Search Results (66)

This editorial synthesizes the key findings from 17 original research articles featured in the Special Issue on “Intelligent and Integrated Approaches for Efficient Oil and Gas Development.” The collection demonstrates a paradigm shift from purely data-driven methods toward physics-informed, interpretable, and operationally deployable intelligent systems across the upstream lifecycle. Advances span intelligent drilling with real-time model predictive control frameworks achieving sub-20 ms execution times and bottomhole pressure fluctuations below 0.30 MPa; AI-assisted reservoir characterization using multiscale convolutional neural networks, seismic waveform-constrained inversion, and geology-informed transformers that improve sandstone thickness prediction (R² = 0.895) and stratigraphic correlation (F1 = 0.886); production optimization through hybrid decomposition-ensemble models (R² = 0.954) and improved XGBoost (R² = 0.989); and enhanced oil recovery via self-assembled foam systems and polymer injector designs. Fundamental geochemical studies on the Qiongzhusi Formation shale and tight sandstone gas in the Ordos Basin provide critical geological constraints. The editorial identifies persistent challenges, including real-time performance versus physical fidelity, interpretability and uncertainty quantification, multi-scale integration, and generalizability across diverse geological settings. Future directions highlight reinforcement learning for autonomous operations, physics-informed digital twins, generative AI for subsurface scenario modelling, and integration with carbon capture, utilization, and storage. This Special Issue advances the convergence of petroleum engineering, artificial intelligence, and Earth sciences toward intelligent, secure, and sustainable hydrocarbon development. Full article

Reversible emulsion drilling fluids integrate the advantages of water-based and oil-based systems, offering solutions to critical challenges in shale oil and gas development. However, conventional reversible emulsions face limitations including poor stability, high cost, and material scarcity. This research introduces widely available, eco-friendly modified nanocrystalline cellulose (MNCC) as a sustainable alternative. While current reversible drilling fluids primarily depend on organoclays and adopt aqueous phases containing 20–25% CaCl₂ for shale inhibition, pH-responsive MNCC was validated as an effective reversible emulsifier capable of stabilizing emulsions through 48 consecutive phase-inversion cycles. Enhanced emulsion stability was achieved with organoclay at an optimal dosage (≤2.5 g/100 mL), and a composite interfacial film superior to the film formed by pure MNCC was fabricated via the combination of organoclay and MNCC. Increasing the organoclay content elevated the acid requirements for phase inversion (due to its lipophilicity) but reduced the alkali needs. Finally, higher CaCl₂ concentrations in the aqueous phase reduced the acid demand for inversion yet increased alkali consumption and diminished stability in both oil-in-water (O/W) and water-in-oil (W/O) emulsions. These effects are attributed to the dual role of CaCl₂ in compressing the electrical double layer and modifying phase density differences, synergistically governing reversible inversion behavior. This research provides a foundation for applying nanocrystalline cellulose-stabilized reversible emulsion drilling fluids, offering practical solutions for efficient development of sensitive reservoirs like shale. Full article

The efficient development of low-permeability reservoirs is critically constrained by severe geological heterogeneity, marginal permeability (<10 mD), and the consequent prevalence of low-productivity wells. Conventional intermittent production management, reliant on empirical fixed-cycle schedules, fails to adapt to dynamic reservoir behavior and wellbore conditions, leading to suboptimal energy efficiency and recovery. This study presents a physics-constrained, data-driven framework for adaptive intermittent production optimization, specifically designed to address the coupled geological-engineering complexities of such reservoirs. The methodology integrates three core innovations: (1) a hybrid flowing bottomhole pressure (FBHP) decline model coupling a “Three-Segment” wellbore pressure calculation with inflow performance relationship (IPR) curves, enabling dynamic characterization of pressure depletion; (2) a shut-in pressure buildup prediction framework combining a physically interpretable dual-exponential recovery mechanism—representing near-wellbore elastic expansion and far-field formation recharge—with a Random Forest Regression algorithm to capture the influence of geological and operational heterogeneity; and (3) a “Dual-Curve Intersection Method” that autonomously determines optimal pumping and shut-in durations by intersecting predicted pressure decline and recovery curves under geological constraints. Field implementation on 15 low-production wells in the Jiyuan Oilfield—a representative low-permeability asset—demonstrated robust performance: average pump efficiency improved from 14.3% to 14.49%, and average single-well electricity savings reached 15.61%. This work establishes a closed-loop intelligent control framework that bridges reservoir geology, wellbore hydraulics, and machine learning, offering a scalable solution for enhancing energy efficiency and production sustainability in low-permeability and unconventional resources. Full article

Diagenetic anomalies within the Upper Paleozoic coal-bearing strata of the Longdong area, Ordos Basin, represent a complex interplay between thermal maturation and fluid evolution, yet their governing mechanisms remain poorly understood. This study integrates petrographic analysis, X-ray diffraction, vitrinite reflectance (Ro) measurements, and fluid inclusion microthermometry to evaluate the discrepancy between organic thermal maturity and mineralogical diagenetic records. The results indicate that the mudstones achieved high thermal maturity, with mean Ro and Tmax values of 2.3% and 555.1 °C, respectively. However, the associated sandstones exhibit anomalous mineral assemblages, characterized by persistent high levels of illite/smectite (I/S) mixed-layer minerals and authigenic kaolinite, which are inconsistent with the anticipated advanced diagenetic stage. Furthermore, homogenization temperatures (Th) of fluid inclusions are significantly lower than expected, implying a localized suppression of illitization. We propose that this atypical diagenetic trajectory is governed by sluggish fluid–rock interactions in a confined diagenetic environment. Specifically, the dissolution of feldspars during acidic diagenesis provided a localized Al³⁺ supply, favoring kaolinite precipitation, while the limited availability of reactive feldspar precursors and pore-fluid retention effectively stalled the progression of illitization. These findings demonstrate that reactant availability and reaction kinetics can decouple mineralogical evolution from organic thermal maturation in coal-bearing sequences. This study provides a novel mechanistic framework for interpreting anomalous diagenetic signatures in heterogeneous sedimentary basins, offering significant implications for reservoir quality prediction in deep-seated, thermally mature strata. Full article

This study addresses the high-risk scenario of dual-sided deep foundation pit construction adjacent to operational metro lines, a complex urban underground engineering context with significant safety implications. A multi-level dynamic safety risk assessment model is proposed by integrating the Analytic Hierarchy Process (AHP) with a risk matrix. Existing approaches generally lack the capability to dynamically incorporate spatiotemporal variations and real-time construction management information, limiting their applicability under complex working conditions. To overcome these limitations, the Tianjin Shouchuang Beiyunhe Metro Complex project is adopted as a case study to develop a concise and efficient risk assessment framework. The framework introduces spatiotemporal effect and safety management coefficients to dynamically adjust risk values and conducts risk identification and integrated evaluation across four dimensions—geology, environment, design, and construction—using 25 indicators. The model enables quantitative, real-time identification and dynamic control of safety risks during metro foundation pit construction. The assessment results indicate that the overall project risk is classified as Level I (highest), with the western pit exhibiting slightly higher risk. Targeted mitigation measures include the use of diaphragm walls with internal buttresses and grouting reinforcement. Compared with conventional methods, the proposed model demonstrates significant advantages in adapting to dynamic construction conditions, enhancing engineering applicability, and strengthening early-warning capability. These improvements provide a scientific, practical, and scalable technical solution for the accurate identification of critical risks and proactive safety management in complex metro foundation pit projects. Full article

Water accumulation in goafs in Chinese coal mines is a major hidden hazard that can trigger water inrush accidents and may also affect aquifer integrity and regional water security. Reliable delineation of goaf water distribution and identification of water-source types are therefore essential for mine water-hazard control and groundwater protection. This paper reviews the main technical routes for goaf groundwater investigation, including geophysical prospecting, hydrogeochemical and isotopic identification, direct inspection tools, and data-driven intelligent workflows. For geophysical detection, the mechanisms, engineering applicability, and key constraints of the Transient Electromagnetic Method (TEM), Surface Nuclear Magnetic Resonance (NMR), the High-Density Resistivity Method (HDRM), and the Coherent Frequency Component (CFC) electromagnetic wave reflection coherence method are synthesized, with emphasis on interpretation boundaries and uncertainty sources under complex geological conditions. For source identification, conventional hydrochemistry, stable isotopes, and laser-induced fluorescence are summarized, and intelligent recognition models such as neural networks and support vector machines are discussed in terms of workflow positioning and practical performance limits. A unified evaluation rationale is established and a semi-quantitative method–metric matrix is constructed to compare techniques in terms of reliability, deployability, cost level, environmental adaptability, and information value, thereby clarifying their functional roles and complementarities within staged engineering workflows. The synthesis indicates that major bottlenecks include limited deep capability under strong interference, pronounced interpretational non-uniqueness caused by complex geology and irregular goaf geometries, and constrained timeliness and generalization for mixed-source identification. Future directions are summarized as multi-method integration with fusion-driven interpretation, intelligent and quantitative decision support with quality control, and sensor–platform advances enabling more practical three-dimensional investigation, aiming to improve the reliability and engineering usability of goaf groundwater hazard assessment. Full article

Universities are struggling in a continuously changing environment surrounded by both accelerated digitalization and increasingly influential Artificial Intelligence. However, experiential learning stemming from direct visualization still relies on traditional tools and supporting materials. This work presents how a historic geology museum can serve as a pedagogical innovation for Civil Engineering students despite the challenges universities face amid accelerating digitalization. The geological collections of the School of Civil Engineering at the Universidad Politécnica de Madrid, neglected for decades, have recently been restored and transformed into a dynamic university museum that now plays a significant role in both degree and MEng education. This museum preserves several Paleolithic collections assembled by its professors since the school’s establishment in 1802. Historical and museological research confirms that these holdings—2471 minerals, 4555 rocks, 2012 fossils, archeological materials, and a unique set of 1200 formatted stone samples from 19th- and early 20th-century Spanish quarries—constitute one of the oldest and most comprehensive geological collections preserved in a Spanish engineering institution. The museum’s revitalization is implying new research on several sub-collections, still in progress. In summary, the historical museum has been integrated into Civil Engineering teaching, supporting experiential and lifelong learning in geology and geotechnics. Furthermore, the museum serves as an innovative tool for teaching geology to secondary school students, promoting innovation in teaching practices and scientific dissemination, and encouraging interest in Earth sciences. Overall, the museum is becoming a valuable resource for innovative pedagogy to respond to the lifelong learning implications of STEM educational practices. Full article

Deep coalbed methane resource evaluation is limited by weak coupling among key controlling factors and by the lack of unified methods for Computational Unit delineation. This study focuses on the No. 8 coal seam of the Benxi Formation in the Ordos Basin. A geological–engineering integrated framework for delineation and evaluation of deep coalbed methane units was established based on the concept of “one body and four levels.” Results indicate that a depth of 1500 m represents a critical boundary for changes in coalbed methane occurrence. Gas in deep coal seams occurs mainly as a combination of adsorbed gas saturation and free gas enrichment. Vitrinite reflectance was used to evaluate gas source conditions, and a threshold of Ro = 1.2% was identified. Cap rock sealing performance was evaluated using lithological assemblages, with mudstone–limestone combinations showing the most favorable preservation conditions. A brittle–ductile index based on rock mechanical parameters was applied to assess reservoir fracability. Gas source effectiveness, preservation conditions, and reservoir transformability were quantified using thermal simulation experiments, formation pressure and temperature analysis, sealing tests, and coal–rock mechanical experiments. GIS-based spatial overlay analysis was used to divide the No. 8 coal seam into 16 computational units. The total deep coalbed methane resources were estimated at approximately 16.49 × 10¹² m³. Accordingly, the research findings provide a crucial scientific basis for the rational delineation of computational units in deep coalbed methane systems. They also offer significant theoretical support for subsequent applications of machine learning and coupled geomechanics–flow modeling methods, enabling accurate dynamic prediction and optimal zone selection within the study area. Full article

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

Urban flood disaster management is an interdisciplinary field that integrates hydrology, geology, engineering, and urban planning, with prediction, assessment, and optimization serving as its core components. However, a comprehensive and systematic synthesis of recent developments in this domain remains limited, constraining both theoretical understanding and practical advancement. To address this gap, this study conducts an in-depth analysis of urban flood management research as a systematic review, with a particular focus on advances in prediction, assessment, and optimization. Utilizing a multistep holistic review, combining bibliometric and scientometric analysis with structured literature categorization, the research critically examines and synthesizes relevant findings. This study analyzed 166 research papers related to urban flood management within the Web of Science database. Through co-citation and keyword co-occurrence analyses, five dominant research dimensions are identified: physics-based simulation methods, data-driven approaches, risk assessment tasks, optimization strategies, and miscellaneous emerging topics. Based on these insights, we propose a task-oriented framework that systematically integrates prediction, assessment and optimization across the four phases of disaster management: mitigation, prevention, emergency response and recovery. This framework aids scholars and practitioners in understanding and implementing effective techniques and strategies. The study’s findings shed light on key trends and potential future directions, providing a roadmap for further exploration of urban flood management and guiding professionals in related fields. 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 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

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

High-resolution lithological mapping of outcrops is fundamental for reservoir characterization and petroleum geology, yet distinguishing lithologies with subtle petrophysical contrasts remains a major challenge. This study proposes MC-H-Geo, a multi-scale contextual hierarchical framework for automated lithology classification from terrestrial laser scanning (TLS) point clouds. The framework integrates three modules: (i) a multi-scale contextual feature engine that extracts spectral, geometric, and textural descriptors across local and stratigraphic contexts, enhanced by cross-scale differentials to capture stratigraphic variability; (ii) a gated expert classifier with task-adaptive feature subsets for hierarchical vegetation–rock and intra-rock discrimination; and (iii) a two-step geological post-processing procedure that enforces stratigraphic continuity through Z-axis correction and neighborhood smoothing. Experiments on the Qianwangjiahe outcrop (Ordos Basin, China) demonstrate state-of-the-art performance (OA = 94.3%, Macro F1 = 0.944), outperforming PointNet++ (77.1%), SG-RFGeo (74.2%), and XGBoost (61.7%). Error analysis reveals that residual sandstone–vegetation confusion results from feature aliasing in weathered zones, highlighting the intrinsic limitations of TLS-only data. Overall, MC-H-Geo establishes an advanced framework for fine-grained lithological mapping and identifies multi-sensor data fusion as a promising pathway toward robust, geologically consistent outcrop interpretation. Full article