Search Results (6,318)

The capacitance resistance model (CRM) is an analytical tool widely used to forecast reservoir performance in enhanced oil recovery (EOR) methods. By representing flow dynamics and the connectivity between injection and production wells through the parameter of interwell connectivity, CRM offers fast computational processing and minimal input data requirements. These advantages make CRM a practical alternative for rapid reservoir analysis, especially when full-scale numerical simulations are infeasible due to time and budget constraints. CRM, rooted in material balance and productivity equations, uses injection/production rates and bottom-hole pressure data to construct reservoir models through optimization techniques, which can then be combined with oil fractional flow models for predictive purposes. Initially designed for waterflooding operations, CRM has seen limited but promising applications in gas injection projects, where research remains incomplete. This study presents a new formulation of CRM tailored for immiscible gas injection, incorporating the pseudo-pressure concept coupled with a saturation profile. The pseudo-pressure concept is a mathematical transformation that linearizes gas flow equations by accounting for variations in gas compressibility and viscosity with pressure. The proposed CRM was evaluated using a PUNQ-S3 benchmark reservoir model in the CMG IMEX black oil simulator, involving two injectors and four producers. History- matching results for fluid production rates showed that the newly developed CRM achieved the lowest NRMSE, outperforming other CRM models across a wide range of reservoir properties. Sensitivity analyses were conducted to examine the effects of gas and oil PVT properties on the model’s performance. The newly developed CRM, incorporating the pseudo-pressure concept and saturation profiles, demonstrates superior performance in predicting fluid production rates, achieving an average NRMSE of 15.3% in a base case scenario, compared to other tested CRM models. Additionally, the sensitivity analysis on the effect of fluid properties shows that higher gas viscosity, lower gas formation volume factor, and increasing oil API gravity improve the CRM model’s performance, and under all tested conditions the newly developed CRM provides the most accurate production history match. This study not only establishes the new CRM as a robust and accurate predictive tool for immiscible gas injection but also provides a comprehensive discussion on reservoir parameter ranges and model limitations, advancing the applicability of CRM in EOR processes. Full article

Deploying highly generalizable deepfake detection systems on resource-constrained edge devices poses a significant technical challenge for conventional end-to-end large models that rely heavily on computational resources. Extracting multi-source physical prior features is a viable approach under limited computational power; however, in few-shot scenarios, the dimensional mismatch of heterogeneous features is prone to causing downstream classifiers to overfit. To mitigate this bottleneck, this paper proposes a “static feature extraction–central normalization alignment–independent downstream decision” decoupled detection system for few-shot cross-domain tasks on edge devices. The front end of the system constructs an 856-dimensional comprehensive feature reservoir, and a lightweight residual normalization adapter $g_{\varphi }$ is introduced as the central support module. This module explicitly compresses the intra-class variance of heterogeneous features, providing a smoothly aligned manifold base for downstream classifiers. Experimental results indicate that this decoupled architecture demonstrates consistent stability in few-shot ($K=10$) cross-domain evaluations. When encountering intra-family cross-domain shifts and cross-mechanism distribution shifts from diffusion models, the accuracy reaches 84.9% and 76.1%, respectively. Compared to representative end-to-end meta-learning baselines (e.g., MAML), the relative error rate is reduced by over 30%. Furthermore, after completing the asynchronous offline pre-processing (approximately 897 ms) at the front end, a single-image online classification query requires only 7.7 ms under a simulated single-core CPU constraint, satisfying the low-latency requirements for lightweight deployment on edge devices. Finally, combined with empirical observations, this paper discusses the performance boundaries of the architecture in cross-mechanism metric mismatch scenarios, providing a low-barrier, robust engineering defense scheme for resource-constrained environments. Full article

Chicken (Gallus gallus domesticus) domestication, likely associated with dry-rice farming in central Thailand, has led to substantial loss of ancestral immune-related genetic diversity in commercial chicken lineages. This study addresses allelic loss by providing the first comprehensive analysis of the highly polymorphic BG1 gene, an MHC-linked marker across the wild–domestic interface in Thailand and Vietnam, using high-depth Illumina amplicon sequencing. Genomic DNA from 47 Thai and Vietnamese chicken populations was extracted using a salting-out protocol following ethical sampling. Allelic variation was examined by targeting the BG1 intron 15–exon 16 region using triplicate PCR and Salus Pro NGS sequencing. Evolutionary dynamics and selection pressures were analyzed using AmpliSAS, MrBayes, and Datamonkey, while AlphaFold 3 was used to predict and validate 3D protein structures. We identified 98 novel alleles and 172 polymorphic sites within the BG1 intron 15–exon 16 region encoding an Ig-like domain. Extensive allele sharing between indigenous chickens and red junglefowl indicated strong balancing selection and trans-species polymorphism. Selection analyses showed that purifying selection conserved structural integrity at codons 9, 13, and 18, while variation at other sites enhanced immune recognition. AlphaFold 3 modeling confirmed conservation of the β-sandwich fold across variants, maintaining stability of the Immunoreceptor Tyrosine-based Inhibition Motif (ITIM). Thus, despite the regional gene flow, geographic isolation has shaped distinct signatures, as evidenced by the presence of 38 unique Thai and 9 unique Vietnamese alleles in addition to breed-specific private markers in the Betong (BG1*TH88), Decoy (BG1*TH91), and Tre (BG1*VN54) populations. A notable adaptive outlier under positive selection (ω = 1.357) was detected in the Dong Tao population, suggesting a recent selective sweep. These findings support the mission of the Siam Chicken Bioresource Project (SCBP) to utilize indigenous breeds as genetic reservoirs and provide a molecular basis for restoring resilience traits in domestic poultry to enhance global food security. Full article

Reservoir fracability evaluation is critical for tight reservoir hydraulic fracturing optimization. This study introduces a novel physics-based fracability evaluation framework integrating stacking ensemble learning (SEL) and the marginal effect of the conditional expectation (MECE). First, a multidimensional indicator system was established, covering characteristics such as reservoir geomechanics, rock mechanics, and the development of natural fractures. Second, SEL models were developed to predict open flow capacity, and four performance metrics were compared to select the optimal model from 26 SEL candidates. Finally, to quantify the individual contribution of each fracability indicator while eliminating interference from treatment and petrophysical parameters, the MECE approach was adopted, thereby developing a new fracability model that quantitatively describes the reservoir’s ability to achieve greater stimulated reservoir volume (SRV) under similar hydraulic fracturing parameters. The experimental results indicate that the RF+KNN model demonstrates optimal performance in both prediction accuracy and model stability. Comparing the fracability index with microseismic monitoring data, the linear correlation coefficient between the fracability index and SRV reached 92%, validating the reliability of the fracability evaluation model. This framework provides a transferable interpretable tool for selecting reservoir sweet spots and fracturing parameter optimization. Full article

The efficient development of low-permeability reservoirs is critically constrained by severe geological heterogeneity, marginal permeability (<10 mD), and the consequent prevalence of low-productivity wells. Conventional intermittent production management, reliant on empirical fixed-cycle schedules, fails to adapt to dynamic reservoir behavior and wellbore conditions, leading to suboptimal energy efficiency and recovery. This study presents a physics-constrained, data-driven framework for adaptive intermittent production optimization, specifically designed to address the coupled geological-engineering complexities of such reservoirs. The methodology integrates three core innovations: (1) a hybrid flowing bottomhole pressure (FBHP) decline model coupling a “Three-Segment” wellbore pressure calculation with inflow performance relationship (IPR) curves, enabling dynamic characterization of pressure depletion; (2) a shut-in pressure buildup prediction framework combining a physically interpretable dual-exponential recovery mechanism—representing near-wellbore elastic expansion and far-field formation recharge—with a Random Forest Regression algorithm to capture the influence of geological and operational heterogeneity; and (3) a “Dual-Curve Intersection Method” that autonomously determines optimal pumping and shut-in durations by intersecting predicted pressure decline and recovery curves under geological constraints. Field implementation on 15 low-production wells in the Jiyuan Oilfield—a representative low-permeability asset—demonstrated robust performance: average pump efficiency improved from 14.3% to 14.49%, and average single-well electricity savings reached 15.61%. This work establishes a closed-loop intelligent control framework that bridges reservoir geology, wellbore hydraulics, and machine learning, offering a scalable solution for enhancing energy efficiency and production sustainability in low-permeability and unconventional resources. Full article

Distributed fiber optic temperature sensing (DTS) monitoring technology is increasingly widely applied in oil reservoir water injection development. However, existing DTS interpretation methods for layered water injection processes have insufficiently considered the effects of multilayer injection and reservoir damage. To address this issue, this paper takes into account interlayer heterogeneity and reservoir damage and, based on the laws of conservation of mass and energy, comprehensively incorporates the effects of friction, the Joule–Thomson effect, thermal convection, and thermal expansion. By coupling wellbore pipe flow with formation seepage, a temperature profile prediction model for multilayer water absorption under steady-state water injection conditions is established. Comparative validation against classical models such as those by Babak and Nowak demonstrates that the proposed model achieves high computational accuracy. Using this model, the influence patterns of injection rate, tubing diameter, formation coefficient, and skin factor on wellbore temperature distribution are systematically analyzed: a higher injection rate leads to a smaller temperature rise in the injected water; a larger tubing diameter results in a greater temperature rise; the formation coefficient affects the temperature profile by regulating interlayer water absorption distribution, while reservoir damage (skin factor) has a relatively limited direct impact on the temperature profile. The model is applied to interpret DTS field data from Well A, and the water absorption rate of each sublayer is quantitatively obtained: the main water absorbing intervals are 1878.7–1897.5 m and 1919.5–1950.6 m, with water absorption accounting for 30.57% and 24.28% of the total injection rate, respectively, while the remaining intervals exhibit secondary water absorption. These interpretation results are in good agreement with earlier oxygen activation tests. This study provides a theoretical basis and analytical method for applying distributed fiber optic temperature measurement technology to monitor water absorption profiles in multilayer injection wells. Full article

Introduction: Efficient geothermal energy extraction has the potential to significantly alleviate the shortage of fossil energy, but low extraction efficiency and an insufficiently understood extraction mechanism remain key bottlenecks hindering its large-scale deployment. Method: This study develops a fluid–solid coupled numerical model based on the intrinsic physical properties of geological reservoirs to systematically analyze the energy extraction characteristics of geothermal systems. Simultaneously, the effects of key geological factors on fluid flow behavior within geothermal reservoirs are investigated. Furthermore, molecular dynamics simulations are employed to elucidate the microscopic mechanisms by which driving fluids facilitate geothermal energy extraction. Results: The results demonstrate that the thermo-hydraulic–mechanical (THM) numerical model was validated through a comparison with benchmark data reported in previous studies, exhibiting a high degree of agreement with geothermal extraction performance. The model further confirms that heat transport in the geothermal reservoir is characterized by a pronounced “tongue-in” isotherm pattern during the extraction process. Discussion: Lower initial temperatures of the driving fluid lead to more rapid geothermal energy extraction compared with higher initial temperatures, and the “tongue-in” phenomenon becomes increasingly pronounced as the initial injection temperature decreases. Moreover, increased injection pressure significantly enhances geothermal energy extraction efficiency; however, reduced pressure differentials markedly suppress the development of the “tongue-in” pattern and decrease reservoir permeability. In addition, water used as a heat-driving fluid achieves higher thermal extraction efficiency than water, while simultaneously exerting a stronger moderating effect on the permeability evolution of geothermal reservoirs. Conclusions: The simulation results obtained from the thermo-hydraulic-mechanical (THM) numerical model provide fundamental data to support the efficient development of geothermal reservoirs, while the associated analyses offer valuable insights into the selection of appropriate driving fluids for reservoirs with distinct geological characteristics. Full article

This paper extends the metacybernetic framework by grounding its conceptual descriptions in first principles of information physics. We demonstrate that for living systems to organise efficiently under uncertainty, they must adhere to a strict recursive pattern, a “fractal seed” originating in the third-order interaction between potential and action. By utilising Fisher Information Field Theory (FIFT) within an Informational Realism paradigm, we formalise this process through variational analysis on an implicate–explicate manifold. Under a rigorous informational parsimony constraint (a functional analogue of the holographic principle), we treat the J-field as the dispositional reservoir of latent potential and the I-field as the operative field of structured configurations, and show how their autopoietic coupling generates the system’s Potential–Actuation trait poles as a scale-invariant viability structure This coupling reveals that the boundary substructure, which encodes the holographic content, directly conditions the emergent superstructure through a deterministic parity rule inherited from the dyadic logic of the minimal generic living system represented by ${\widehat{\theta }}_2$. Drawing on the application of Fisher Information, we show that maintaining informational parsimony requires the system’s architecture to oscillate: odd-numbered orders express two traits (dyads), whereas even-numbered orders express three (triads). This produces a canonical 2–3–2–3–2 sequence, preventing a combinatorial explosion of traits as systemic depth increases. We present the Cogitor5 model as a complete fifth-order exemplar of this rule, demonstrating how this rhythmic structural pattern enables self-evolution, systemic coherence, and collective intelligence in both biological and artificial agencies. Full article

The Gulong shale oil reservoir is characterized by high clay content and strong heterogeneity, with substantial variations in mineral composition among different intervals. However, existing fracability evaluation methods for such continental shales remain inconsistent and often rely on oversimplified two-dimensional fracture descriptors, lacking a multi-parameter quantitative framework derived from three-dimensional fracture characterization. In this study, the Q1 and Q9 members of the Gulong shale oil were selected, and laboratory-scale hydraulic fracturing simulation experiments were conducted using supercritical carbon dioxide (SC-CO₂), liquid CO₂, and water as the fracturing media. Within a fractal-theory framework based on CT-derived three-dimensional reconstructions, a multi-scale evaluation index system was established by integrating fractal dimension, fracture density, and spatial connectivity. The experimental results demonstrate that fluid properties exert a decisive influence on rock failure behavior. Owing to its ultra-low viscosity and strong diffusivity, SC-CO₂ can significantly reduce formation breakdown pressure while effectively activating natural weak planes to generate a more complex fracture network. For the Q9 shale, the breakdown pressure under SC-CO₂ is reduced by 11.91% and 8.33% relative to water and liquid CO₂, respectively. Moreover, the fracture fractal dimension reaches 2.41 under SC-CO₂, which is markedly higher than the values obtained under liquid CO₂ (2.18) and water (2.12). Mineral composition and densely developed bedding are the key factors inducing fracture branching and deflection, whereas injection rate and an asymmetric stress field regulate the internal energy-release rate and stress path, thereby influencing fracture crossing capability and aperture evolution. Based on the experimental dataset, a fracture complexity index (FCI) evaluation model was developed: under SC-CO₂ fracturing, the FCI values are 8.92 for the Q9 member and 4.43 for the Q1 member, and the model predictions are in good agreement with physical observations. This work elucidates the failure mechanism of the Gulong shale under multi-field coupling and provides a theoretical basis for optimizing hydraulic fracturing and evaluating fracability in unconventional reservoirs through the proposed FCI-based assessment framework. Full article

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