Search Results (101)

We developed an ensemble model based on the fusion of Extreme Gradient Boosting and Light Gradient Boosting Machine. 20 key parameters, including hourly charging rate, blast volume, and blast pressure, are selected as input features to construct and train the ensemble model for predicting the blast furnace permeability index. The results show that the model achieves a root mean squared error of 0.0868, a mean absolute error of 0.0708, and a coefficient of determination of 0.9602, confirming its excellent predictive accuracy. Full article

To investigate how spreading conditions affect green tea taste and aroma and to develop a generalizable prediction model from small data for process optimization, this study integrated SEM, non-targeted dual-omics, and TabPFN to systematically analyze Echa No. 10 spreading. A central composite design was used. Dehydration-induced mechanical stress altered cell membrane permeability, driving non-volatile taste compound transformation and volatile aroma release. Two chemical-sensory proxies, relative polyphenol-to-amino acid ratio (R-PAR) and floral intensity index (FII), were established using ultra-high performance liquid chromatography–high-resolution mass spectrometry (UHPLC-HRMS) and headspace solid-phase microextraction–gas chromatography–mass spectrometry (HS-SPME-GC-MS). A prediction model was built with these indicators and TabPFN. Multi-objective optimization yielded optimum conditions: initial moisture 76.8%, temperature 26.2 °C, relative humidity 61.5%, air speed 0.85 m/s, achieving R-PAR 0.465 and FII 125.70. Compared with response surface methodology (RSM), partial least squares regression (PLSR), and support vector regression (SVR), TabPFN showed prediction R² of 0.81 and 0.77, showing favorable applicability and predictive capability on small-sample data. This study validates TabPFN’s suitability for small-sample tea processing modeling, quantifies the mapping between spreading and key taste/aroma metabolism, and provides a methodological foundation for digital precision and intelligent optimization in green tea production. Full article

In the ultra-high water cut stage, unconsolidated sandstone reservoirs suffer from severe reservoir property time-variation, streamline solidification, and inefficient water circulation. To tackle these problems, this study takes Chengdao Oilfield Block 1G as an example and establishes a dynamic geological model considering permeability time-varying characteristics based on logging, core, and production data. The flow field intensity index and streamline solidification rate are introduced to quantitatively characterize the preferential flow channels and high water-consumption zones. Results show that long-term water flooding increases the average permeability by 26.88% and expands the interlayer permeability ratio from 10.33 to 19.00. The streamline solidification rate reaches 75%, forming obvious “short-circuit” circulation. Three remaining oil enrichment patterns are identified, which are mainly controlled by sedimentary microfacies, structural highs, and well pattern control. A differential regulation strategy including 3D well pattern reconstruction and streamline diversion is proposed. Field prediction indicates that the cumulative incremental oil can reach 410,000 tons and the recovery factor is enhanced by 1.3%. This study not only reveals the dynamic evolution mechanism of flow field under water-rock coupling effects but also provides a practical technical system for flow field regulation and remaining oil tapping in similar offshore ultra-high water-cut unconsolidated sandstone reservoirs. Full article

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R² of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R² of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs. Full article

In semi-arid regions, sustainable groundwater management for irrigation is critical for agricultural productivity and food security. This study presents an integrated methodological framework combining hydrochemical characterization, machine learning (ML) modeling, and explainable artificial intelligence (XAI) to predict the Irrigation Water Quality Index (IWQI) in the Ain Oussera plain, Djelfa Province, Algeria. A total of 191 groundwater samples were collected from November 2023 to September 2024 and analyzed for major ions and physicochemical parameters. Multiple irrigation suitability indices were calculated, including Sodium Adsorption Ratio (SAR), Sodium Percentage (Na%), Magnesium Hazard (MH), Permeability Index (PI), Residual Sodium Carbonate (RSC), Soluble Sodium Percentage (SSP), and Kelly’s Ratio (KR). Five ML models were developed and evaluated for IWQI prediction: Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Regression. Results showed that 55% of groundwater samples exhibited low to no restrictions for irrigation use, while 19% required high to severe restrictions. The XGBoost model demonstrated superior performance, with the highest R² (0.95) and the lowest RMSE (3.22) among all tested algorithms. SHAP (SHapley Additive exPlanations) analysis provided a transparent interpretation of model predictions, identifying electrical conductivity and Sodium Adsorption Ratio as the most influential parameters affecting IWQI, while chloride, sodium, total hardness, and magnesium had minimal impact. Spatial mapping using Inverse Distance Weighting (IDW) interpolation in ArcGIS 10.8 revealed considerable spatial variability in water quality throughout s the plain. This research addresses a critical gap in North African groundwater management by integrating ML predictive capabilities with XAI transparency, providing water resource managers and agricultural stakeholders with interpretable, data-driven tools for sustainable irrigation planning in water-stressed semi-arid environments. Full article

Permeability constitutes a critical factor controlling the production of coalbed methane (CBM), and the sensitivity of the CBM reservoirs to stress and the degree of coalification strongly influences permeability variations. Elucidating the mechanism underlying the sensitivity of reservoir permeability to stress and degree of coalification is therefore a crucial prerequisite for enhancing CBM production capacity. Helium permeability tests were conducted on raw coal pillar samples to investigate the variation in coal permeability under different effective stresses and degrees of coalification. The effective stress ranged from 1.5 to 7.5 MPa, and the maximum vitrinite reflectance (R_o,max) varied between 0.456% and 3.211%. The results indicate that permeability decreases with increasing effective stress and R_o,max. When internal fractures in the coal samples are poorly developed, this relationship follows a negative exponential trend. To evaluate the permeability sensitivity of the coal samples, a stress sensitivity index (S₁) and a coalification degree sensitivity index (S_1R) were introduced and constructed. In addition, the permeability damage rate (PDR) and stress sensitivity coefficient (α_k) were also employed to assess permeability sensitivity. The results show that the stress sensitivity of coal decreases with the increase in effective stress but increases with the rise in R_o,max; the coal sensitivity of coalification degree decreases with the rise in R_o,max and increases with the increase in effective stress. Furthermore, S₁ and S_1R exhibit strong positive linear correlations with other sensitivity evaluation parameters, indicating that they can serve as comprehensive indices for evaluating the overall permeability sensitivity of coal samples. A predictive model relating permeability to effective stress and maximum vitrinite reflectance was established for coal reservoirs. Using Pearson’s, Spearman’s, and Kendall’s correlation coefficients, the relationships among effective stress, coalification degree, and permeability were analyzed. The results reveal that coalification degree exerts a stronger control on permeability than effective stress. The permeability control mechanism was thereby clarified, providing theoretical guidance for the efficient development of CBM reservoirs. Full article

As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses on low-permeability reservoirs in the Changqing Oilfield, evaluating three surfactant systems—YHS-Z1 (a 7:3 mass ratio blend of hydroxypropyl sulfobetaine and cocamide), YHS-Z2 (a polyether carboxylate, a nonionic-anionic composite) and a middle-phase microemulsion system (Heavy alkylbenzene sulfonate and hydroxysulfobetaine were combined with a mass ratio of 7:3)—through a series of experiments including interfacial tension measurement, contact angle analysis, static and dynamic oil displacement tests, as well as emulsion transport/retention index assessments, to comprehensively characterize their oil displacement properties. Based on the experimental data, this study constructed four classical regression models: Ridge Regression, Random Forest (RF), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR), and conducted a comparative analysis of their predictive performance. The results demonstrate that the Random Forest (RF) model achieved the optimal prediction performance, with a Mean Absolute Error (MAE) of 1.8245, a Mean Absolute Percentage Error (MAPE) of 4.78%, and a coefficient of determination (R²) of 0.9428 on the training set. Further analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the retention index is the primary global factor (accounting for 49.79% of the variance), while significant intergroup differences exist in the primary factors across different surfactant systems. Concurrently, single-factor and multi-factor sensitivity analyses were conducted to elucidate synergistic effects and threshold behaviors among parameters. The optimal parameter combination, identified via a random search method, achieved a predicted recovery factor of 45.61%, representing a 6.57% improvement over the highest experimental value. This study demonstrates that machine learning methods can effectively identify the dominant factors in oil displacement and enable synergistic parameter optimization, thereby providing a theoretical foundation for the efficient development of surfactant flooding in low-permeability reservoirs. Full article

High-velocity fluid flow in porous media frequently exhibits non-Darcy behavior, where inertial losses lead to nonlinear pressure gradient velocity behavior. Predicting the Forchheimer coefficient β remains challenging because β varies sensitively with pore geometry and is often not constrained by porosity and permeability alone. This study develops a structure-based method to estimate β using intrinsic descriptors obtained from the Darcy regime flow characterization and image-based geometry analysis. A set of two-dimensional granular porous media was generated with controlled variations in porosity, particle size distribution, and grain size variability. Single phase simulations are simulated with a body-force multiple-relaxation-time lattice Boltzmann method. The transition from Darcy flow to non-Darcy flow is identified from the velocity and pressure gradient response, and β is determined by fitting the inertial flow regime. Two tortuosity responses were observed. In uniform media, hydraulic tortuosity remained nearly constant in the Darcy regime and then gradually decreased. In disordered media, hydraulic tortuosity first increased with the onset of recirculation and then decreased as dominant flow paths became stable. Based on these results, a dimensionless inertial factor was correlated with porosity, intrinsic hydraulic tortuosity, and a pore structure index derived from specific surface area and hydraulic pore size. The resulting model predicts β from permeability and structural descriptors. The resulting correlation provides β estimates from Darcy permeability and geometry descriptors. Validation with quasi-two-dimensional microfluidic pillar array data showed that the model captured both the magnitude and relative ordering of β for the tested geometries. The proposed framework should be regarded as a proof of concept for idealized granular porous media and quasi-two-dimensional structured systems. Full article

The southern Junggar Basin in Xinjiang is rich in coalbed methane (CBM) resources. Large-scale development is underway in the Sigong River block (SGR block) of the Fukang West Block. Based on an integrated analysis of geological and hydrogeochemical characteristics, this study clarifies the key factors affecting CBM well productivity in the SGR block. Based on gas and water production performance, four distinct productivity types of CBM wells are identified, which are jointly controlled by burial depth, local structural and hydraulic disturbance, and also governed by synergistic interplay between gas content and permeability. The optimal geological combination—comprising the 700–1000 m burial depth, syncline core structure, stagnant hydrodynamic conditions, relatively high gas content, and favorable permeability—collectively contributes to the high-productivity Type I wells with low water production. In contrast, deep coal seams (>1400 m), characterized by reduced gas content and extremely low permeability, correspond to Type IV wells, which exhibit low gas and water production. Type II wells, located in the 1000–1400 m interval, exhibit moderate and variable productivity controlled by the interplay between high gas content and a wide range of permeability. Shallow margins (<700 m) affected by coal combustion and surface water influx produce high-water and low-gas wells (Type III). Geochemical signatures effectively differentiate between these types: closed, stagnant environments (Types I/II) are marked by a Na-Cl-HCO₃/Na-HCO₃-Cl water type, moderate total dissolved solids, and low sodium chloride coefficients, while open hydrodynamic conditions (Type III) are indicated by Na-SO₄-HCO₃ water with high sodium chloride coefficients. A δD-H₂O/δ¹⁸O-H₂O ratio of 7–9, combined with favorable TDS and water type, is identified as a key indicator of high productivity. Based on these relationships, a productivity response index model incorporating critical geological and geochemical parameters was developed. This model provides a practical tool for predicting CBM well performance and targeting sweet spots, offering significant value for exploring geologically and hydrologically complex basins. Full article

Unconsolidated sandstone reservoirs, particularly in offshore and marine environments, are highly susceptible to sand production, which leads to flow-capacity degradation, plugging evolution, sand-retention instability, and erosion–corrosion damage in gravel-pack completion systems. To address the lack of system-level and quantitative integrity evaluation methods, a unified assessment framework is developed by coupling flow behavior, sand-retention mechanisms, and erosion–corrosion damage processes. The gravel-pack completion system is idealized as a concentric multilayer porous-medium structure under steady-state radial Darcy flow, and an equivalent radial permeability model is established to characterize flow capacity and anti-plugging performance, which enables consistent comparison of different completion schemes under identical plugging conditions. Based on sand-retention mechanisms, a sand-retention capacity index is proposed by integrating formation particle size distribution, screen aperture, gravel size, and sand-leakage risk. An erosion–corrosion coupled damage model is further developed to predict screen damage rates in CO₂-containing environments, and an integrity index is formulated to link damage evolution with long-term service performance. By integrating flow capacity, anti-plugging performance, sand-retention capacity, and structural integrity using a weighted geometric mean, a comprehensive evaluation index is established for overall system integrity assessment. Using the proposed framework, a representative formation sand with d₁₀ = 30  μm, d₅₀ = 180  μm, and d₉₀ = 500 μm  is evaluated. The optimal sand control design corresponds to a gravel median size of 971.53 μm (equivalent to a standard 16/20 mesh gravel) and an optimal screen aperture of 523.11 μm, with a screen porosity of 0.56. Under these conditions, the selected screen aperture and gravel size are well matched with the formation sand size, falling within recommended engineering ranges and achieving a favorable balance among sand retention, flow capacity, anti-plugging performance, and structural integrity. The proposed framework provides a quantitative and engineering-applicable basis for the optimization and integrity classification of offshore gravel-pack sand control completions under multi-constraint operating conditions. Full article

The coconut water market continues to expand, but industrial supply is constrained by the high perishability of fresh coconut water and the need for stabilization routes that preserve quality. This study optimized crossflow microfiltration of coconut water using a silicon carbide (SiC) ceramic membrane, high permeability, chemical/thermal robustness, and cleanability, and assessed the techno-economic feasibility of a green coconut biorefinery producing microfiltered coconut water and coconut pulp. Pressure and temperature were modeled and optimized using a face-centered design (FCD) and artificial neural networks coupled with a genetic algorithm (ANN–GA), considering permeate flux and fouling index (p < 0.05). Both approaches converged to the same operating point, and experimental validation at 75 kPa and 30 °C achieved 605.32 ± 15.34 L h⁻¹ m⁻² and 82.79 ± 1.35% at VRR = 1. Sample-level fit statistics favored ANN (higher R² and lower sample-level errors), whereas condition-wise grouped cross-validation (leave-one-condition-out) indicated higher predictivity and lower RMSE_CV for the quadratic FCD/RSM models across experimental conditions, highlighting response-dependent generalization within the investigated domain. Fouling analysis indicated concentration polarization as the main resistance contribution and a flux-decline behavior best described by the intermediate blocking mechanism. A SuperPro Designer^® simulation over a 20-year project life indicated economic feasibility under baseline assumptions (Internal rate of return—IRR = 23.80%, Net present value—NPV = US$733,761, payback = 2.96 years), with profitability remaining attractive under ±10% selling-price variation. Overall, the process optimization and modeling outcomes align with the economic case, reinforcing the potential of this biorefinery concept for industrial deployment. Full article

CO₂ injection success hinges on the injectivity index, a major determinant of storage feasibility. This study develops a machine learning (ML)-driven framework optimized for CO₂ injectivity prediction, benchmarking its robustness and real-world applicability against an empirical correlation developed in the literature. The framework is applied to the Entrada Formation in the San Juan Basin, a laterally extensive sandstone unit with limited structural complexity across most of the basin, except for localized uplift in the Hogback region. A numerical model was calibrated to perform sensitivity analysis to identify the dominant parameters influencing injectivity. A dataset of these parameters generated through experimental design informs the development of several ML-based proxies and the best model is selected based on error metrics. These metrics include coefficient of determination (R²), mean absolute error (MAE), and mean squared error (MSE). The effective permeability-thickness product was obtained by the Peaceman’s well model, fractional flow slope, and Dykstra–Parsons coefficient were identified as the most influential parameters impacting the objective function. Train–test and blind test validation identified the Ridge model as the best, achieving an R² ≈ 0.994. The Ridge model which was used to map the Entrada Formation closely matches field-based correlations in the literature, confirming both its physical validity and the Entrada Formation’s strong injectivity potential, with slight deviations explained by the inclusion of additional parameters. This study reduces dependence on computationally intensive simulations while improving prediction accuracy. By benchmarking against established correlations, it enhances model reliability across diverse reservoir conditions. The proposed framework enables rapid, data-driven well placement and feasibility evaluations, streamlining decision-making for CO₂ storage projects. Full article

Background: Diabetes can increase cardiovascular risk (CVR) through hyperglycemia and intestinal damage. The purpose of this study is to evaluate several intestinal permeability biomarkers in predicting CVR in patients with type 2 diabetes mellitus (T2DM). Methods: This study was conducted in 2024 with a total of 70 patients with T2DM, aged 19–64 years (32.9% men, 67.1% women). Socio-demographic data and health status were collected; Framingham Risk Score (FRS), anthropometric measures, and serum parameters (glucose, HbA1c, lipids, CRP, TNF-α, IL-6, trimetilamine-N-oxide (TMAO), zonulin, intestinal fatty acid binding protein (I-FABP)) were evaluated, and visceral adiposity index (VAI) and plasma atherogenic index (PAI) were calculated. Results: The mean age of patients (n = 70) was 55.0 ± 7.55 years. According to FRS, 18.5% of individuals were determined to be at medium–high CVR; a positive correlation was found between BMI, waist–height ratio, body fat ratio, VAI value, and FRS total score (p < 0.05). Serum TMAO, zonulin, and I-FABP levels did not differ between low-risk and medium–high-risk patients (p > 0.05). Serum TMAO, zonulin, and I-FABP levels were positively correlated with TNF-α and IL-6 levels, and serum TMAO and I-FABP levels were positively correlated with triglyceride levels (p < 0.05). Moreover, serum zonulin and I-FABP levels were positively correlated with PAI (p < 0.05). Conclusions: Abdominal obesity and intestinal permeability may affect inflammatory processes and blood lipids in patients with T2DM. Further studies with large samples are needed to examine dietary factors related to the relationship between intestinal permeability and cardiometabolic risk. Full article

Shale gas reservoirs are currently a focus in exploration and development in China. However, they exhibit pronounced vertical heterogeneity, are influenced by numerous geological and engineering parameters, and present significant challenges for “sweet spot” identification. Traditional sweet spot identification methods mainly rely on geologists’ experience and judgment regarding individual influencing parameters, which inevitably introduces subjectivity and uncertainty. The rapid development of artificial intelligence technology offers an opportunity to address this issue. This study adopts a geology–engineering integration approach and, based on data integration and a multi-algorithm prediction ensemble model with deep learning, proposes a predictive model built on actual data from the Nanchuan Block of the Sichuan Basin. The model integrates the Tetrahedral Topology Optimization (TBO) algorithm, Extreme Gradient Boosting (XGBoost), and Geological Attribute Feature Mapping (GAFM), aiming to improve the accuracy of shale gas reservoir sweet spot identification more effectively. The results show that sweet spots are jointly influenced by geological, rock-mechanical, and hydraulic fracturing parameters. The primary reservoir property factors controlling post-fracture productivity include TOC, permeability, porosity, and gas saturation, while the main rock-mechanical controlling factors are Poisson’s ratio, Young’s modulus, brittleness index, and Bursting Pressure. Based on the analysis of these productivity-controlling factors, the proposed integrated AI learning model achieved a sweet spot identification accuracy of 88.5%, enabling precise identification of single-well sweet spot distribution. Full article

The permeability index (PI) is a key comprehensive indicator that reflects the smoothness of internal gas flow in pig iron production via blast furnace. An accurate prediction for it is essential for forecasting abnormal furnace conditions and preventing potential faults. However, developing an early prediction model for PI has been neglected in existing research, and it faces massive challenges due to the strong nonlinearity, undesirable nonstationarity, and significant multiscale time delays inherent in the blast furnace data. To bridge this gap, a new modeling paradigm for PI is proposed to explore the inherent time delay characteristics among multiple variables. First, the data are progressively decomposed into multiple components using wavelet decomposition and spike separation. Then, a novel delay extraction method based on wavelet coherence analysis is developed to obtain accurate multiscale time delay knowledge. Furthermore, the integration of Orthonormal Subspace Analysis (OSA) and wavelet neural network (WNN) achieves comprehensive modeling across time and frequency domains, incorporating global and local features. A Gauss–Markov-based fusion framework is also utilized to reduce the output error variance, ultimately enabling the early prediction of PI. Mechanism analysis and a practical case study on blast furnace production verify the effectiveness of the proposed target-oriented prediction framework. Full article