Search Results (262)

The high water cut period represents a critical phase in the development of tight oil wells, and accurately forecasting productivity during this stage is essential for effective oilfield development planning. However, traditional reservoir engineering methods find it difficult to handle complex oil-water seepage behaviors and cannot accurately predict the productivity of tight sandstone oil wells in the high water cut period. Therefore, this paper proposes a method for predicting the productivity of tight oil reservoirs based on a hybrid deep learning algorithm, using the geological, engineering, and development parameters of 342 fractured horizontal wells in the Z211 block of Heshui Oilfield. The model was based on the KAN deep learning algorithm, and the WOA meta-heuristic optimization algorithm was used to optimize the KAN model parameters. Combined with multi-dimensional parameters such as oil well geology, engineering and development, an efficient and accurate productivity prediction model was established. Based on the interpretability of the model itself, the key features of the model and the factors affecting productivity are explained in combination with the SHAP (SHapley Additive exPlanations) value and the Pearson coefficient, revealing the changing relationship of productivity and the degree of influence of different parameters on productivity. The results indicate that the KAN-WOA model demonstrates strong performance in both prediction accuracy and robustness for productivity forecasting. For high water cut fractured horizontal wells in tight oil reservoirs, water content and permeability were identified as the primary influencing factors on initial productivity, whereas for low water cut wells, dynamic liquid level, number of fracturing stages, and sand volume were the key determinants. This approach offers a novel data-driven solution for the development and management of tight oil wells, serving as an effective decision-support tool in oilfield development. Full article

Maintaining air quality is an important environmental challenge, affecting both urban and regional areas where industrial, agricultural, and energy activities intersect. The Upper Hunter Valley, NSW, experiences emissions from coal mining, power generation, agriculture, and wood fires, compounded by local meteorology, geology, and climate change. This study applies a political economy framework to examine historical governance structures including colonial legacies, institutional arrangements, and power relations and how they shape stakeholder roles and influence decision-making related to air quality. Technical applied research including improving dust monitoring, occupational health studies, and investigations into alternative fuels provided an empirical basis for identifying key stakeholders, including mining and energy companies, regulatory agencies, local councils, community groups, and environmental organisations. The analysis demonstrates how these actors influence governance processes, social licence to operate, and public perceptions of environmental risk. Findings indicate that effective air quality management requires multi-level, collaborative approaches that integrate technical expertise, regulatory oversight, and community engagement. The study highlights the importance of systemic strategies that align economic, environmental, and social objectives, providing insight into the governance of contested environmental resources in historically and politically complex regional contexts. This article is a rewritten and expanded version of the study “Analysis of air quality stakeholders in the Upper Hunter”, presented at the Clean Air conference, in Hobart, Australia, August 2024. Full article

To study the bearing capacity of large width-to-height ratio (LWHR) wind power spread foundations on onshore sandy soils, this study takes a 2 MW unit foundation of a specific wind farm as the object, conducts its sustainable design optimization, structural design and bearing capacity analysis, and explores internal force variation patterns of the structure and foundation. First, it investigates interactions among foundation soil stiffness, foundation deformation, and width-to-height ratio, and proposes the ratio can be slightly over 2.5 under specific geological conditions. Then, it verifies the foundation’s design and bearing capacity via code-based methods and uses ABAQUS to build an integrated finite element model (foundation, reinforcement, foundation ring, soil) for analyzing the mechanical behavior of the overall structure and key components. Finally, it focuses on bending moments of the foundation’s top/bottom slab reinforcement and reaction force distribution, and compares results from code formulas, commercial software, and finite element analysis. The results show that foundation deformation relates to the outer cantilever’s width-to-height ratio, reaction force magnitude, and soil stiffness. Soil stiffness impacts the linear distribution of reaction forces more significantly than the ratio—when soil stiffness is smaller, reaction forces still meet the linear assumption even if the ratio exceeds 2.5 within a range; when larger, they differ greatly. Under specific sandy soils (bearing stratum capacity < 300 kPa), the LWHR foundation meets wind turbine requirements, and its foundation ring’s punching shear resistance complies with standards (considering uplift-resistant reinforcement). For coarse sand bearing strata, the foundation’s bottom pressure follows linear distribution per code, and code formulas apply. However, the LWHR scheme is not fully suitable for complex geology or other foundation types, whose applicability needs comprehensive analysis. Full article

Accurate terrain modeling from high-resolution digital surface models (DSM) is critical for geosciences, geology, geomorphology, earthquake studies, and applied geology. However, existing filtering methods such as progressive morphological filtering (PMF), cloth simulation filtering (CSF), and progressive TIN densification (TIN) often struggle with complex topography and urban structures, leading to either excessive ground loss or incomplete object removal. Furthermore, some of these algorithms are only specialized for point cloud data and are not optimized for grid data. To address these limitations, we propose an iterative morphological filtering (IMF) algorithm that introduces a binary surface edge-segmentation strategy. The method refines object–ground separation by combining iterative morphological operations with block-based graph-cut stitching, thus enhancing continuity and accuracy in challenging terrain. Validation on UAV-derived DSM over the Haihe Basin in China and the ISPRS Vaihingen dataset shows that IMF achieves notable accuracy improvements: the Vaihingen test areas yielded an average Type I error of 8.93%, Type II error of 3.09%, overall accuracy of 80.85%, and Kappa coefficient of 0.7524, while the Haihe Basin test areas achieved Type I and II errors of 2.22% and 1.87%, overall accuracy of 89.32%, and a Kappa coefficient of 0.8706. These results demonstrate that IMF outperforms conventional methods by reducing both Type I and Type II errors, producing terrains highly consistent with real conditions. This innovation provides a robust and scalable solution for digital elevation models (DEM) generation from gridded DSM, offering significant value for large-scale environmental monitoring and flood risk assessment. Full article

Tunnel anchorages are critical components in long-span suspension bridges, transferring immense cable forces into the surrounding rock mass. Although previous studies have advanced the understanding of their pullout behavior through field tests, laboratory models, numerical simulations, and theoretical analyses, significant challenges remain in predicting their performance in complex geological conditions. This study investigates the pullout failure mechanism and bearing behavior of tunnel anchorages situated in heterogeneous conglomerate rock, with application to the Wujiagang Yangtze River Bridge in China to employ a tunnel anchorage in such strata. An integrated research methodology is adopted, combining in situ and laboratory geotechnical testing, a highly instrumented 1:12 scaled field model test, and detailed three-dimensional numerical modeling. The experimental program characterizes the strength and deformation properties of the rock, while the field test captures the mechanical response under design, overload, and ultimate failure conditions. Numerical models, calibrated against experimental results, are employed to analyze the influence of key parameters such as burial depth, inclination, and overburden strength. Furthermore, the long-term stability and creep behavior of the anchorage are evaluated. The results reveal the deformation characteristics, failure mode, and ultimate pullout capacity specific to weakly cemented and stratified rock. The study provides novel insights into the rock–anchorage interaction mechanism under these challenging conditions and validates the feasibility of tunnel anchorages in complex geology. The findings offer practical guidance for the design and construction of future tunnel anchorages in similar settings, ensuring both safety and economic efficiency. Full article

Optimization algorithms play a crucial role in solving complex problems in reservoir geology and engineering, particularly those involving highly non-linear, multi-parameter, and high-dimensional systems. In the context of reservoir development, accurate optimization is essential for enhancing hydrocarbon recovery, improving production efficiency, and managing subsurface uncertainties. The Moss Growth Optimization (MGO) algorithm emulates the adaptive growth and reproductive strategies of moss. It provides a robust bio-inspired framework for global optimization. However, MGO often suffers from slow convergence and difficulty in escaping local optima in highly multimodal landscapes. To address these limitations, this paper proposes a novel algorithm called Strategic Moss Growth Optimization (SMGO). SMGO integrates two enhancements: an Outpost Mechanism (OM) and an Early Stopping Strategy (ESS). The OM improves exploitation by guiding individuals through multi-stage local search with Gaussian-distributed exploration around promising regions. This helps refine the search and prevents stagnation in sub-optimal areas. In parallel, the ESS periodically reinitializes the population using a run-and-reset procedure. This diversification allows the algorithm to escape local minima and maintain population diversity. Together, these strategies enable SMGO to accelerate convergence while ensuring solution quality. Its performance is rigorously evaluated on a suite of global optimization benchmarks and compared with state-of-the-art metaheuristics. The results show that SMGO achieves superior or highly competitive outcomes, with clear improvements in accuracy and stability. To demonstrate real-world applicability, SMGO is applied to production optimization in tight reservoirs. The algorithm identifies superior production strategies, leading to significant improvements in projected economic returns. This successful application highlights the robustness and practical value of SMGO. It offers a powerful and reliable optimization tool for complex engineering problems, particularly in strategic resource management for tight reservoir development. Full article

Mineral prospecting from vast geological text corpora is impeded by challenges in domain-specific semantic interpretation and knowledge synthesis. General-purpose Large Language Models (LLMs) struggle to parse the complex lexicon and relational semantics of geological texts, limiting their utility for constructing precise knowledge graphs (KGs). Our novel framework addresses this gap by integrating a domain-specific LLM, GeoGPT, with a lightweight retrieval-augmented generation architecture, LightRAG. Within this framework, GeoGPT automates the construction of a high-quality mineral-prospecting KG by performing ontology definition, entity recognition, and relation extraction. The LightRAG component then leverages this KG to power a specialized geological question-answering (Q&A) system featuring a dual-layer retrieval mechanism for enhanced precision and an incremental update capability for dynamic knowledge incorporation. The results indicate that the proposed method achieves a mean F1-score of 0.835 for entity extraction, representing a 17% to 25% performance improvement over general-purpose large models using generic prompts. Furthermore, the geological Q&A model, built upon the LightRAG framework with GeoGPT as its core, demonstrates a superior win rate against the DeepSeek-V3 and Qwen2.5-72B general-purpose large models by 8–29% in the geochemistry domain and 53–78% in the remote sensing geology domain. This study establishes an effective and scalable methodology for intelligent geological text analysis, enabling lightweight, high-performance Q&A systems that accelerate knowledge discovery in mineral exploration. Full article

Understanding coalbed methane (CBM) enrichment patterns is essential for optimizing production capacity. This study evaluates the CBM reservoir-forming characteristics and exploration potential of the Longtan Formation in the Santang Syncline, Zhijin area, to systematically reveal CBM enrichment and high-production patterns. The investigation integrates regional geology, logging, well testing, laboratory analyses, and drainage production data. Results indicate that coal seam vitrinite reflectance (R_o,max) ranges from 3.20% to 3.60%, with metamorphic grade increasing with burial depth. Coal lithotypes consist predominantly of semi-bright coal, with subordinate semi-bright to semi-dull coal and minor semi-dull coal. Coal seam roofs comprise gray-black mudstone and calcareous mudstone, locally developing limestone, while floors consist of bauxitic mudstone. Pore structure analysis reveals greater complexity in coal seams 6 and 14, whereas seams 7 and 16 display simpler structures. Coal seams 5-3 and 6 demonstrate the weakest adsorption capacity and lowest theoretical gas saturation, while other seams exceed 55% gas saturation. Langmuir volume (V_L) increases with burial depth, reaching maximum values in coal seam 30. Langmuir pressure (P_L) follows a low–high–low trend, with lower values at both ends and higher values in the middle section. Measured gas content is highest in the middle section, moderate in the lower section, and lowest in the upper section. Reservoir condition assessment indicates favorable conditions in coal seams 14, 16, and 21, relatively favorable conditions in seam 7, and unfavorable conditions in seams 6, 30, 32, and 35. Among the three coal groups penetrated, the middle coal group exhibits the most favorable reservoir conditions, followed by the upper and lower groups. Full article

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

Tunnel lining voids, a common latent defect induced by the coupling effects of complex geological, environmental, and load factors, pose severe threats to operational and personnel safety. Traditional detection methods relying on Ground-Penetrating Radar (GPR) combined with manual interpretation suffer from high subjectivity, low efficiency, frequent missed or false detections, and an inability to achieve real-time monitoring. Thus, this paper proposes an intelligent identification methodology for tunnel lining voids based on an improved version of YOLOv8. Key enhancements include integrating the RepVGGBlock module, dynamic upsampling, and a spatial context-aware module to address challenges from diverse void geometries—resulting from interactions between the environment, geology, and load—and complex GPR signals caused by heterogeneous underground media and the varying electromagnetic properties of materials, which obscure void–background boundaries, as well as interference signals from detection processes. Additionally, the C2f-Faster module reduces the computational complexity (GFLOPs), parameter count, and model size, facilitating edge deployment at detection sites to achieve real-time GPR signal interpretation for tunnel linings. Experimental results on a heavy-haul railway tunnel’s lining defect dataset show 11.57% lower GFLOPs, 14.55% fewer parameters, and 13.85% smaller weight files, with average accuracies of 94.1% and 94.4% in defect recognition and segmentation, respectively, meeting requirements for the real-time online detection of tunnel linings. Notably, the proposed model is specifically tailored for void identification and cannot handle other prevalent tunnel lining defects, which restricts its application in comprehensive tunnel health monitoring scenarios where multiple defects often coexist to threaten structural safety. Full article

The numerous inhabited small islands of the Mediterranean basin are marginal geographic territories of high natural value. Historically, island communities have developed complex, poly-cultural agricultural systems, based on the use of native genetic resources and traditional ecological knowledge, to address the challenges linked to unfavorable climate, geology, and topography. However, economic, socio-demographic, and climatic factors have caused farmland abandonment, leading to soil and land degradation and to a decline in biodiversity and ecosystem services. Following the PRISMA guidelines, we conducted a systematic review to assess the state of scientific research with regard to agriculture on small Mediterranean islands. After screening records retrieved on Scopus, Web of Science, CABI, and Google Scholar, 167 articles published before July 2025 were included in the analysis. The articles covered 6 countries and 126 islands, with Greek and Italian islands being the most represented. Key topics included trajectories, drivers, and consequences of land use change, agrobiodiversity, and water resources. To complete the systematic review, 30 relevant EU-funded projects were identified and analyzed. Overall, the scientific research aimed at supporting agriculture on Mediterranean small islands tends to focus on a single issue or very few issues. However, we suggest that given the complexity of the drivers and consequences of farmland abandonment, more integrated approaches could have a greater impact. By providing a systematic overview of the current state of the research on agriculture on small Mediterranean islands, this review offers a solid basis for guiding ongoing and future research, actions, and policies aimed at building resilience in these fragile and endangered lands. Full article

Carbonate fracture–cavity reservoirs are significant oil and gas reservoirs globally, and their efficient development is influenced by the connectivity between fracture–cavity units within the reservoir. These reservoirs primarily consist of large caves, dissolution holes, and natural fractures, which serve as the primary storage and flow spaces. The S91 unit of the Tarim Oilfield is a karstic fracture–cavity reservoir with shallow coverage. It exhibits significant heterogeneity in the fracture–cavity reservoirs and presents complex connectivity between the fracture–cavity bodies. The integration of static and dynamic data, including geology, well logging, seismic, and production dynamics, resulted in the development of a set of static and dynamic connectivity evaluation processes designed for highly heterogeneous fracture–cavity reservoirs. Methods include using structural gradient tensors and stratigraphic continuity attributes to delineate the boundaries of caves and holes; performing RGB fusion analysis of coherence, curvature, and variance attributes to characterize large-scale fault development features; applying ant-tracking algorithms and fracture simulation techniques to identify the distribution and density characteristics of fracture zones; utilizing 3D visualization technology to describe the spatial relationship between fracture–cavity units and large-scale faults and fracture development zones; and combining dynamic data to verify interwell connectivity. This process will provide a key geological basis for optimizing well network deployment, improving water and gas injection efficiency, predicting residual oil distribution, and formulating adjustment measures, thereby improving the development efficiency of such complex reservoirs. Full article

The karst region is highly ecologically fragile due to its unique geology and poor water and nutrient retention. Despite long-term restoration, vegetation often remains in the secondary shrubland stage. Soil microorganisms play a vital role in maintaining ecosystem functions, but how microbial communities respond to combined water and nitrogen-phosphorus nutrient changes in karst shrubland remains poorly understood. This knowledge gap hinders effective restoration strategies in karst shrublands. Here, the effects of water, nitrogen, and phosphorous additions and their interactions on soil physico-chemical properties, soil microbial abundance, diversity, community composition, and the co-occurrence network were explored. A full factorial experiment (water × nitrogen × phosphorous, each at two levels) was conducted in a karst shrubland with over 20 years of vegetation restoration, with treatments including control, water (+120 mm yr⁻¹), nitrogen (+20 g N m⁻² yr⁻¹), phosphorus (+16 g P m⁻² yr⁻¹), and their four combinations. Our results suggested that water addition significantly increased soil water content and soil microbial abundance but reduced fungal diversity. Nitrogen addition significantly increased soil nitrate nitrogen content and fungal diversity, and fungal diversity showed an increasing trend under phosphorous addition. The addition of nitrogen and phosphorous did not significantly alter the soil microbial community composition, while water addition showed a tendency to change the soil fungal community composition. Network topological properties, robustness, and vulnerability analyses indicated that individual nitrogen or phosphorous additions, as well as their interactions, reduced network complexity and stability. In contrast, water addition alone or in combination with nitrogen and/or phosphorous alleviated these negative effects, and the water and phosphorous interaction exhibited the highest levels of network complexity and stability. Further analysis showed that the soil pH, available phosphorous, ratio of carbon to phosphorous, and ammonium nitrogen were explanatory variables contributing significantly to soil microbial abundance, diversity, community composition, and network complexity. Overall, these findings highlighted the pivotal role of water availability in enhancing soil microbial stability under nutrient enrichment, offering valuable insights into ecological restoration in karst ecosystems. Full article

This study provides a detailed geochronological paragenesis of fracture systems from the Upper Jurassic petroleum source formation in NE Iraq, utilizing U-Pb dating, integrated with microprobe analyses and petrographic studies. Five fracturing stages are recognized (FI–FV), indicating significant tectonic and temperature changes from the Late Jurassic to Pliocene times (approximately 5.2–5.5 Ma). The burial history curve shows continuous subsidence events, starting with initial burial of the Barsarin Formation reaching depths of 1000–1200 m by 110 Ma, this depth interval coincides with the first fracturing stage (FI). The buffered system of FI by pristine facies and geometrical cross-cutting of FI with early stylolite formation show a prior formation of stylolite. Subsequent fracturing stages FII (28.6 ± 2 Ma, Oligocene) and FIII (19.83 ± 0.43 Ma, Early Miocene) were contemporaneous with tectonic deformation phases and hydrocarbon generation times. Microprobe and optical analyses demonstrate variations in mineralogical composition, particularly in FIV/FV-filled calcite and dolomite cements (12.2 ± 1.5 Ma and 5.5 Ma), highlighting the periods of conduit formation for the hydrocarbon migration. Backscattered electron (BSE) imaging reveals a textural alteration of these cements, especially those associated with fluorite precipitation, which further support the hydrothermal entrapment associated with the hydrocarbon migration. The hydrocarbon entrapment appeared in at least two episodes under subsurface setting under temperatures exceeding 100 °C. In summary, the significant meaningful ages and compositional analyses obtained from this study reveal crucial insights into the dynamics of fracture-filling cements and hydrocarbon entrapment mechanisms within the petroleum source rock formation. The novelty of these data would enhance our understanding of the complex relationship between structural geology and migration conduits, highlighting the influence of fracture-filling cements on hydrocarbon accumulation and reservoir quality as a main target for hydrocarbon field development. 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