Search Results (48)

As a low-carbon and clean energy source, Coalbed methane (CBM) is of great significance in reducing greenhouse gas emissions, optimizing the energy structure, safeguarding mine safety, and promoting the transformation to a green economy to achieve sustainable development. Coalbed methane (CBM) in Xinjiang’s steeply dipping coal seams is abundant but difficult to predict due to complex geology and distinct gas flow behaviors, making traditional methods ineffective. This study proposes GCN-BiGRU, a parallel dual-module model integrating seepage mechanics, reservoir engineering, geological structures, and production history. The GCN module models wells as nodes, using geological attributes and spatial distances to capture inter-well interference; the BiGRU module extracts temporal dependencies from production sequences. An adaptive fusion mechanism dynamically combines spatiotemporal features for robust prediction. Validated on Baiyanghe block data, the model achieved MAE 59.04, RMSE 94.25, and improved accuracy from 64.47% to 92.8% as training wells increased from 20 to 84. It also showed strong transferability to independent sub-regions, enabling real-time prediction and scenario analysis for CBM development and reservoir management. Full article

Addressing the challenge of sulfonated lignite (SL) removal from oilfield wastewater, this study introduces a novel hierarchical MgFe-layered double hydroxide (LDH) adsorbent. The material was fabricated via in situ co-precipitation, utilizing a template formed by the NaCl-induced co-assembly of oleylaminopropyl betaine (OAPB) and sodium dodecyl sulfate (SLS) into zwitterionic, anionic, shear-responsive viscoelastic gels. This gel-templating approach yielded an LDH structure featuring a hierarchical pore network spanning 1–80 nm and a notably high specific surface area of 199.82 m²/g, as characterized by SEM and BET. The resulting MgFe-LDH demonstrated exceptional efficacy, achieving a SL removal efficiency exceeding 96% and a maximum adsorption capacity of 90.68 mg/g at neutral pH. Adsorption kinetics were best described by a pseudo-second-order model (R² > 0.99), with intra-particle diffusion identified as the rate-determining step. Equilibrium adsorption data conformed to the Langmuir isotherm, signifying monolayer uptake. Thermodynamic analysis confirmed the process was spontaneous (ΔG < 0) and exothermic (ΔH = −20.09 kJ/mol), driven primarily by electrostatic interactions and ion exchange. The adsorbent exhibited robust recyclability, maintaining over 79% of its initial capacity after three adsorption–desorption cycles. This gel-directed synthesis presents a sustainable pathway for developing high-performance adsorbents targeting complex contaminants in oilfield effluents. Full article

Compared with conventional gas reservoirs, deep shale gas reservoirs are characterized by developed faults and fractures, strong heterogeneity, high stress sensitivity, and complex in situ stress distribution. To address traditional 3D static models’ inability to predict in situ stress changes in strongly heterogeneous reservoirs during fracturing, this study takes the deep shale gas in the Zigong block of the Sichuan Basin as an example. By comprehensively considering the heterogeneity and anisotropy of geomechanical parameters and natural fractures in shale gas reservoirs, a 4D in situ stress multi-physics coupling model for shale gas reservoirs based on geology–engineering integration is established. Through coupling geomechanical parameters with fracturing operation data, the dynamic evolution laws of multi-scale stress fields from single-stage to platform-scale during large-scale fracturing of horizontal wells in deep shale gas reservoirs are systematically studied. The research results show the following: (1) The fracturing process has a significant impact on the magnitude and direction of the stress field. With the injection of fracturing fluid, both the minimum and maximum horizontal principal stresses increase, with the minimum horizontal principal stress rising by 1.8–6.4 MPa and the maximum horizontal principal stress by 1.1–3.2 MPa; near the wellbore, there is an obvious deflection in the direction of in situ stress. (2) As the number of fracturing stages increases, the minimum horizontal principal stress shows an obvious cumulative growth trend, with a more significant increase in the later stages, and there is a phenomenon of stress accumulation along the wellbore, with the stress difference decreasing from 15 MPa to 11 MPa. (3) The on-site adoption of the fracturing operation method featuring overall flush advancement and inter-well staggered fracture placement has achieved good stress balance; comparative analysis shows that the stress communication degree of the 400 m well spacing is weaker than that of the 300 m well spacing. This study provides a more reasonable simulation method for large-scale fracturing development of deep shale gas, which can more accurately predict and evaluate the dynamic stress field changes during fracturing, thereby guiding fracturing operations in actual production. Full article

Relative permeability, as a key parameter characterizing multiphase fluid flow behavior in porous media, holds significant importance across various fields, such as reservoir evaluation and engineering geology. However, measuring relative permeability is time-consuming and costly. Considering the analogy between fluid flow and electrical current conduction provides a novel approach for studying multiphase flow characteristics using resistivity data. An integrated oil–water relative permeability and resistivity co-measurement experiment was specifically designed for a complex argillaceous sandstone reservoir in a block, referred to as Block A. Research has shown that as the resistance coefficient increases, the water and oil relative permeability decrease and increase, respectively. As the porosity–permeability comprehensive index increases and the shale content decreases, corresponding to the same resistance coefficient, the water and oil relative permeability show increasing and decreasing trends, respectively. The integration of tortuous capillary tube theory and three-water model concepts, combined with the flow-current similarity principle, has enabled the development of a novel relative permeability–resistivity correlation model that is applicable to complex argillaceous sandstone formations. The application of actual data from the study area shows that the relative errors of the water- and oil-phase relative permeability calculated by the proposed model are both small, at 16% and 8.6%, respectively. The model is validated to better characterize multiphase fluid flow in rocks, offering a new approach for accurately calculating relative permeability based on formation resistivity data. Full article

The classification of submarine topography and geomorphology is essential for marine resource exploitation and ocean engineering, with wide-ranging implications in marine geology, disaster assessment, resource exploration, and autonomous underwater navigation. Submarine landscapes are highly complex and diverse. Traditional visual interpretation methods are not only inefficient and subjective but also lack the precision required for high-accuracy classification. While many machine learning and deep learning models have achieved promising results in image classification, limited work has been performed on integrating backscatter and bathymetric data for multi-source processing. Existing approaches often suffer from high computational costs and excessive hyperparameter demands. In this study, we propose a novel approach that integrates pruning-enhanced ConDenseNet with label smoothing regularization to reduce misclassification, strengthen the cross-entropy loss function, and significantly lower model complexity. Our method improves classification accuracy by 2% to 10%, reduces the number of hyperparameters by 50% to 96%, and cuts computation time by 50% to 85.5% compared to state-of-the-art models, including AlexNet, VGG, ResNet, and Vision Transformer. These results demonstrate the effectiveness and efficiency of our model for multi-source submarine topography classification. Full article

Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology first employs the Maximum Overlap Discrete Wavelet Transform (MODWT) to denoise raw Global Navigation Satellite System (GNSS)-monitored displacement time series data, enhancing the underlying deformation features. Subsequently, a geology-aware graph is constructed, using the temporal correlation of displacement series as a practical proxy for physical relatedness between monitoring nodes. The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. Experiments conducted on a real-world dataset from an active open-pit mine demonstrate the framework’s superior performance. The DCRNN-proposed model achieved the highest accuracy among eight competing models, recording a Root Mean Square Error (RMSE) of 2.773 mm in the Vertical direction, a 39.1% reduction compared to its baseline. This study validates that the proposed dynamic graph optimization approach provides a robust and significantly more accurate solution for landslide prediction in complex, real-world engineering environments. Full article

In the Kelameili volcanic gas reservoir, primary hydraulic fracturing treatments in some wells take place on a limited scale, resulting in a rapid decline in production post stimulation and necessitating re-fracturing operations. However, prolonged production has led to a significant evolution in the in situ stress field, which complicates the design of re-fracturing parameters. To address this, this study adopts an integrated geology–engineering approach to develop a formation-specific geomechanical model, using rock mechanical test results and well-log inversion to reconstruct the reservoir’s initial stress field. The dynamic stress field simulations and re-fracturing parameter optimization were performed for Block Dixi-14. The results show that stress superposition effects induced by multiple fracturing stages and injection–production cycles have significantly altered the current in situ stress distribution. For Well K6, the optimized re-fracturing parameters comprised a pump rate of 12 m³/min, total fluid volume of 1200 m³, prepad fluid ratio of 50–60%, and proppant volume of 75 m³, and the daily gas production increased by 56% correspondingly, demonstrating the effectiveness of the optimized re-fracturing design. This study not only provides a more realistic simulation framework for fracturing volcanic rock gas reservoirs but also offers a scientific basis for fracture design optimization and enhanced gas recovery. The geology–engineering integrated methodology enables the accurate prediction and assessment of dynamic stress field evolution during fracturing, thereby guiding field operations. Full article

With the rapid urbanization and increasing development of underground spaces, foundation pit groups in complex geological environments encounter considerable challenges in deformation control. These challenges are especially prominent in cases of adjacent constructions, complex geology, and environmentally sensitive areas. Nevertheless, existing research is lacking in systematic analysis of construction sequencing and the interaction mechanisms between foundation pit groups. This results in gaps in comprehending stress redistribution and optimal excavation strategies for such configurations. To address these gaps, this study integrates physical model tests and PLAXIS 3D numerical simulations to explore the Nanjing Jiangbei New District Phase II pit groups. It concentrates on deformations in segmented and adjacent configurations under varying excavation sequences and spacing conditions. Key findings reveal that simultaneous excavation in segmented pit groups optimizes deformation control through symmetrical stress relief via bilateral unloading, reducing shared diaphragm wall displacement by 18–25% compared to sequential methods. Sequential excavations induce complex soil stress redistribution from asymmetric unloading, with deep-to-shallow sequencing minimizing exterior wall deformation (≤0.12%He). For adjacent foundation pit groups, simultaneous excavation achieves minimum displacement interference, while phased construction requires prioritizing large-section excavation first to mitigate cumulative deformations through optimized stress transfer. When the spacing-to-depth ratio (B/He) is below 1, horizontal displacements of retaining structures increase by 43% due to spacing effects. This study quantifies the effects of excavation sequences and spacing configurations on pit group deformation, establishing a theoretical framework for optimizing construction strategies and enhancing retaining structure stability. The findings are highly significant for underground engineering design and construction in complex urban geological settings, especially in high-density areas with spatial and geotechnical constraints. Full article

CO₂ geo-storage is a promising approach in reducing greenhouse gas emissions and controlling global temperature rise. Although numerous studies have reported that offshore saline aquifers have greater storage potential and safety, current suitability evaluation models for CO₂ geo-storage primarily focus on onshore saline aquifers, and site-level evaluations for offshore CO₂ geo-storage remain unreported. In this study, we propose a framework to evaluate the site-level offshore CO₂ geo-storage suitability with a multi-tiered indicator system, which considers three types of factors: engineering geology, storage potential, and socio-economy. Compared to the onshore CO₂ geo-storage suitability evaluation models, the proposed indicator system considers the unique conditions of offshore CO₂ geo-storage, including water depth, offshore distance, and distance from drilling platforms. The Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) methods were integrated and applied to the analysis of the Ying–Qiong Basin, South China Sea. The results indicated that the average suitability score in the Yinggehai Basin (0.762) was higher than that in the Qiongdongnan Basin (0.691). This difference was attributed to more extensive fault development in the Qiongdongnan Basin, suggesting that the Yinggehai Basin is more suitable for CO₂ geo-storage. In addition, the DF-I reservoir in the Yinggehai Basin and the BD-A reservoir in the Qiongdongnan Basin were selected as the optimal CO₂ geo-storage targets for the two sub-basins, with storage potentials of 1.09 × 10⁸ t and 2.40 × 10⁷ t, respectively. This study advances the methodology for assessing site-level potential of CO₂ geo-storage in offshore saline aquifers and provides valuable insights for engineering applications and decision-making in future CO₂ geo-storage projects in the Ying–Qiong Basin. Full article

Shear wave velocity (V_s) is a key geomechanical variable in subsurface exploration, essential for hydrocarbon reservoirs, geothermal reserves, aquifers, and emerging use cases, like carbon capture and storage (CCS), offshore geohazard assessment, and deep Earth exploration. Despite its broad significance, no comprehensive multidisciplinary review has evaluated the latest applications, estimation methods, and challenges in V_s prediction. This study provides a critical review of these aspects, focusing on energy-efficient prediction techniques, including geophysical surveys, remote sensing, and artificial intelligence (AI). AI-driven models, particularly machine learning (ML) and deep learning (DL), have demonstrated superior accuracy by capturing complex subsurface relationships and integrating diverse datasets. While AI offers automation and reduces reliance on extensive field data, challenges remain, including data availability, model interpretability, and generalization across geological settings. Findings indicate that integrating AI with geophysical and remote sensing methods has the potential to enhance V_s prediction, providing a cost-effective and sustainable alternative to conventional approaches. Additionally, key challenges in V_s estimation are identified, with recommendations for future research. This review offers valuable insights for geoscientists and engineers in petroleum engineering, mining, geophysics, geology, hydrogeology, and geotechnics. Full article

The Qinghai–Tibet Plateau is one of the most geologically active regions in the world, characterized by significant geomorphic variation and a wide range of geological hazards. The multifactorial coupling of tectonic movements, geomorphological evolution, climate variability, and lithological characteristics contributes to the pronounced spatial heterogeneity of the disaster-inducing environment. Identifying key controlling factors and their driving mechanisms is crucial for effective regional disaster prevention and mitigation. This study adopts a systematic framework based on regional disaster systems theory, integrating tectonic activity, engineering geology, topography, and precipitation to construct a multi-factor zoning system. Using the Random Forest model, we quantify factor contributions and delineate eight distinct disaster-inducing environment zones. Zones I–III (Himalayas–Hengduan Mountains–Qilian Mountains) are characterized by a dominant coupling mechanism of “tectonic fragmentation—topographic relief—precipitation erosion” and account for the majority of large-scale disasters. In contrast, Zones IV–VIII, primarily located in the central–western Plateau basins, are constrained by limited material sources, resulting in lower disaster densities. The findings indicate that geological structures and lithological fragmentation provide the material foundation for hazard occurrence, while topographic potential and hydrodynamic forces serve as critical triggering conditions. This nonlinear coupling of factors shapes a disaster geographic pattern characterized by “dense in the east and sparse in the west”. Based on these results, the targeted recommendations proposed offer valuable theoretical insights and methodological guidance for disaster mitigation and region-specific management across the Qinghai–Tibet Plateau. Full article

The exploration and development of shale oil offers significant potential as an alternative to address oil and gas supply. The complex geological conditions characterized by inter-lithology and strong heterogeneity pose substantial challenges in fracturing reconstruction. This paper established an integrated geoengineering model of Gulong shale in the Songliao Basin using multi-source data to accurately characterize the vertical and lateral heterogeneity of the reservoir. Based on the reconstruction economy, the compressibility evaluation of different reservoirs showed that the advantage of reconstruction was highest in the Q9 reservoir and lowest in the Q3 reservoir. Analysis of the fluctuation in the reservoir compressibility and lithology characteristics revealed limited fracture reconstruction volume in highly plastic reservoirs with elevated mudstone concentrations, which affected reconstruction efficacy. The number of clusters could be increased while the spacing between the clusters could be reduced in the lithologically brittle region. In areas characterized by strong lithology and plasticity, the number of clusters was appropriately decreased while the inter-cluster spacing increased. The research results allow local adjustments according to the logging curve, inform differentiated decisions on fracturing reconstruction, and support the efficient fracturing reconstruction of the Gulong shale oil reservoir. Full article

The study block is located on the eastern edge of the Ordos Basin and is one of the typical medium coalbed methane blocks in China that have previously been subjected to exploration and development work. The rich CBM resource base and good exploration and development situation in this block mean there is an urgent need to accelerate development efforts, but compared with the current situation for tight sandstone gas where development is in full swing in the area, the production capacity construction of CBM wells in the area shows a phenomenon of lagging to a certain degree. In this study, taking the 4 + 5 coal seam of the YJP block in the Ordos Basin as the research object, we carried out technical research on an integrated program concerning CBM geology and engineering and put forward a comprehensive seismic geology analysis method for the prediction of the CBM content. The study quantitatively assessed the tectonic conditions, depositional environment, and coal seam thickness as potential controlling factors using gray relationship analysis, trend surface analysis, and seismic geological data integration. The results show that tectonic conditions, especially the burial depth, residual deformation, and fault development, are the main controlling factors affecting the coalbed methane content, showing a strong correlation (gray relational value greater than 0.75). The effects of the depositional environment (sand–shale ratio) and coal bed thickness were negligible. A weighted fusion model incorporating seismic attributes and geological parameters was developed to predict the gas content distribution, achieving relative prediction errors of below 15% in validation wells, significantly outperforming traditional interpolation methods. The integrated approach demonstrated enhanced spatial resolution and accuracy in delineating the lateral CBM distribution, particularly in structurally complex zones. However, limitations persist due to the seismic data resolution and logging data reliability. This method provides a robust framework for CBM exploration in heterogeneous coal reservoirs, emphasizing the critical role of tectonic characterization in gas content prediction. Full article

The permanently shadowed regions of the lunar South Pole have become a key target for international lunar exploration due to their unique scientific value and engineering challenges. In order to effectively screen suitable landing zones near the lunar South Pole, this research proposes a comprehensive evaluation method based on a self-organizing map (SOM). Using multi-source remote sensing data, the method classifies and analyzes candidate landing zones by combining scientific purposes (such as hydrogen abundance, iron oxide abundance, gravity anomalies, water ice distance analysis, and geological features) and engineering constraints (such as Sun visibility, Earth visibility, slope, and roughness). Through automatic clustering, the SOM model finds the important regions. Subsequently, it integrates with a supervised learning model, a random forest, to determine the feature importance weights in more detail. The results from the research indicate the following: the areas suitable for landing account for 9.05%, 5.95%, and 5.08% in the engineering, scientific, and synthesized perspectives, respectively. In the weighting analysis of the comprehensive data, the weights of Earth visibility, hydrogen abundance, kilometer-scale roughness, and slope data all account for more than 10%, and these are thought to be the four most important factors in the automated site selection process. Furthermore, the kilometer-scale roughness data are more important in the comprehensive weighting, which is in line with the finding that the kilometer-scale roughness data represent both surface roughness from an engineering perspective and bedrock geology from a scientific one. In this study, a local examination of typical impact craters is performed, and it is confirmed that all 10 possible landing sites suggested by earlier authors are within the appropriate landing range. The findings demonstrate that the SOM-model-based analysis approach can successfully assess lunar South Pole landing areas while taking multiple constraints into account, uncovering spatial distribution features of the region, and offering a rationale for choosing desired landing locations. Full article

With rapid urbanization, the utilization of underground space has become an important part of infrastructure. However, the stability of underground spaces such as large caverns remains a key challenge in civil engineering throughout the lifecycle of a project. Traditional methods of stability assessment rely on static models and periodic monitoring and often fail to capture real-time changes in rock behavior, leading to potential safety risks and, in severe cases, even the collapse of underground infrastructure. To address this challenge, this study introduces a digital twin (DT) framework to improve stability assessments and monitor deformations in underground structures. The framework enables the continuous monitoring and adaptive optimization of rock support systems by combining real-time sensor data with virtual simulations. A five-dimensional DT framework comprises physical objects, virtual objects, service systems, DT data, and their interconnections. It incorporates six key modules, which are structure, geology, material, behavior, performance, and environment, to enhance the understanding of cavern stability. The framework is based on Industry Foundation Classes standards to ensure seamless data exchange, interoperability, and the standardized representation of geotechnical and structural data. A seven-step methodology is developed for this framework, encompassing geological assessment, virtual modeling, Building Information Modeling (BIM)-based design, construction processes, real-time monitoring, and optimization strategies. To evaluate its effectiveness, the framework is applied to a case study, demonstrating improvements in deformation monitoring and rock support efficiency. The findings highlight the potential of integrating DT with BIM to enhance safety, reliability, and long-term stability in underground construction projects. Full article