Search Results (144)

Gallium (Ga) in coal is a nationally emerging strategic mineral resource, yet research on using petrophysical methods to detect the spatial variation in critical metals in coal seams remains limited. Analyzing the distribution characteristics of Ga-rich coal using geophysical well-logging methods is of great significance for the development and utilization of Ga. This study introduces a quantitative method for predicting Ga-rich laminations in ultra-thick bituminous coal seams by integrating: (i) wireline-log-based lithofacies classification, (ii) lithofacies-constrained mineral inversion, and (iii) lithofacies-constrained and laboratory-established Ga–mineral correlations. The coal seam was first classified into four distinct lithofacies types—(i) parting, (ii) medium-ash coal (MA), (iii) low-ash coal (LA), and (iv) extra-low-ash coal (ELA)—through integration of conventional wireline log interpretation, cluster analysis, and XGBoost machine learning. Second, lithofacies-constrained Ga–host mineral associations were established by integrating core sample analysis, correlation analysis, and linear regression modeling. Third, mineral content predictions for each lithofacies were obtained through wireline-log-based mineral inversion, constrained by petrophysical boundaries. Finally, prediction uncertainties were evaluated using Markov Chain Monte Carlo (MCMC) simulation, while Ga-rich laminations were predicted by integrating log-derived mineral inversion results with regressed Ga prediction models. The results demonstrate strong agreement between mineral inversion and XRD analyses within uncertainty ranges, achieving a prediction accuracy of 73.6% for Ga. This validated methodology presents a novel approach for quantifying Ga concentrations in coal, as demonstrated through a case study. Full article

Niobium is a strategic critical mineral that supports emerging energy and high-end manufacturing. The geophysical parameters of carbonatite-alkaline rock-type niobium deposits constitute essential baseline data for regional geophysical exploration and prospecting target delineation. To clarify the geophysical response characteristics and exploration the significance of the Dagele niobium deposit in the Eastern Kunlun Region (western China). This study focuses on drill hole ZK3202. Samples from ore bodies, mineralized zones, and wall rocks of different lithologies were continuously measured. Combined with 1001.8 m of full-hole core digital logging data, statistical methods, including box plots, histograms, multi-parameter cross-plots, and correlation coefficient analysis, were applied to quantitatively investigate the physical property responses of lithologies such as calcite-biotite rock (ore body), calcite-bearing pyroxenite (mineralized zone) and amphibolite in the vertical profile. Lithological identification thresholds were established to divide the drill-hole into lithological and mineralized ore layers. The results show that the ore-bearing lithofacies exhibit a distinctive geophysical signature characterized by high density, strong magnetism, medium-low resistivity, high polarizability, and slightly elevated natural radioactivity, which clearly distinguishes them from surrounding from wall rocks. Based on five key parameters—density, magnetic susceptibility, resistivity, polarizability, and natural gamma—a lithological identification model for amphibolite and mineralized altered rock assemblages was established. This study also summarizes the multi-parameter coupling mechanism of ore-bearing lithofacies, which can effectively delineate favorable niobium-bearing horizons. This work fills a gap in the geophysical property characterization of carbonatite-alkaline complex-type niobium deposits in the Eastern Kunlun region and provides data support and regional reference for integrated gravity-magnetic-electrical-radioactive geophysical exploration, prospecting target delineation, and the exploration of similar niobium deposits in western China. Full article

The Mabi Block is located in the southern Qinshui Basin, representing an underexplored region with high-rank coal seams that host significant Coalbed Methane (CBM) potential. Despite extensive CBM development in the nearby Anze and Zheng Zhuang blocks, the geological and geophysical controls on Coalbed Methane enrichment in Mabi remain insufficiently constrained. This study integrates the core data (63 samples) of isothermal adsorption tests, well-logging data from (13 wells), and 3D seismic attributes to systematically evaluate the key controlling factors, such as burial depth, roof and floor lithology, and sealing capacity, in the horizons of the No.3# and No.15# coal seams. Lithology is characterized using natural gamma ray (GR), acoustic (AC), deep resistivity (RD), compensated neutron log (CNL), and seismic wave impedance inversion. Coal quality parameters, ash content, and the Langmuir volume (V_L) are correlated with gas content, and structural controls are mapped using curvature, fault interpretation, and burial depth analysis. The results show that thick mudstone and limestone roofs, moderate burial depth (1100–1350 m), synclinal structural lows, and thicker coal seams (6–9 m) collectively enhance methane preservation. The ash content (%) exhibits a moderate negative correlation with the Langmuir volume (R² = 0.4) and gas content. Structural curvature (syncline) and fault intensity strongly govern lateral sealing integrity, where anticline zones and faulted regions display notable degassing. This integrated assessment contributes to a refined CBM optimization model for the Mabi Block and guides targeted future drilling, reservoir evaluation, and production optimization. Full article

The principal difficulty in studying the physico-mechanical and filtration-capacity properties of coals and host rocks under laboratory conditions using core samples lies in reproducing natural thermodynamic conditions characteristic of in situ depths. To address this issue, specialized equipment and methodologies for transferring measurement results are employed, including the Hoek–Brown failure criterion, the structural weakening coefficient, and the development of thermodynamic models. The reliability and accuracy of such measurements are determined by the degree of conformity between the adopted laboratory conditions and natural in situ conditions, the number of samples representing different lithological varieties, and the adequacy of sampling procedures ensuring representativeness. Particular challenges arise when sampling cleated and fractured coals formed under natural stress–strain conditions and contain methane, which significantly influences their physical properties. These difficulties are especially pronounced in prepared-for-mining high-gas-content coal seams of the Karaganda Basin at depths of approximately 700 m, where obtaining representative samples is technically complicated. Reliable values of the physico-mechanical properties of the coal–rock mass are essential for geomechanical calculations aimed at ensuring safe mining of high-gas-content seams through risk assessment of geodynamic phenomena, particularly in zones of geological disturbances, floor heave, and roof collapse. In this context, the use of a comprehensive suite of geophysical logging data from exploration boreholes makes it possible to obtain continuous, high-precision information on physico-mechanical and filtration-capacity properties. These methods are particularly important for characterizing the coal–rock mass in operating mines, since the natural state of host rocks and prepared coal seams is altered due to stress relief caused by mine workings, preliminary degasification measures, and hydraulic fracturing. The problem addressed is the need for reliable assessment of rock and coal seam parameters under natural thermodynamic stress–strain conditions, taking into account lithological composition, structural heterogeneity, fracture development, stratigraphic differentiation, and gas saturation. The aim of this study is to ensure efficient and safe coal extraction based on geomechanical calculations utilizing physico-mechanical and filtration-capacity properties of host rocks and gas-bearing coal seams, whether prepared for mining or not yet extracted. The research methods are based on an integrated complex of geophysical logging of exploration wells, specialized software tools, and statistical processing techniques to identify patterns in physico-mechanical and filtration-capacity properties of host rocks and coal seams under natural stress–strain conditions, as well as to determine the nature of changes in these properties within coal seams and roof and floor rocks in prepared mining areas. The physico-mechanical and filtration-capacity properties of host rocks and coals from the Lenin and Kazakhstanskaya mines were determined. Regularities governing the application of these parameters to coals of different formations and depths were established; fracture orientations and characteristics were evaluated; and relationships between changes in coal seam parameters and gas content were identified. A comprehensive methodological framework for studying the physical and capacity properties of the coal–rock mass under natural thermodynamic conditions has been developed. Its primary application is the investigation of coal seams prepared for mining to support geomechanical calculations for efficient and safe coal extraction, the implementation of degasification measures for high-gas-content seams, and the assessment of gas-dynamic risks based on the character of variations in physical parameters. Full article

Strike-slip faults and their associated fractures in the ultra-deep marine carbonate reservoirs of the Fuman Oilfield, Tarim Basin, hold significant petroleum geological importance, with the developmental characteristics of fractures being a key factor controlling reservoir productivity. This study targets the F_I17 strike-slip fault zone within the oilfield, where a comprehensive evaluation of fracture effectiveness was performed by integrating geological methods, including core and thin section observation, fluid inclusion thermometry, geophysical fracture identification approaches using imaging logging and seismic data, and geomechanical simulations. The results showed that: (1) structural fractures were developed in at least three stages, predominantly high-angle fractures with their strikes obliquely intersecting the main fault at a small angle, and were affected by multiple episodes of fluid activity, while early-phase fractures exhibited severe filling whereas late-phase fractures had good effectiveness; (2) ultra-deep carbonate rocks contained well-developed stylolites, with extensive horizontal stylolites reducing fracture effectiveness; (3) mechanical effectiveness evaluation parameters were proposed by integrating normal stress, shear stress, and formation pressure, with slip tendency as the dominant indicator, and referenced to the leakage factor and dilation tendency to characterize fracture effectiveness; (4) dynamic effectiveness was assessed using closure/opening pressures, defining a reasonable formation pressure range for hydrocarbon exploitation. The findings of this study can provide theoretical guidance for the further exploration and development of ultra-deep reservoirs in the Fuman Oilfield. 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

Reservoir fluid identification is a critical aspect of oil and gas geophysical exploration. Accurate fluid identification directly impacts the interpretation of subsurface geological conditions, reduces exploration risks, and provides essential guidance for formulating oil and gas development strategies. Therefore, reliable and precise fluid identification is indispensable across different stages of oil and gas exploration and production. This study proposes a hierarchical classification method based on conditional Variational Autoencoder (cVAE) and time series forest (TSF) algorithms to address reservoir fluid identification under complex geological conditions. The main contributions of this work are as follows: (i) the cVAE is used to pre-process the logging data to suppress local high-frequency disturbances and isolated anomalies that may exist in the logging curves, thereby improving the quality of the input data; and (ii) hierarchical classification strategy is utilized to perform the fluid identification task in two steps. The first step involves a top-level classification to distinguish the gas bearing layer from the non-gas layer. The second step refines this classification into subcategories, including the gas layer (GL), gas–water layer (GW), gas-bearing water layer (GBW), water layer (WL), and non-reservoir layer (DW). This can fully address the challenges of imbalanced datasets and improve the recognition accuracy of minority classes. Additionally, integrating the TSF algorithm within the hierarchical classification framework effectively captures the sequential characteristics of well logging data, improving the model’s ability to recognize complex geological patterns. A real-world application in a block of the Yinggehai Basin in the South China Sea demonstrated the superior performance of the proposed model. Experimental results show that the method achieves an accuracy of over 84% in all four wells, enabling accurate and reliable reservoir fluid classification. Full article

The detection of underground cavities and dissolution features is a critical component in assessing geohazards within karst terrains, particularly where natural processes interact with long-term human occupation. This study investigates two contrasting sites in the Sefrou region of northern Morocco: Binna, a rural travertine-dolomite system shaped by Quaternary karstification, and the urban Old Medina of Bhalil, where traditional cave dwellings are carved into carbonate formations. A combined geophysical and geological approach was applied to characterize subsurface heterogeneities and assess the extent of near-surface void development. Vertical electrical soundings (VES) at Binna site delineated high-resistivity anomalies consistent with air-filled cavities, dissolution conduits, and brecciated limestone horizons, all indicative of an active karst system. In the Bhalil old Medina site, ground-penetrating radar (GPR) with low-frequency antennas revealed strong reflection contrasts and localized signal attenuation zones corresponding to shallow natural cavities and potential anthropogenic excavations beneath densely constructed areas. Geological observations, including lithostratigraphic logging and structural cross-sections, provided additional constraints on cavity geometry, depth, and spatial distribution. The integrated results highlight a high degree of subsurface karstification across both sites and underscore the associated geotechnical risks for infrastructure, cultural heritage, and land-use stability. This work demonstrates the value of combining electrical and radar methods with geological analysis for mapping hazardous subsurface voids in cavity-prone Quaternary landscapes, offering essential insights for risk mitigation and sustainable urban and rural planning. Full article

In the Ordos Basin, the lengths of cores are disproportionate to image logging data (1:9) and fracture research is difficult because of their complex lithology and fracture patterns. Based on the characteristics of conventional logging and cores, this paper describes the color, shape, geophysical characteristics and geological features of the basin to establish an image recognition template and to identify nine distinct lithologies. The genesis, type, occurrence, opening mode, cutting depth, host lithology, density and tectonic stress of the fractures are used to define four types of fracture facies (bedding fracture facies, N100° tectonic fracture facies, N10° tectonic fracture facies and coal fracture facies) and to build four models. The comprehensive coherence among the neural network results, curvatures, ant bodies, lithologies, and thicknesses was used to predict the type of different fracture facies. The results show that the fracture prediction model fully reflects the genesis of the cracks and influencing factors and provides insights into optimal areas for future exploration and development. Full article

Hydraulic conductivity estimation in fractured and clay-rich sedimentary rocks remains challenging due to substantial heterogeneity and drilling disturbances. This study evaluates the capability of borehole electrical logs—particularly spontaneous potential (SP) and single-point resistance (SPR)—to improve hydraulic conductivity prediction in Taiwan’s mountainous sedimentary formations. Integrating 124 double-packer test intervals with high-resolution electrical logs facilitates the examination of electrical–hydraulic relationships under complex lithologic conditions. The analysis shows that formation factor approaches perform poorly because drilling mud invasion alters pore–water resistivity and clay content disrupts Archie-type assumptions. An SP-assisted screening workflow was developed to identify intervals with stable electrochemical behavior, which substantially strengthened the relationship between SPR and hydraulic conductivity. The regression models developed in this study estimate hydraulic conductivity (K) from single-point resistance (SPR). The general model achieves R² = 0.716, while the high-precision model yields R² = 0.946 after SP-based data refinement. These results indicate that SP screening markedly improves the predictive reliability of resistivity-based K estimation. The findings highlight a practical and cost-effective framework for generating continuous hydraulic conductivity profiles in fractured sedimentary environments and for supporting groundwater evaluation and engineering investigations in data-limited settings. Full article

Focusing on the West African Craton (WAC) as a test bed, this protocol-oriented scoping review synthesizes indicators for orogenic gold and translates them into an auditable, map-first checklist that separates Fertility and Preservation, while deliberately deferring any performance estimation to a blinded, out-of-sample evaluation. There is a need for a transparent, auditable, and field-ready framework that integrates geological, structural, geophysical, and geochemical evidence. We (i) synthesize the state of knowledge into a map-first, reproducible targeting checklist, (ii) formalize an indicator decision matrix that separates Fertility from Preservation factors, and (iii) specify a deferred, out-of-sample evaluation protocol to quantify performance. We conduct a Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)-style scoping review (2010–2025) and codify commonly used indicators (e.g., transpressional jogs, lineament density, proximity to tonalite-trondhjemite-granodiorite (TTG)/tonalite contacts, Sr/Y proxies). Indicators are operationalized as auditable pass/fail rules and assembled into a decision matrix with explicit uncertainty handling and risk logging. We further define a deferred evaluation protocol using classification and ranking metrics (receiver operating characteristic (ROC) and precision–recall (PR) curves, odds ratios), ablation/sensitivity tests, and district-level threshold calibration. We deliver (1) a unified, auditable checklist with default (tunable) thresholds; (2) an indicator decision matrix that disentangles Fertility vs. Preservation signals; and (3) a deferred evaluation protocol enabling a reproducible, out-of-sample assessment without inflating apparent performance. All numerical thresholds reported here are explicit placeholders that facilitate transparency and auditability; they are not optimized. A properly blocked train/validation/test scheme, operating-point selection criteria, null models, and uncertainty procedures are prespecified for future evaluation. By publishing the checklist, data lineage, and audit-log schema now—without performance claims—we enable reproducible adoption and stress-test the framework ahead of calibration. Full article

Coal texture is an important factor in optimizing the characterization of coalbed methane (CBM) reservoirs, directly affecting key reservoir properties such as permeability, gas content, and production potential. This study develops an advanced methodology for coal texture classification in the Zhengzhuang Field of the Qinshui Basin, utilizing well-log data from 86 wells. Initially, 13 geophysical logging parameters were used to characterize the coal seams, resulting in a dataset comprising 2992 data points categorized into Undeformed Coal (UC), Cataclastic Coal (CC), and Granulated Coal (GC) types. After optimizing and refining the data, the dataset was reduced to 8 parameters, then further narrowed to 5 key features for model evaluation. Two primary scenarios were investigated: Scenario 1 included all 8 parameters, while Scenario 2 focused on the 5 most influential features. Five machine learning classifiers Extra Trees, Gradient Boosting, Support Vector Classifier (SVC), Random Forest, and k-Nearest Neighbors (kNN) were applied to classify coal textures. The Extra Trees classifier outperformed all other models, achieving the highest performance across both scenarios. Its peak performance was observed when 20% of the data was used for the test set and 80% for training, where it achieved a Macro F1 Score of 0.998. These findings demonstrate the potential of machine learning for improving coal texture prediction, offering valuable insights into reservoir characterization and enhancing the understanding of gas migration and accumulation processes. This methodology has significant implications for optimizing CBM resource evaluation and extraction strategies, especially in regions with limited sampling availability. Full article

Marine organic-rich shales frequently exhibit anomalously high uranium (U) concentrations, yet the mechanisms governing its enrichment in overmature formations like the Wufeng–Longmaxi shales remain unclear. This study examines the distribution and enrichment patterns of uranium in the Wufeng–Longmaxi shales in typical wells through integrated geochemical and geophysical analyses, supplemented by natural gamma spectral logging data. Key findings include: (1) Multiple (up to three) uranium enrichment events are identified within the Wufeng–Longmaxi sequence, consistently corresponding to shale gas sweet spots. (2) Uranium content shows a clear dependence on organic matter (OM) type, with algal fragments being the primary host of uranium, likely due to incorporation during early diagenesis. Pore-water redox conditions and pH further govern the reduction of U (U⁶⁺) and its subsequent sequestration into organic phases. (3) The equivalent vitrinite reflectance (ER_o) of uranium-rich shales is 0.11%–0.17% higher than that of non-uranium-rich shales, suggesting that uranium enrichment may slightly enhance OM thermal maturity. (4) Uranium distribution is collectively controlled by reducing conditions, volcanic eruptions (e.g., tuff layers), and OM type. Additionally, uranium enrichment provides chronostratigraphic markers that may aid in timing marine black shales. These findings thus offer a mechanistic understanding of uranium enrichment in overmature shales, with direct implications for targeting productive intervals in shale gas systems. Full article

Coal-free zones, particularly scouring zones, reduce recoverable reserves and increase water inrush risk in coal mining. Existing sedimentological, geophysical, and geostatistical methods are often constrained by coring conditions, lithological interpretation accuracy, and geological complexity. Given that well log signals provide the most continuous carriers of geological information, this study integrates Singular Spectrum Analysis (SSA), Maximum Entropy Spectral Analysis (MESA), and Integrated Prediction Error Filter Analysis (INPEFA) to establish a multi-curve framework for analyzing the genesis and logging responses of coal-free zones. A two-stage SSA workflow was applied for noise reduction, and a Trend–Fluctuation Composite (TFC) curve was constructed to enhance depositional rhythm detection. The minimum singular value order (N), naturally derived from SSA-decomposed INPEFA curves, emerged as a quantitative indicator of mine water inrush risk. The results indicate that coal-free zones resulted from inhibited peat-swamp development followed by fluvial scouring and are characterized by dense inflection points and frequent cyclic fluctuations in TFC curves, together with the absence of low anomalies in natural gamma-ray logs. By integrating multi-curve logs, core data, and in-mine three-dimensional direct-current resistivity surveys, the genetic mechanisms and boundaries of coal-free zones were effectively delineated. The proposed framework enhances logging-based stratigraphic interpretation and provides practical support for working face layout and mine water hazard prevention. Full article

This study conducts a cyclostratigraphic analysis of the third member of the Eocene Shahejie Formation (Es3) in the Laizhou Bay Sag, Bohai Bay Basin, to investigate the influences of astronomically driven climate variations on sea-level changes, sedimentation rates, and depositional environments. We integrated high-resolution geophysical well logs, ostracod fossils, and palynological data from Well B-2 for cyclostratigraphic and paleoclimate analyses. Time series analysis identified orbital cyclicity in the natural gamma-ray (GR) log, with its significance confirmed by correlation coefficients and statistical significance tests. By tuning the GR log to the 405 kyr eccentricity cycle, we constructed a ~7.695 Myr floating astronomical timescale. Integrating the preliminary biostratigraphic framework (based on ostracods and palynology) with the La2010d astronomical solution yielded a high-resolution absolute astronomical timescale for the 1317–2594 m interval of Well B-2, spanning from 33.9 to 41.6 Ma. Sedimentary noise modeling reconstructed the Eocene sea-level curve in the study area, revealing that the 1.2 Myr obliquity modulation cycle was a key driver of sea-level fluctuations. The δ¹³C and δ¹⁸O records confirm the presence of the Middle Eocene Climatic Optimum (MECO), indicating that its stratigraphic signature constitutes a robust marker for regional stratigraphic subdivision in the southern Bohai Bay Basin. Our findings provide new insights into the climatic evolution of the Es3 member in the southern Bohai Bay Basin. Full article