Search Results (136)

To address the high costs and inefficiencies of blind prospecting in deep geothermal exploration, this study develops a three-dimensional heat transfer model for quantitative prediction of geothermal enrichment targets. Unlike traditional qualitative or single-mechanism analyses, this research utilizes a finite element forward modeling approach based on step-faulted depressions (sedimentary basins/grabens) and uplifts (domes/uplift belts). We simulate temperature fields and heat flux distributions in multilayered systems incorporating four thermal conductivity types (A, K, H, Q). By systematically comparing the geometric heat flow convergence in depressions with the lateral diffusion in uplifts, this work reveals mirror and anti-mirror relationships between temperature fields and structural morphology at middle and deep levels, as well as local “hot spot” and “cold zone” effects. The results indicate that, in depressional structures, shallow high-temperature reservoirs (<2 km) are mainly concentrated in A- and K-types, while deeper reservoirs (>3 km) are enriched in Q- and H-types. In contrast, uplift structures are characterized by mid- to shallow-depth (<3 km) reservoirs predominantly in A- and K-types, with high temperatures at depth preferentially hosted in A- and H-types, and the highest temperatures observed in the A-type. Thermal conductivity contrasts, layer thicknesses, and structural morphology collectively control the spatial distribution of heat flux. A strong positive correlation between thermal conductivity and heat flux is observed at the central target area, significantly stronger than at the margins, whereas this relationship is notably weakened in Q-type. Crucially, low-conductivity zones display high geothermal gradients coupled with low terrestrial heat flow, disproving the axiom that “elevated geothermal gradients imply high heat flow,” thus establishing “high-gradient/low-heat-flow coupling zones” as strategic exploration targets. The model developed in this study demonstrates high simulation accuracy and computational efficiency. The findings provide a robust theoretical basis for reconstructing geothermal geological evolution and precise geothermal target localization, thereby reducing the risk of “blind heat exploration” and promoting the cost-effective and refined development of deep concealed geothermal resources. Full article

Carbon Capture and Storage (CCS) is a key technology for achieving carbon neutrality goals. Relevant foreign research began in the 1970s, but overall it remains in the exploration and demonstration stage. Clarifying the geological parameters and characteristics of reservoir–caprock systems in CCS projects is of great significance to the effectiveness and safety of long-term storage. By reviewing 15 typical global CCS projects, this paper identifies that ideal reservoirs are gently structured sandstones with few faults (characterized by high porosity, high permeability, and large scale, which are conducive to CO₂ diffusion) or basalts (which can react with CO₂ for mineralization, enabling permanent storage). Caprocks are mainly composed of thick mudstone and shale; composite caprocks consisting of multi-layer low-permeability formations and tight interlayers within reservoirs have stronger sealing performance. Additionally, they should be far from faults, and sufficient caprock thickness is required to reduce leakage risks. Meanwhile, this paper points out the challenges faced by CCS technology, such as complex site selection, limitations in long-term monitoring, difficulties in designing injection parameters, and challenges in large-scale deployment. It proposes suggestions including establishing a quantitative site selection system, building a comprehensive monitoring network, and strengthening collaborative optimization of parameters, so as to provide a basis for safe site selection and assessment. Full article

Marine sediments enriched with rare earth elements (REEs) serve as a significant reservoir, particularly for heavy REEs. Conventional lab-based REE exploration restricts rapid and large-scale assessment, whereas hyperspectral imaging provides a promising approach for quantitative evaluation. This study evaluates the capacity of hyperspectral data for the quantitative determination of REEs in marine sediments. A total of 53 samples from various locations were analyzed to determine their chemical composition and spectral characteristics within the 380–1000 nm range under natural light. The influence of surface conditions on spectral integrity was evaluated, and multiple preprocessing and spectral feature extraction methods were applied to enhance data reliability. This study proposes a novel approach, termed Feature Importance within Pearson Correlation Coefficient-Based High-Correlation Spectral Range (PCCR-FI), designed for the identification of characteristic spectral bands associated with REEs. Machine learning models were subsequently constructed to estimate REE concentrations, and the following key findings were observed: (a) technical adjustments effectively addressed variations in sediment surface conditions, ensuring data reliability. (b) The PCCR-FI technique identified characteristic REEs spectral bands, enhancing processing efficiency and prediction accuracy. (c) The integration of the reciprocal logarithmic first derivative (TLOG-FD) technique with a multilayer perceptron (MLP) model, termed TLOG-FD-MLP, efficiently captured critical spectral features, resulting in improved prediction accuracy. For light REEs, the model achieved coefficient of determination (R²) values exceeding 0.60 and relative performance deviation (RPD) values exceeding 1.60, with some elements demonstrating R² values as high as 0.81 with RPD values surpassing 2.00. Furthermore, several heavy REEs exhibited moderate prediction performance, with R² values consistently exceeding 0.60. When considering the total REE content, an R² of 0.73 and an RPD of 1.97 were achieved. These findings demonstrate the use of hyperspectral imaging as a viable tool for quantitative evaluation of REE concentrations in marine sediments, providing valuable guidance for resource mapping and the exploration of seafloor polymetallic deposits. Full article

Coalbed methane (CBM) reservoirs in southwestern China are characterized by thick, multi-layered coal sequences partitioned into several independent pressure systems by impermeable strata. Commingled production from multiple coal seams in such multi-superposed CBM systems often suffers from severe inter-layer interference, leading to suboptimal gas recovery. To address this challenge, we developed a systematic four-step optimization workflow integrating geological data screening, pressure compartmentalization analysis, and numerical reservoir simulation. The workflow identifies the key “main” coal seams and evaluates various co-production layer combinations to maximize gas recovery while minimizing negative interference. We applied this method to a CBM well (LC-C2) in the Western Guizhou–Eastern Yunnan region, which penetrates three discrete CBM pressure systems. In the case study, single-layer simulations first revealed that one seam (No. 7 + 8) contributed over 30% of the total gas potential, with a few other seams (e.g., No. 18, 13, 4, 16) providing moderate contributions and many seams yielding negligible gas. Guided by these results, we simulated five commingling scenarios of increasing complexity. The optimal scenario was to co-produce the seams from the two higher-pressure systems (a total of six seams) while excluding the low-pressure shallow seams. This optimal six-seam configuration achieved a 10-year cumulative gas production of approximately 2.53 × 10⁶ m³ (about 700 m³/day average)—roughly 75% higher than producing the main seam alone, and even about 15% greater than a scenario involving all available seams. In contrast, including all three pressure systems (ten seams) led to interference effects where the high-pressure seams dominated flow and the low-pressure seams contributed little, resulting in lower overall recovery. The findings demonstrate that more is not always better in multi-seam CBM production. By intelligently selecting a moderate number of compatible seams for co-production, the reservoir’s gas can be extracted more efficiently. The proposed quantitative optimization approach provides a practical tool for designing multi-seam CBM wells and can be broadly applied to similar geologically compartmentalized reservoirs. Full article

To address the challenges of characterizing commingled production from multiple channel sand layers with varying boundaries and shapes in tight gas reservoirs, a novel Rate Transient Analysis (RTA) model was established based on the principle of equivalent seepage volume (ESV). This model enables the determination of boundary sizes and permeabilities of individual channel sand layers within commingled tight reservoirs using modern production decline analysis theory. The production decline behavior under different channel sizes, numbers, and configurations was systematically investigated through type curve analysis. The results reveal the existence of five distinct stages in the production decline curves for unequal-width channel sands. The intermediate transient flow stage serves as a diagnostic indicator for identifying boundary disparities among layers. Furthermore, reservoirs with smaller boundary distances, fewer wide channel sand layers, and lower thickness proportions of wider channels exhibit poorer productivity and tend to experience accelerated production decline during early and middle transient flow stages. The proposed method provides an effective approach for characterizing boundary parameters of commingled tight reservoirs and offers a theoretical foundation for evaluating individual layer contributions and productivity. Full article

Hydraulic fracturing technology significantly enhances reservoir conductivity by creating artificial fractures, serving as a crucial means for the economically viable development of low-permeability reservoirs. Accurate prediction of post-fracturing productivity is essential for optimizing fracturing parameter design and establishing scientific production strategies. However, current limitations in understanding post-fracturing production dynamics and the lack of efficient prediction methods severely constrain the evaluation of fracturing effectiveness and the adjustment of development plans. This study proposes a machine learning-based method for predicting post-fracturing productivity in multi-layer commingled production wells and validates its effectiveness using a key block from the PetroChina North China Huabei Oilfield Company. During the data preprocessing stage, the three-sigma rule, median absolute deviation, and density-based spatial clustering of applications with noise were employed to detect outliers, while missing values were imputed using the K-nearest neighbors method. Feature selection was performed using Pearson correlation coefficient and variance inflation factor, resulting in the identification of twelve key parameters as input features. The coefficient of determination served as the evaluation metric, and model hyperparameters were optimized using grid search combined with cross-validation. To address the multi-layer commingled production challenge, seven distinct datasets incorporating production parameters were constructed based on four geological parameter partitioning methods: thickness ratio, porosity–thickness product ratio, permeability–thickness product ratio, and porosity–permeability–thickness product ratio. Twelve machine learning models were then applied for training. Through comparative analysis, the most suitable productivity prediction model for the block was selected, and the block’s productivity patterns were revealed. The results show that after training with block-partitioned data, the accuracy of all models has improved; further stratigraphic subdivision based on block partitioning has led the models to reach peak performance. However, data volume is a critical limiting factor—for blocks with insufficient data, stratigraphic subdivision instead results in a decline in prediction performance. Full article

Reservoir evaporation is a vital component of the hydrological cycle and presents considerable challenges for sustainable water management, especially in arid and semi-arid regions. This study assesses the effectiveness of two Artificial Intelligence (AI) methods: Multilayer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), a combination ANN with fuzzy logic, in estimating daily evaporation from a large reservoir in a semi-arid region. Using eight years of hydrometeorological data from a nearby station, the study employed the ReliefF algorithm as a feature selection method for relevant input variables. The dataset was divided into training, validation, and testing subsets with 5% and 10% validation ratios, using four train–test splits of 70:30, 75:25, 80:20, and 85:15. Various training algorithms (e.g., Levenberg–Marquardt) and membership functions (e.g., generalized bell-shaped functions) were tested for both models. MLP consistently outperformed ANFIS on the test sets, showing higher R² and lower RMSE values. In the best-performing 70:30 split, MLP achieved an R² of 0.8069 and RMSE of 0.0923, compared to ANFIS with an R² of 0.3192 and RMSE of 0.2254. The findings highlight the AI-based approaches’ potential to support improved evaporation forecasting and integration into decision support tools for water resource planning amid changing climatic conditions. Full article

This study proposes a multi-task learning-based production capacity prediction model aimed at improving the prediction accuracy for gas wells in multi-layer tight sandstone reservoirs of the Sulige gas field under small-sample conditions. The model integrates mutation theory and progressive hierarchical feature extraction to achieve adaptive nonlinear feature extraction and autonomous feature selection tailored to different prediction tasks. Using the daily average production of each gas-bearing layer during the first month after well commencement and the cumulative production of each gas-bearing layer over the first year as targets, the model was applied to predict the production capacity of 66 gas wells. Compared with single-task models and classical machine learning methods, the proposed multi-task model significantly improves prediction accuracy, reducing the root mean squared error (RMSE) by over 40% and increasing the coefficient of determination (R²) to 0.82. Experimental results demonstrate the model’s effectiveness in environments with limited training data, offering a reliable approach for productivity prediction in complex multi-layer tight sandstone reservoirs. Full article

The aim of this study is to compare the performance of the multi-layer and the single-layer CO₂ injection methods used in offshore carbon capture and storage (CCS) through TOUGH-FLAC numerical simulations. Four key indicators, namely CO₂ saturation, pore pressure, vertical displacement, and Coulomb Failure Stress (CFS), are employed as indices to assess the storage capacity of reservoirs and the mechanical stability of caprocks. Numerical simulation results show that the multi-layer injection method increases the CO₂ migration distance and reduces CFS values compared with the single-layer injection method. After 1 year of injection, the combined CO₂ migration distance across two aquifers in Case 3 is 610 m, which is greater than that obtained using single-layer injection in Cases 1 and 2 (350 m and 380 m, respectively). Additionally, deep saline aquifers demonstrate superior CO₂ storage capacity due to higher overburden pressure, which also reduces the risk of caprock failures. After 30 years of injection, in Cases 1 and 2, the maximum CFS values are 0.591 and 0.567, respectively, and the CO₂ migration distances are 2400 m and 2650 m, respectively. Overall, the findings of this study indicate that the multi-layer injection method, particularly in deep saline aquifers, provides a safer and more efficient CO₂ injection approach for offshore CCS projects. Full article

This review explores the biofilm architecture and drug resistance of Enterococcus faecalis in clinical and environmental settings. The biofilm in E. faecalis is a heterogeneous, three-dimensional, mushroom-like or multilayered structure, characteristically forming diplococci or short chains interspersed with water channels for nutrient exchange and waste removal. Exopolysaccharides, proteins, lipids, and extracellular DNA create a protective matrix. Persister cells within the biofilm contribute to antibiotic resistance and survival. The heterogeneous architecture of the E. faecalis biofilm contains both dense clusters and loosely packed regions that vary in thickness, ranging from 10 to 100 µm, depending on the environmental conditions. The pathogenicity of the E. faecalis biofilm is mediated through complex interactions between genes and virulence factors such as DNA release, cytolysin, pili, secreted antigen A, and microbial surface components that recognize adhesive matrix molecules, often involving a key protein called enterococcal surface protein (Esp). Clinically, it is implicated in a range of nosocomial infections, including urinary tract infections, endocarditis, and surgical wound infections. The biofilm serves as a nidus for bacterial dissemination and as a reservoir for antimicrobial resistance. The effectiveness of first-line antibiotics (ampicillin, vancomycin, and aminoglycosides) is diminished due to reduced penetration, altered metabolism, increased tolerance, and intrinsic and acquired resistance. Alternative strategies for biofilm disruption, such as combination therapy (ampicillin with aminoglycosides), as well as newer approaches, including antimicrobial peptides, quorum-sensing inhibitors, and biofilm-disrupting agents (DNase or dispersin B), are also being explored to improve treatment outcomes. Environmentally, E. faecalis biofilms contribute to contamination in water systems, food production facilities, and healthcare environments. They persist in harsh conditions, facilitating the spread of multidrug-resistant strains and increasing the risk of transmission to humans and animals. Therefore, understanding the biofilm architecture and drug resistance is essential for developing effective strategies to mitigate their clinical and environmental impact. Full article

The synergistic development of low-permeability reservoirs such as deep coalbed methane (CBM) and tight gas has emerged as a key technology to reduce development costs, enhance single-well productivity, and improve gas recovery. However, due to fundamental differences between coal seams and tight sandstones in their pore structure, permeability, water saturation, and pressure sensitivity, significant variations exist in their flow capacities and fluid production behaviors. To address the challenges of production allocation and main reservoir identification in the co-development of CBM and tight gas within deep gas-bearing basins, this study employs the transient multiphase flow simulation software OLGA to construct a representative dual-gas co-production well model. The regulatory mechanisms of the gas–liquid distribution, deliquification efficiency, and interlayer interference under two typical vertical stacking relationships—“coal over sand” and “sand over coal”—are systematically analyzed with respect to different tubing setting depths. A high-precision dynamic production allocation method is proposed, which couples the wellbore structure with real-time monitoring parameters. The results demonstrate that positioning the tubing near the bottom of both reservoirs significantly enhances the deliquification efficiency and bottomhole pressure differential, reduces the liquid holdup in the wellbore, and improves the synergistic productivity of the dual-reservoirs, achieving optimal drainage and production performance. Building upon this, a physically constrained model integrating real-time monitoring data—such as the gas and liquid production from tubing and casing, wellhead pressures, and other parameters—is established. Specifically, the model is built upon fundamental physical constraints, including mass conservation and the pressure equilibrium, to logically model the flow paths and phase distribution behaviors of the gas–liquid two-phase flow. This enables the accurate derivation of the respective contributions of each reservoir interval and dynamic production allocation without the need for downhole logging. Validation results show that the proposed method reliably reconstructs reservoir contribution rates under various operational conditions and wellbore configurations. Through a comparison of calculated and simulated results, the maximum relative error occurs during abrupt changes in the production capacity, approximately 6.37%, while for most time periods, the error remains within 1%, with an average error of 0.49% throughout the process. These results substantially improve the timeliness and accuracy of the reservoir identification. This study offers a novel approach for the co-optimization of complex multi-reservoir gas fields, enriching the theoretical framework of dual-gas co-production and providing technically adaptive solutions and engineering guidance for multilayer unconventional gas exploitation. Full article

The continuous variability in the microbiological quality of surface waters presents significant challenges for ensuring the production of safe drinking water in compliance with public health regulations. Inadequate treatment of surface waters can lead to the presence of pathogenic microorganisms in the drinking water supply, posing serious risks to public health. This research presents an in-depth data analysis using machine learning tools for the induction of models to describe and predict microbiological water quality for the sustainable management of the Butoniga drinking water treatment facility in Istria (Croatia). Specifically, descriptive and predictive models for total coliforms and E. coli bacteria (i.e., classes), which are recognized as key sanitary indicators of microbiological contamination under both EU and Croatian water quality legislation, were developed. The descriptive models provided useful information about the main environmental factors that influence the microbiological water quality. The most significant influential factors were found to be pH, water temperature, and water turbidity. On the other hand, the predictive models were developed to estimate the concentrations of total coliforms and E. coli bacteria seven days in advance using several machine learning methods, including model trees, random forests, multi-layer perceptron, bagging, and XGBoost. Among these, model trees were selected for their interpretability and potential integration into decision support systems. The predictive models demonstrated satisfactory performance, with a correlation coefficient of 0.72 for total coliforms, and moderate predictive accuracy for E. coli bacteria, with a correlation coefficient of 0.48. The resulting models offer actionable insights for optimizing operational responses in water treatment processes based on real-time and predicted microbiological conditions in the Butoniga reservoir. Moreover, this research contributes to the development of predictive frameworks for microbiological water quality management and highlights the importance of further research and monitoring of this key aspect of the preservation of the environment and public health. Full article

In the global energy sector, water-bearing reservoir-typed gas storage accounts for about 30% of underground gas storage (UGS) reservoirs and is vital for natural gas storage, balancing gas consumption, and ensuring energy supply stability. However, when constructing the UGS in the M gas reservoir, selecting suitable areas poses a challenge due to the complicated gas–water distribution in the multi-layered water-bearing gas reservoir with a long production history. To address this issue and enhance energy storage efficiency, this study presents an integrated geomechanical-hydraulic assessment framework for choosing optimal UGS construction horizons in multi-layered water-bearing gas reservoirs. The horizons and sub-layers of the gas reservoir have been quantitatively assessed to filter out the favorable areas, considering both aspects of geological characteristics and production dynamics. Geologically, caprock-sealing capacity was assessed via rock properties, Shale Gouge Ratio (SGR), and transect breakthrough pressure. Dynamically, water invasion characteristics and the water–gas distribution pattern were analyzed. Based on both geological and dynamic assessment results, the favorable layers for UGS construction were selected. Then, a compositional numerical model was established to digitally simulate and validate the feasibility of constructing and operating the M UGS in the target layers. The results indicated the following: (1) The selected area has an SGR greater than 50%, and the caprock has a continuous lateral distribution with a thickness range from 53 to 78 m and a permeability of less than 0.05 mD. Within the operational pressure ranging from 8 MPa to 12.8 MPa, the mechanical properties of the caprock shale had no obvious changes after 1000 fatigue cycles, which demonstrated the good sealing capacity of the caprock. (2) The main water-producing formations were identified, and the sub-layers with inactive edge water and low levels of water intrusion were selected. After the comprehensive analysis, the I-2 and I-6 sub-layer in the M 8 block and M 14 block were selected as the target layers. The numerical simulation results indicated an effective working gas volume of 263 million cubic meters, demonstrating the significant potential of these layers for UGS construction and their positive impact on energy storage capacity and supply stability. Full article

Classical epidemic models treat vaccine uptake as an exogenous parameter, yet real-world coverage emerges from strategic choices made by individuals facing uncertain risks. During the last two decades, vaccination games, which combine epidemic dynamics with game theory, behavioural economics, and network science, have become a very important tool for analysing this problem. Here, we synthesise more than 80 theoretical, computational, and empirical studies to clarify how population structure, psychological perception, pathogen complexity, and policy incentives interact to determine vaccination equilibria and epidemic outcomes. Papers are organised along five methodological axes: (i) population topology (well-mixed, static and evolving networks, multilayer systems); (ii) decision heuristics (risk assessment, imitation, prospect theory, memory); (iii) additional processes (information diffusion, non-pharmacological interventions, treatment, quarantine); (iv) policy levers (subsidies, penalties, mandates, communication); and (v) pathogen complexity (multi-strain, zoonotic reservoirs). Common findings across these studies are that voluntary vaccination is almost always sub-optimal; feedback between incidence and behaviour can generate oscillatory outbreaks; local network correlations amplify free-riding but enable cost-effective targeted mandates; psychological distortions such as probability weighting and omission bias materially shift equilibria; and mixed interventions (e.g., quarantine + vaccination) create dual dilemmas that may offset one another. Moreover, empirical work surveys, laboratory games, and field data confirm peer influence and prosocial motives, yet comprehensive model validation remains rare. Bridging the gap between stylised theory and operational policy will require data-driven calibration, scalable multilayer solvers, and explicit modelling of economic and psychological heterogeneity. This review offers a structured roadmap for future research on adaptive vaccination strategies in an increasingly connected and information-rich world. Full article

Geological storage of CO₂ in offshore deep saline aquifers is widely recognized as an effective strategy for large-scale carbon emission reduction. This study aims to assess the mechanical integrity and storage efficiency of reservoirs using a multi-layer CO₂ injection method in the Enping 15-1 Oilfield CO₂ storage project which is the China’s first offshore carbon capture, utilization, and storage (CCUS) demonstration. A coupled Hydro–Mechanical (H–M) model is constructed using the TOUGH-FLAC simulator to simulate a 10-year CO₂ injection scenario, incorporating six vertically distributed reservoir layers. A sensitivity analysis of 14 key geological and geomechanical parameters is performed to identify the dominant factors influencing injection safety and storage capacity. The results show that a total injection rate of 30 kg/s can be sustained over a 10-year period without exceeding mechanical failure thresholds. Reservoirs 3 and 4 exhibit the greatest lateral CO₂ migration distances over the 10-year injection period, indicating that they are the most suitable target layers for CO₂ storage. The sensitivity analysis further reveals that the permeability of the reservoirs and the friction angle of the reservoirs and caprocks are the most critical parameters governing injection performance and mechanical stability. Full article