Search Results (282)

Shale gas “sweet spot” prediction serves as a pivotal technical link in shale gas exploration and development, directly governing the efficiency of exploration deployment and the economic viability of development projects. To address the research gap in sweet spot prediction for complex synclinal structures, this study establishes an integrated geology–engineering–economics evaluation framework, incorporating artificial intelligence (AI)-assisted parameter optimization and dynamic weight adjustment. This innovative approach overcomes the inherent limitations of single-parameter and static evaluation methods commonly employed in new exploration areas. Focusing on the Upper Ordovician Wufeng Formation to Lower Silurian Longmaxi Formation shale sequences within the Anchang Syncline of northern Guizhou, a comprehensive geological characterization of shale reservoirs was accomplished through the fine processing of 3D seismic data (dominant frequency: 30 Hz; signal-to-noise ratio: 8.5) and statistical analysis of logging data. Prestack elastic parameter inversion technology was utilized to quantitatively predict key geological sweet spot parameters, including the total organic carbon (TOC) content and total gas content, with model validation conducted using core test data. Coupled with prestack and poststack seismic attribute analysis, engineering sweet spot evaluation indicators—encompassing fracture development, in situ stress, the pressure coefficient, and the brittleness index—were established with well-defined quantitative criteria. By integrating multi-source data from geology, geophysics, and engineering dynamics, a three-dimensional evaluation system encompassing “preservation conditions–reservoir quality–engineering feasibility” was constructed, with the random forest algorithm employed for sensitive parameter screening. Research findings indicate that high-quality shale in the study area exhibits a thickness ranging from 17 to 22 m, characterized by a TOC content ≥ 4%, gas content of 4.3–4.8 m³/t, effective porosity of 3.5–5.25%, and brittleness index of 55–75. These properties collectively manifest the “high organic matter enrichment, high gas content, and high brittleness” characteristics. Through multi-parameter weighted comprehensive evaluation using the Analytic Hierarchy Process (AHP), complemented by sensitivity testing, sweet spots were classified into three grades: Class I (63 km²), Class II (31 km²), and Class III (27 km²). An optimized well placement scheme for the southern region was proposed, taking into account long-term production dynamics and economic assessment. This study establishes a multi-parameter, multi-technology integrated sweet spot evaluation system with strong transferability, providing a robust scientific basis for the large-scale exploration and development of shale gas in northern Guizhou and analogous complex structural regions worldwide. Full article

With the rapid advancement in information technology, the digital twin and smart assembly process simulation have become an integral part of the design and manufacturing of high-precision products. However, conventional Tunnel Boring Machine (TBM) cutterhead design and on-site assembly planning remain largely experience-driven and fragmented, with limited interoperability between geological characterization, structural verification, and constructability validation. This study proposes a digital twin-driven framework for TBM cutterhead design optimization and assembly process simulation that integrates geology-aware design inputs, BIM-based information modelling, FEM-based structural assessment, and immersive virtual environments within a unified virtual–physical workflow. To ensure consistent data exchange across platforms, an IFC4.3-compliant ontology is established using a non-intrusive property-set (Pset) extension strategy to represent cutterhead components, geological parameters, FEM load cases/results, and assembly tasks. Tunnel-scale stress analysis and cutter–rock interaction modelling are used to define project-representative cutter loading envelopes, which are mapped to a high-fidelity cutterhead FEM model for iterative structural refinement. The optimized configuration is then transferred to a game-engine/VR environment to support full-scale design inspection and assembly rehearsal, followed by manufacturing and field deployment with bidirectional feedback. To validate the proposed framework, an implementation case study of a deep hard-rock tunnelling project is presented where five design iterations were tracked across BIM–FEM–VR and nine constructability issues detected and resolved prior to assembly. The results indicate that the proposed digital twin approach strengthens traceability from geology to loading to structural response, reduces localized stress concentration at critical interfaces, and improves assembly readiness for complex tunnelling projects. Full article

The territory of the Republic of Armenia (RA) lies within the central Arabia–Eurasia collision zone and is characterized by rugged mountain landscapes, complex geology, active faulting, and seismicity. Armenia is highly vulnerable to seismic and landslide hazards, with more than 2504 active landslides mapped in the country. A significant landslide in the Tumanyan Community, Lori Marz, was activated in January 2018 and threatened critical infrastructure, including the railway linking Armenia to Georgia and the M6 interstate highway. The landslide’s activation was driven by groundwater, a nearby water reservoir leak, and adjacent infrastructure. Preliminary hazard analysis revealed that further movement of the landslide could dam the Debed River, leading to potentially catastrophic downstream impacts. In response, the Minister of Emergency Situations of RA requested urgent studies by the Institute of Geological Sciences of NAS RA. Surveys began on 22 January 2018, involving an interdisciplinary approach including geotechnical study, UAV-based digital mapping, and application of geophysical methods, such as MASW, microtremor recordings, GPR, and VES. The combination of these methods provided reliable information on the landslide’s geotechnical structure, identified the sliding plane, and allowed for numerical slope stability modeling, which confirmed the landslide’s unstable condition and susceptibility to reactivation from earthquakes or elevated groundwater. Based on this complex research, protective measures were developed and applied, including, in particular, horizontal drilling to dewater the sliding plane. These emergency measures stabilized the landslide, mitigating immediate threats to infrastructure and ensuring relative safety. Full article

The southeastern Iberian Peninsula, particularly the Águilas Arc within the Neogene Volcanic Province (NVP), represents a promising geothermal domain with complex tectonics and geology. The Palomares Fault Zone (PFZ), a key shear structure initiated during the Late Miocene, acts as a conduit for fluid migration, promoting mineralization and potential anomalies of rare and critical metals through fluid–rock interaction. This study investigates such interactions in the southernmost Águilas Arc, focusing on the El Arteal fault segment within the eastern PFZ strand. Mineralogical, geochemical, and hydrogeological analyses were performed using XRD, SEM, and ICP-MS techniques. Results reveal six mineral assemblages (MA) within the fault segment where the fault gouge samples were characterized by cataclastic textures and the occurrence of authigenic minerals, including halite, kaolinite, illite, paragonite, goethite, hematite, gypsum, barite, celestine, and quartz. Geochemical data indicate enrichment signatures in large-ion lithophile elements (LILE) and minor chalcophile and light rare-earth elements (LREE). Two thermal hydrofacies with alkaline metals enrichment were identified in wells and mine shafts: (1) Na⁺SO₄²⁻ and (2) Na⁺Cl⁻, where the latter exhibits high Na⁺ and Cl⁻ concentrations toward deeper sectors. These findings suggest multiple stages of fluid–rock interaction controlled by temperature: an early phase dominated by epithermal mineralization, followed by late-stage circulation of hypersaline fluids. This evolution provides an abnormal geochemical signature that is unique in the Aguilas Arc Geothermal System. Full article

Understanding the mechanisms of hydrocarbon migration, accumulation, and alteration, particularly how evolution controls these processes, is critical for exploring lithologic hydrocarbons in reservoirs. In the complex tectonic settings of the continental margin of the stable North China Craton, there is a significant presence of small yet highly prolific hydrocarbon reservoirs. The processes of hydrocarbon migration and accumulation are complex and thus represent an important research focus in geology. This study, based on core, logging, and seismic data and integrating fluid inclusion analysis, quantitative fluorescence techniques, and geochemical experiments, combines the shale smear factor and paleotectonic reconstructions to clarify the hydrocarbon accumulation episodes, migration pathways, and factors controlling reservoir adjustments in the Yanwu area of the Tianhuan Depression in the Ordos Basin, China. The results reveal three types of NE-trending left-lateral strike–slip faults: linear, left-stepping, and right-stepping. Shale Smear Factor (SSF) analysis confirms that these faults exhibit segmented opening behaviors, with SSF > 1.7 identified as the threshold for fault openness. Multiparameter geochemical tracing based on terpanes and steranes shows that lateral migration along fault zones dominates the preferential migration pathways for hydrocarbons. Fluid inclusion thermometry revealed homogenization temperatures within the 100–110 °C and 80–90 °C intervals, while the oil inclusions exhibit blue or blue-and-white fluorescence, reflecting early hydrocarbon charging and late-stage secondary migration. Integrated analysis indicates that during the late Early Cretaceous (105–90 Ma), hydrocarbons were charged upward through open segments of linear strike–slip fault zones in the northern study area, experiencing lateral migration and accumulation along high-permeability sand bodies and unconformities in the shallow strata. Since the Late Cretaceous (65 Ma-present), the regional tectonic framework has evolved from a west–high, east–low to a west–low, east–high configuration, inducing secondary hydrocarbon migration and leading to the remigration or even destruction of early-formed oil reservoirs. This study systematically demonstrates that fault activity and tectonic evolution control the accumulation and distribution of hydrocarbons in the region. These findings provide theoretical insights for hydrocarbon exploration in regions with complex tectonic evolution within stable cratonic basins. Full article

Accurate characterization of ore grindability is essential for optimizing mill throughput, reducing energy consumption, and predicting mill performance under varying ore conditions. However, the standard Bond work index (BWI) test remains time-consuming, costly, and requires a large amount of sample. This study evaluates the effectiveness of several rapid, low-cost alternatives, Leeb rebound hardness (LRH), Cerchar abrasivity Index (CAI), portable X-ray fluorescence (pXRF), and hyperspectral imaging (HSI), as proxies for grindability in gold-bearing ores. Sixty-two hand-size rock samples collected from two adjacent Canadian open-pit mines were analyzed using these techniques and subsequently grouped into ten ore groups for BWI testing. LRH and CAI effectively differentiated moderate (<15 kWh/t) from hard (>15 kWh/t) grindability classes, while geochemical features and HSI-based mineralogical attributes also showed strong predictive capability. HSI, in particular, provided non-destructive, spatially continuous data that are advantageous for complex geology and large-scale operational deployment. A conceptual workflow integrating HSI with complementary field measurements is proposed to support comminution planning and optimization, enabling more responsive and timely decision-making. While BWI testing remains necessary for circuit design, the results highlight the value of combining rapid proxy measurements with advanced analytics to enhance geometallurgical modelling, reduce operational risk, and improve overall mine-to-mill performance. Full article

Designing long-span cable-stayed bridges in high seismic hazard zones presents significant challenges due to their flexible structural systems, the influence of multi-support excitation, and the need to control large displacements while limiting seismic demands on critical components. These difficulties are further amplified in regions with complex geology and for bridges required to maintain high levels of post-earthquake serviceability. This study develops a low-damage seismic design approach for long-span cable-stayed bridges and demonstrates its application in the New Panama Canal Bridge. Probabilistic seismic hazard assessment and site response analyses are performed to generate spatially varying ground motions at the pylons and side piers. The pylons adopt a reinforced concrete configuration with embedded steel stiffeners for anchorage, forming a composite zone capable of efficiently transferring concentrated stay-cable forces. The lightweight main girder consists of a lattice-type steel framework connected to a high-strength reinforced concrete deck slab, providing both rigidity and structural efficiency. A coordinated girder–pylon restraint system—comprising vertical bearings, fuse-type restrainers, and viscous dampers—ensures controlled stiffness and effective energy dissipation. Nonlinear seismic analyses show that displacements of the girder remain well controlled under the Safety Evaluation Earthquake, and the dampers and bearings exhibit stable hysteretic behaviours. Cable tensions remain within 500–850 MPa, meeting minimal-damage performance criteria. Overall, the results demonstrate that low-damage seismic performance targets are achievable and that the proposed design approach enhances structural control and seismic resilience in long-span cable-stayed bridges. Full article

The transition to Outcome-Based Education (OBE) in engineering demands instructional tools that bridge theoretical knowledge and practical engineering competencies. However, traditional Learning Management Systems (LMS) primarily function as static resource repositories, lacking the semantic structure necessary to support deep learning and precise competency tracking. To address this, this study developed a three-layer domain Knowledge Graph (KG) for Structural Geology and integrated it into the ChaoXing LMS (a widely used Learning Management System in Chinese higher education). A semester-long quasi-experimental study (N = 84) was conducted to evaluate its impact on student performance and specific graduation attribute achievement compared to a conventional folder-based approach. Empirical results demonstrate that the KG-integrated group significantly outperformed the control group (p < 0.01, Cohen’s d = 0.74). Notably, while performance on rote memorization tasks was similar, the experimental group showed marked improvement in identifying and solving complex engineering problems. LMS log analysis confirmed a strong positive correlation (r = 0.68) between graph navigation depth and academic success. KG effectively bridged the gap between theoretical knowledge and practical engineering applications (e.g., geohazard analysis). This research confirms that explicit semantic visualization acts as vital cognitive scaffolding, effectively enhancing higher-order thinking and ensuring the rigorous alignment of instruction with engineering accreditation standards. Ultimately, this approach promotes sustainable learning capabilities and prepares future engineers to address complex, interdisciplinary challenges in sustainable development. Full article

The formation of fractures in urban areas is typically related to construction processes, natural ground settlement, and material quality. In valleys, the distribution of ground fissures is associated with aquifer overexploitation and basement faulting. However, where the soil layer is only a few meters thick or absent, the influence of basement structures remains poorly understood. We hypothesize that urban fractures develop parallel to major basement faults. To test this, we applied a simple structural geology technique to systematically measure extension axes, from street fractures, throughout the town of Las Pilas. These axis orientations were then compared with those calculated for normal faults of Las Pilas Complex. Street fractures are generally about 1 cm thick, with lengths ranging from 0.51 to 1 m and occasionally reaching up to 3 m. They occur within streets 2 to 4 m wide, typically appearing as a single fracture within a 1–2 m wide fracture zone. Based on these characteristics, the fractures do not represent a significant hazard. Measurement results indicate that urban fractures primarily extend in an NE-SW direction. This is consistent with the orientation of the minimum principal stress axis (3) of the regional San Luis-Tepehuanes fault system, thereby supporting our hypothesis. Full article

Effective mitigation of geotechnical risk and safety management of underground mine requires accurate estimation of rockburst damage potential. The inherent complexity of the rockburst phenomena due to nonlinear, high dimensional, and interdependent nature of the geological factors involved, however, makes predictive modeling a difficult task. The proposed research is based on the use of the K-Nearest Neighbor (KNN) algorithm to predict the risk of rockbursts with the use of microseismic monitoring data. Several key features like the ratio of total maximum principal stress to uniaxial compressive strength, energy capacity of support system, excavation span, geology factor, Richter magnitude of seismic event, distance between rockburst location and microseismic event, and rock density were applied as input parameters to extract critical rockburst precursor activities. In the test stage, the proposed KNN model recorded an accuracy of 75.50%, a precision of 0.913, a recall value of 0.509, and F1 Score of 0.576. The model is reliable with a significant performance indicating its efficacy in practice. The KNN model showed better classification results as compared to recently available models in literature and provided better generalization and interpretability. The model exhibited high prediction in classified low-risk incidents and had strong indicative capabilities towards high-risk situations, attributed to being a useful tool in rockburst hazard measurement. Full article

Xicheng is an important Chinese area enriched in lead–zinc polymetallic ore concentration area. Since the 1970s, substantial research achievements have been made in various domains, including the geological and geochemical characteristics of the deposits, metallogenic chronology, features of the marine basin during the initial mineralization stage, enrichment and precipitation of lead–zinc and other metallic ions, ore genesis, and metallogenic simulation experiments. Among these, the most representative findings focus on exhalative sedimentary reformation and the complexation of organic matter with lead–zinc metal elements during sedimentary processes. This review discusses the formation and evolution of sulfur-containing organic matter, especially H₂S, under Thermal Decomposition of Sulfate (TDS), Bacterial Sulfate Reduction (BSR), and Thermochemical Sulfate Reduction (TSR) conditions, and further summarizes the general characteristics of organic matter and lead–zinc (and other metal elements) adsorption–complexation–reduction. Subsequent research on organic lead–zinc mineralization in the Xicheng area has been grounded in ore deposit geology and geochemistry, adopting the perspective of organic fluids. These studies focus particularly on the formation process of Pb–Zn organic complexes and analyze the various stages and mechanisms of mineralization based on the characteristics and evolution of organic matter. This approach provides new insights for understanding both the general features and the unique attributes of lead–zinc mineralization in the Xicheng area. Full article

Natural fractures in tight sandstone reservoirs are the key factors controlling hydrocarbon flow and productivity. The Bozi-1 gas reservoir in the Kuqa Depression, as a typical ultra-deep tight sandstone gas reservoir, is characterized by low-porosity and ultra-low-permeability sandstones. This study addresses the limitations of previous fracture characterization, which primarily focused on macro-structural fractures while neglecting medium- and small-scale fractures. We integrate multi-source heterogeneous data, including core, well-logging imaging, seismic, and production observations, to systematically conduct multi-scale natural fracture characterization and modeling. First, the overall geology of the study area is briefly introduced, followed by a detailed description of the development characteristics of large-scale and medium–small-scale fractures, achieving a multi-scale representation of complex curved fracture networks. Finally, the three-dimensional multi-scale fracture model is validated using static indicators, including production characteristics, water invasion features, and well leakage data. The main findings are as follows: (1) Large-scale fractures in the Bozi-1 reservoir are mainly oriented near EW, NE–SW, and NW–SE, acting as the primary hydrocarbon migration pathways. Medium–small-scale fractures predominantly develop near SN, NE–SW, NW–SE, and near EW directions, exhibiting strong heterogeneity. (2) The complex curvature of large-scale fractures was captured by the “adaptive sampling + segmented splicing + equivalent distribution of fracture flow capacity” method, while the distribution of effective medium–small-scale fractures across the study area was represented using “single-well Stoneley wave inversion + seismic machine learning prediction”, achieving an 86% match with actual single-well measurements. (3) Model reliability was further verified through static comparisons, including production characteristics (unimpeded flow vs. effective fracture density, R² = 0.92), water invasion features (fracture-dominated water invasion matching fracture distribution), and well leakage characteristics (matching rate of high fracture density zones: 84.2%). The results provide key technical support for the precise characterization of fracture systems and establish a model ready for dynamic simulation in ultra-deep tight sandstone gas reservoirs. Full article

In high-mountain canyon areas, complex geological environments lead to frequent cascading disasters with unclear triggering mechanisms, posing severe threats to human life and property. Existing knowledge graph research in geology predominantly focuses on single-hazard types or general geological entities, lacking structured modeling and specialized datasets for cascading disaster processes, particularly the evolutionary chains in high-mountain canyon settings. To address this gap, this study proposes a method for constructing a knowledge graph tailored to cascading disasters in high-mountain canyon regions. First, a three-layer schema framework—comprising concept, relation, and instance layers—was designed to systematically characterize the knowledge elements and evolutionary relationships of disaster chains. To address the lack of a knowledge dataset for cascade disasters, this paper integrates multi-source heterogeneous data to construct a high-mountain canyon cascading disasters entity–relation dataset (DCER-MC), providing a reliable benchmark for related tasks. Based on this dataset, we implemented the knowledge graph and conducted disaster chain analysis. Experiments and applications demonstrate that the constructed knowledge graph effectively supports structured storage, centralized management, and scenario-based application of regional cascading disaster information. The main contributions of this work are (1) proposing a targeted schema framework for cascading-disaster knowledge graphs; (2) releasing a specialized dataset for cascading disasters in high-mountain canyon regions; and (3) establishing a complete pipeline from data to knowledge to scenario-based services, offering a novel knowledge-driven paradigm for disaster chain risk identification, inference prediction, and emergency decision-making in these areas. Full article

Abductive reasoning—the search for plausible explanations—has long been central to human inquiry, from forensics to medicine and scientific discovery. Yet formal approaches in AI have largely reduced abduction to eliminative search: hypotheses are treated as mutually exclusive, evaluated against consistency constraints or probability updates, and pruned until a single “best” explanation remains. This reductionist framing fails on two critical fronts. First, it overlooks how human reasoners naturally sustain multiple explanatory lines in suspension, navigate contradictions, and generate novel syntheses. Second, when applied to complex investigations in legal or scientific domains, it forces destructive competition between hypotheses that later prove compatible or even synergistic, as demonstrated by historical cases in physics, astronomy, and geology. This paper introduces quantum abduction, a non-classical paradigm that models hypotheses in superposition, allowing them to interfere constructively or destructively, and collapses only when coherence with evidence is reached. Grounded in quantum cognition and implemented with modern NLP embeddings and generative AI, the framework supports dynamic synthesis rather than premature elimination. For immediate decisions, it models expert cognitive processes; for extended investigations, it transforms competition into “co-opetition” where competing hypotheses strengthen each other. Case studies span historical mysteries (Ludwig II of Bavaria, the “Monster of Florence”), literary demonstrations (Murder on the Orient Express), medical diagnosis, and scientific theory change. Across these domains, quantum abduction proves more faithful to the constructive and multifaceted nature of human reasoning, while offering a pathway toward expressive and transparent AI reasoning systems. Full article

The integration of large language models (LLMs) into project-based learning (PBL) holds significant potential for addressing enduring pedagogical challenges in engineering education, such as providing scalable, personalized support during complex problem-solving. Grounded in Self-Determination Theory (SDT), this study investigates how different LLM usage strategies impact student learning within a blended engineering geology PBL context. A one-semester quasi-experiment (N = 120) employed a 2 (usage mode: individual/shared) × 2 (interaction restriction: restricted/unrestricted) factorial design. Mixed-methods data, including surveys, interaction logs, and reflective reports, were analyzed to assess learning engagement, psychological needs satisfaction, cognitive interaction levels, and project outcomes. Results demonstrate that the individual use strategy significantly outperformed shared use in enhancing engagement, needs satisfaction, higher-order cognitive interactions, and final project scores. The restricted interaction strategy effectively served as a metacognitive scaffold, optimizing the learning process by promoting deliberate planning. Notably, individual autonomy did not undermine collaboration but enhanced it by improving the quality of individual contributions to group work. Students also developed robust critical verification habits to navigate LLM “hallucinations.” This research identifies “individual autonomy” as the core mechanism and “moderate constraint” as a crucial design principle for LLM integration, providing an empirically supported framework for harnessing generative AI to foster both motivational and cognitive outcomes in engineering PBL. Full article