requires comprehensive consideration of the pore structure and clay-bound water conduction. The primary novelty of this work lies in replacing macroscopic empirical fitting parameters with a mechanistic, multi-dimensional fractal framework. We develop a novel conductivity response characterization model that explicitly couples multi-dimensional fractal pore structure theory with clay-bound water conduction. Experimental data verification demonstrates the new model’s superior characterization accuracy. Results indicate three distinct zones in the shale conductivity-pore water conductivity relationship: a nonlinear zone, a transition zone, and a linear zone. A higher cation exchange rate on clay surfaces leads to an increase in the nonlinear characteristics of the conductivity for both the shale and the pore water in low-salinity regions. An increase in the values of the conduction path fractal dimension, pore morphology fractal dimension, and pore fractal dimension all contribute to reduced shale conductivity. While sharing clay-induced conductivity terms with conventional dual-water and shale volume models, the new model offers advantages in operational simplicity and parameter accessibility. This research provides a physically rigorous and highly accessible approach for conductivity-based reservoir parameter calculation, offering new technical perspectives for complex shale oil/gas evaluation. Full article

The development of tight heavy-oil reservoirs is severely hampered by the high viscosity and poor mobility of crude oil caused by strong intermolecular stacking interactions among asphaltenes, coupled with the substantial adsorption loss and inadequate deep transport capacity of conventional displacement agents. By targeted penetrant delivery, a novel nanoemulsion system with a well-defined “core–shell” architecture was synthesized to address these critical challenges. The physicochemical properties, stability and oil displacement performance were evaluated. The prepared nanoemulsion exhibited an ultrasmall and uniform particle size distribution between 10 nm and 20 nm. It also demonstrated exceptional dispersibility in aqueous media and remarkable thermal and salinity stability under reservoir conditions. Furthermore, an ultralow critical micelle concentration of approximately 0.01% could be achieved and the oil–water interfacial tension was reduced to 7.3 × 10⁻² mN/m, significantly outperforming the conventional surfactant AES. Core flooding tests revealed that the proposed nanoemulsion enhanced oil recovery by 37.1% and attained a displacement efficiency of 68.9% in oil-wet capillary models. Molecular dynamics simulations further elucidated the underlying synergistic mechanism. The hydrophilic shell minimized adsorption on rock surfaces, facilitating deep migration within nanoporous channels. The hydrophobic core, containing terpinene as a penetrant, effectively disrupted the π-π stacking of asphaltenes due to its nonplanar molecular configuration. This disruption transformed the asphaltene aggregates from a tightly packed state to a dispersed state, resulting in substantial viscosity reduction. This work elucidated the mechanism of asphaltene aggregate disruption by nanoemulsions at the molecular level, offering a promising and theoretically grounded strategy for the efficient exploitation of tight heavy-oil reservoirs. Full article

During coalbed methane (CBM) production, coal reservoir pore/fracture structure varies dynamically under the action of fluid–solid coupling. And coal reservoir permeability changes accordingly. In order to factually investigate the dynamic changes in coal reservoir permeability in the CBM well drainage process, a comparative simulation experiment on the difference in coal permeability sensitivity to confining pressure (external pressure) and pore pressure (internal pressure) was carried out in this study. The results show that coal permeability presents a typical negative exponential decline with a decrease in pore pressure. The pore pressure sensitivity experiment can effectively simulate the permeability sensitivity characteristics caused by coal reservoir pressure. Based on the negative exponential function relationship between permeability and effective stress, a new calculating method for the effective stress coefficient was deduced. Namely, its value could be expressed as the quotient of the pore pressure sensitivity curve regression coefficient divided by the confining pressure sensitivity curve regression coefficient. A dynamic theoretical model for coal reservoir permeability characterized by reservoir pressure was systematically constructed based on the unique fluid (gas/liquid)–solid coupling characteristics of coal reservoirs. Furthermore, the general characteristics of the stress sensitivity of coal permeability during coalbed methane (CBM) recovery were analyzed. The dynamic evolution characteristics of coal reservoir permeability in the study area were further examined. Taking the production and drainage data of a typical actual CBM production well as an example, the theories regarding the permeability sensitivity of coal reservoirs to reservoir pressure presented in this paper were validated in practice. This indirectly confirmed the rationality and accuracy of the calculation method for the effective stress coefficient obtained through laboratory-based permeability sensitivity simulation experiments. This research provides robust theoretical support for the systematic monitoring and prediction of fluid production, reservoir pressure, and permeability during the CBM production process, carrying significant practical implications. Full article

Marine controlled source electromagnetic (CSEM) surveys have been proven to be an effective tool in hydrocarbon exploration, principally due to the method’s ability (in the right circumstances) to identify electrical resistivity contrasts between hydrocarbon-saturated and brine-saturated sedimentary units. However, the sensitivity of such surveys decreases in shallow water, for deeper targets, and for targets with limited horizontal extent. In principle, the resolution and sensitivity of a survey can be improved by moving either the transmitting or the receiving dipoles into the sub-surface. We have therefore investigated the sensitivity of Seafloor to Borehole CSEM (sbCSEM) survey geometries, specifically for the case of simplified targets with small lateral dimensions in shallow water areas—including targets whose depth of burial substantially exceeds their lateral extent. The results are encouraging. Neither small target size nor shallow water presents obstacles in principle to the use of this approach. Our models reveal distinct lobes in the patterns of electric field and current density amplitudes around a sub-seafloor transmitting dipole. The shape, positions and amplitudes of these lobes are all strongly modified by the presence of one or more small resistive targets, and they are strongly influenced by the positions of target edges. These effects significantly modify the pattern of electric fields at the seafloor and hence result in good sensitivity for realistic survey geometries. Small targets can be detected by seafloor receivers when the sub-seafloor transmitting dipole is located at some distance laterally outside the targets—leading to potential applications in ‘step-out’ prospecting. The asymmetry of responses at the seafloor from targets that are offset with respect to transmitter location has potential applications in field appraisal, while monitoring of reservoirs during production provides another possible application. Varying the depth of the transmitter down the borehole generates a Vertical EM Profiling (VEMP) survey—analogous to Vertical Seismic Profiling (VSP)—and we demonstrate that this too can have useful applications. Modelling for deeper (3 km sub-seafloor) targets continues to yield encouraging results and suggests that step-out sbCSEM may be effective at depths beyond the detection limit of conventional seafloor–seafloor CSEM. Full article

Downhole boosting technology breaks the physical limitations of conventional surface boosting by enhancing pressure at the wellbore bottom, with micro-axial compressors serving as its core compression module. However, traditional axial compressors, when miniaturized, suffer from severe end losses and easy instability, failing to adapt to downhole space constraints and the efficient pressurization demands of low-permeability, low-pressure, and small-flow reservoirs. To address this, this study designed a compact micro-axial compressor. CFturbo was used for parametric blade design and optimization, while ANSYS CFX 2025 (with the SST turbulence model) conducted numerical simulations. A “simulation–diagnosis–optimization–validation” closed-loop strategy was adopted to adjust the blade’s leading-edge shape, camber line, and thickness distribution, combined with grid independence verification and inter-stage matching optimization. The results show that at the design speed (60,000 rpm), the compressor achieves a pressure ratio of 1.57 and an isentropic efficiency of 83.6%. It also maintains stable performance at 55,000 rpm (off-design speed), with excellent inter-stage aerodynamic matching and controllable leakage losses. This compressor meets downhole operational needs, providing technical support for developing low-permeability, low-pressure, small-flow reservoirs. Full article

To elucidate coalbed methane (CBM) adsorption mechanisms in deep coal reservoirs, the macromolecular structures of coal samples with different coal ranks were characterized using FTIR, XPS, and C NMR, followed by the construction of corresponding molecular models. Grand Canonical Monte Carlo (GCMC) simulations were employed to investigate methane adsorption behavior within the coal matrix at 313.15 K and pressures up to 20 MPa. The results showed that as coal rank increased (R_o,max = 1.63% to 3.18%), the coal macromolecular structure transformed from a side-chain-rich configuration to a highly aromatized and directionally stacked structure. This structural maturation leads to a more compact coal matrix, evidenced by a reduction in free volume from 5108.39 ų to 3999.87 ų and a decline in accessible free volume from 8.23% to 6.26%, thereby restricting the effective space for methane storage. At 20 MPa, although the pore walls of high-rank coal exhibit stronger localized adsorption capacity, the bulk adsorption capacity follows the order: DZ > ZC > SH. This suggests that under deep, high-pressure conditions, the pore-volume compression effect associated with increasing coal rank governs the upper limit of adsorption per unit mass of coal. As pressure increases into the deep reservoir regime, the state of methane in coal micropores gradually shifts from surface adsorption to a high-density, quasi-liquid filling behavior. Consequently, the influence of specific surface area diminishes, while effective free volume emerges as the primary determinant of high-pressure adsorption capacity. The impact of coal rank on deep methane adsorption reflects a competition between enhanced adsorption potential and restricted storage space. The densification-induced compression of effective free volume is identified as the dominant factor limiting the adsorption capacity of deep CBM. This study provides a molecular-scale understanding of deep CBM occurrence mechanisms and establishes a theoretical framework for resource evaluation. Full article

Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in NM, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling. Full article

Algal proliferation in subtropical drinking water reservoirs has become increasingly severe, and developing a reliable prediction for algal abundance through high-frequency in situ data is essential for early risk warning and effective management. This study analyzed the interannual variations in algal abundance in the Shanmei (SM) Reservoir, located in Quanzhou City, Fujian Province, China, based on the high-frequency data between 2020 and 2025, and forecasted algal abundance 24 h ahead via the optimized Transformer model. Results revealed that the SM reservoir exhibited seasonal variability in environmental factors, with persistently elevated pH during spring and summer, ranging from 7.12 to 9.66, and relatively high total nitrogen concentrations, ranging from 1.17 to 2.28 mg/L. Overall, algal abundance increased throughout the study period, and the annual average algal abundance in 2025 was 8.18 × 10⁶ cells/L, which was twice that in 2021. Model comparisons revealed that the optimized Transformer model exhibited the highest performance in terms of R² = 0.88 when predicting the next hour using 12 days of data. Feature importance analysis based on SHapley Additive exPlanations (SHAPs) revealed that the predictions of algal dynamics were primarily influenced by previous-hours algal abundance, permanganate index, dissolved oxygen, air temperature, wind speed, and pH. This study revealed that the optimized independent learning model with integrated multi-scale features can significantly enhance the predictive performance of algal dynamics, offering a technical basis for early warning of algal blooms and refined reservoir management. Full article