Search Results (4,388)

Electrical imaging logging images play a crucial role in petroleum exploration; however, in practical applications, blank strips frequently appear due to instrument malfunctions or data transmission failures, severely compromising geological interpretation and hydrocarbon evaluation. Existing image inpainting methods have limited adaptability to blank strips at different depth scales and exhibit blurred high-resolution geological textures. To address these issues, this paper proposes a blank strip filling method that integrates Arbitrary Kernel Convolution (AKConv) with the Aggregated Contextual-Transformations Generative Adversarial Network (AOT-GAN). Specifically, the adaptive sampling mechanism of AKConv is incorporated into the generator network of AOT-GAN, enabling the model—to effectively capture long-range contextual information and adaptively handle blank strips of varying scales and shapes through multi-scale feature fusion. Experimental results on real oilfield datasets demonstrate that the proposed method achieves significant improvements in PSNR, SSIM, and MAE, exhibiting superior structural preservation and texture sharpness—especially in restoring deep and large-scale blank strips. Furthermore, visual comparisons confirm the method’s superior performance in recovering key geological features, such as bedding continuity and fracture structures, thus providing an effective approach for electrical imaging logging image restoration. Full article

The thermal history of petroliferous basins controls the thermal evolution of source rocks and the diagenetic evolution of reservoirs. However, although various thermal events are common in such basins, previous studies have largely focused on the outcomes of thermal anomalies rather than systematically evaluating the spatiotemporal extent of their thermal effects. This oversight has impeded accurate assessment of source rock maturation and the timing of hydrocarbon accumulation. This study takes the Baiyun Deep Water Area in the Pearl River Mouth Basin as a case study, aiming to identify types of thermal events and systematically evaluate the extent of their impacts using geologic thermometers, numerical simulations, and measured data. Magmatic activity and hydrocarbon charging are two widely distributed types of thermal events in this area. Apatite fission track (AFT) data reveal two magmatic underplating events in the southern part of the area at 20 Ma and 10 Ma, which led to a rapid increase in vitrinite reflectance (R_o) in the overlying strata. COMSOL Multiphysics 6.2 simulations of the B6-1 diapir show that its thermal impact extends laterally up to 10 km, with the Wenchang Formation source rocks within 2 km of the diapir rapidly heating to 310 °C and reaching over-maturity. Abnormally high homogenization temperatures recorded by saline inclusions associated with hydrocarbon inclusions provide evidence of thermal anomalies induced by hydrocarbon charging. By reconstructing the trapping depths of these inclusions, the timing of their formation was determined. Comparison with normal burial-thermal histories indicates that their homogenization temperatures are 20–30 °C higher than the ambient formation temperatures. Current thermal anomalies in the Enping Formation reservoir of Well K18-1, caused by ongoing hydrocarbon charging, were simulated using COMSOL. The results show that hydrocarbon charging only causes mild thermal anomalies confined to the reservoir and adjacent strata, with a temperature increase of about 29 °C. Present-day measured vitrinite reflectance data further confirm that hydrocarbon charging does not lead to an increase in R_o. Clarifying the types and effects of thermal events is essential for accurately reconstructing the thermal evolution of source rocks and the history of hydrocarbon accumulation. This study provides a new methodology for geothermal field research in petroliferous basins. By integrating AFT, R_o, and fluid inclusion analyses, we reveal past thermal events, and through numerical simulation, quantify the spatiotemporal influence of magmatic activity and hydrocarbon charging on the geothermal field. Full article

The Dibaya Granitic and Migmatitic Complex (DGMC), located in the Kanyiki–Kapangu sector of the Kasaï Shield (Congo–Kasaï Craton, Democratic Republic of the Congo), represents a key exposure of Neoarchean continental crust in Central Africa. Despite its geological importance, information on its petrology, geochronology, geochemistry, and structural evolution remains dispersed across historical studies. This contribution presents a structured geological synthesis based exclusively on previously published cartographic, petrographic, structural, and isotopic data. No new analytical data are introduced; rather, existing datasets are systematically compiled, critically reassessed, and integrated into a coherent tectono-thermal framework. Published Rb–Sr and U–Pb ages indicate high-grade metamorphism and widespread migmatitization at ca. 2.72 Ga, followed by granitoid emplacement at ca. 2.65 Ga. Documented mineral assemblages (garnet–biotite–plagioclase–quartz ± K-feldspar ± amphibole) and the absence of reported high-pressure index minerals support high-temperature, moderate-pressure metamorphism consistent with intracrustal reworking. Reported regional geochemical characteristics suggest high-K calc-alkaline, weakly to moderately peraluminous granitoids derived predominantly from reworking of older TTG-type crust. Structural relationships, particularly along the Malafudi corridor, demonstrate strong coupling between deformation, anatexis, and magma emplacement. Collectively, this synthesis formalizes a Neoarchean intracrustal reworking model and provides a structured analytical basis for future high-resolution petrochronological and geochemical investigations. Although no new quantitative datasets are presented, this study provides the first systematic integration of dispersed geological and isotopic information for the Dibaya Complex, establishing a transparent analytical framework for future high-resolution investigations. Full article

Northeast China, situated in the eastern Central Asian Orogenic Belt (CAOB), marks the terminal closure zone of the Paleo-Asian Ocean (PAO) (eastern segment). At present, due to extensive Quaternary cover, the structural deformation characteristics and deep structure of the Solonker Suture Zone in the east of the Nenjiang–Balihan fault remain poorly constrained, which limits our understanding of the tectonic evolution of the PAO. This study integrates deep seismic reflection (DSR) and magnetotelluric (MT) sounding profiles to investigate the crustal structural, sedimentary framework, and tectonic evolution of the oceanic and continental crusts along the western slope of the Songliao Basin. Two regional detachment surfaces (D1 and D2) were identified. The D2 interface demarcates the upper crust’s basal boundary, overlain by multiple high-amplitude monoclinic reflections. The area below the D2 interface exhibits a network structure of arcuate and variably oriented reflections, indicating a dual-layered orogenic structure. The upper crust exhibits distinct structural domains defined by strongly contrasting monoclinal reflections: north-dipping, low-resistivity zones in the southern sector and south-dipping, high-resistivity zones in the northern sector. These oppositely oriented reflections have been interpreted as marking an Early Paleozoic accretionary wedge and oceanic island arc, respectively. Interposed between these opposing structural domains, the Paleozoic to Early Mesozoic forearc basin sequences are preserved, with a pre-Middle Permian oceanic basin identified north of the study area. By integrating characteristics of seismic reflection sequences with regional geological data, this paper clarifies the processes of closure and collision at the northern margin of the PAO (Eastern Segment). Full article

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

The complex architecture and stacking patterns of deepwater turbidite channel sandbodies introduce significant uncertainty in injector–producer connectivity. This uncertainty affects both the mechanisms and the quantitative evaluation of the waterflood sweep. In this study, a representative reservoir in the Niger Delta Basin is selected as a case study. Injector–producer well groups are first classified into three connectivity patterns—coeval, cross-stage, and hybrid based on geological and seismic constraints. Time-lapse seismic data are then interpreted to delineate sweep morphology and to infer the controlling mechanisms associated with each pattern. Coeval connectivity exhibits a relatively uniform and continuous front advance with minimal barriers. Cross-stage connectivity shows fragmented swept regions with pronounced bypassing, and localized preferential breakthrough caused by discontinuous sandbodies and pervasive barriers. Hybrid connectivity is characterized by intermediate behavior, combining features of both patterns. To translate these mechanistic differences into quantitative metrics for development evaluation, an oil–water relative permeability ratio correlation for low viscosity oil is established that remains valid across the full water cut range, thereby overcoming the limitations of conventional semi-log linear correlations at both low and ultra-high water cut stages. Based on this framework, a production data-driven predictive model for waterflood sweep efficiency is derived using production data and steady state flow theory. The model is validated across well groups representing different connectivity patterns. Field application yields a consistent ranking of sweep efficiency: coeval > hybrid > cross-stage, with group average values of 0.86, 0.80, and 0.70, respectively. These results agree with the mechanistic interpretation derived from time-lapse seismic analysis. The proposed methodology provides a practical quantitative framework for evaluating injector–producer connectivity and comparing development strategies in deepwater turbidite channel reservoirs. Full article

Pegmatites with miarolitic cavities have not previously been reported from the Larsemann Hills, East Antarctica, and their age and origin remain poorly constrained. We report the first geochemical and geochronological data for fluorapatite from a newly discovered pegmatite with miarolitic cavities in the Larsemann Hills. Large Fe-rich fluorapatite crystals (up to 5 cm) contain abundant oriented monazite-(Ce) inclusions and display elevated REE (1397–7966 ppm), relatively high Y (945–4192 ppm), and low Sr (52.2–83.5 ppm). Their trace-element signatures plot within the fields of partial melts, high-grade metamorphic rocks, and evolved fluid-rich magmatic systems. U–Pb dating of fluorapatite yields concordant ages of 519 ± 4 Ma (ID-TIMS) and 521 ± 31 Ma (LA-ICP-MS), indicating crystallization during the D₄ stage of the Pan-African orogeny. The isotopic equilibrium between apatite and monazite inclusions suggests synchronous formation and late-stage fluid overprinting. Combined geological, geochemical, and isotopic evidence shows that the pegmatite formed in situ as a product of anatexis of the Broknes paragneisses and evolved within a volatile-rich magmatic–hydrothermal system. These results provide the first direct age constraints on pegmatites with miarolitic cavities in Antarctica and shed new light on the final stages of East Gondwana assembly. Full article

A new version of the linear integral representation method is developed for solving inverse problems in geophysics. This approach is applied to the interpretation of anomalous time-dependent field data. The reconstruction of field elements is reduced to solving a system of linear algebraic equations (SLAE) with an approximately given right-hand side. Since the matrix elements of this system are derived analytically, the modeling process is significantly simplified. The article also analyzes how the approximation quality of a non-stationary field element depends on the observation network geometry, enabling its optimization for more accurate detection of geological properties. The proposed method for solving inverse problems for hyperbolic partial differential equations with constant coefficients can also be applied to data described by systems of nonlinear PDEs, provided the target field is represented as a composition of components differing in magnitude. Finally, the results of non-stationary gravity field modeling are presented. Full article

The synergistic impacts of land use/land cover (LULC) transformations and weather pattern variabilities (WPV) represent a primary driver of hydro-geological instability, threatening agricultural productivity, soil conservation, and water quality. Disentangling the discrete contributions of these stressors to runoff and sediment yield (SY) remains a significant challenge, particularly in complex, confluence-proximal watersheds lacking major hydraulic regulations. This study investigates the Tirumakudalu Narasipura watershed in Karnataka, India, an agriculturally intensive system undergoing rapid peri-urbanization. Leveraging the process-based geospatial interface of the Water Erosion Prediction Project (GeoWEPP), we analyzed hydrological responses over a 24-year period (2000–2023) and projected future trajectories through 2030. To overcome the traditional constraints of GeoWEPP, which was developed for small-scale watersheds (<260 ha), we present a novel upscaling framework utilizing a multi-site multivariate temporal calibration of hydrological response variables to exploit its process-based precision in capturing distributed soil erosion and landscape heterogeneity. This approach is further reinforced by an ancillary data validation to minimize error propagation while model-upscaling. Our findings reveal projected increases in runoff and SY of 14.69% and 49.23%, respectively, between 2000 and 2030. Notably, the sub-decadal acceleration from 2023 to 2030 (17.32% for runoff and 18.51% for SY) underscores a shifting dominance where LULC-driven surface modifications now outweigh climatic variance in forcing hydrologic change. Furthermore, the study quantifies how anthropogenic interventions such as strategic crop selection, tillage intensity, and irrigation regimes act as critical determinants of topsoil preservation. These results provide a scalable, economically feasible framework for precision land stewardship and sustainable watershed management in rapidly developing tropical landscapes. Full article

The Middle Tonian tectonic setting of the Jiangnan Orogen, South China, remains intensely debated, and is centered on two competing models: subduction–collision versus mantle plume. This study addresses this critical knowledge gap through an integrated, multi-proxy investigation of the Middle Tonian Fanjingshan Group. This region preserves a continuous volcano-sedimentary and magmatic record, offering key insights into the orogen’s full lifecycle. To test these hypotheses, we employed a synthesis of geological survey, sediment provenance analysis, detrital zircon U-Pb geochronology of clastic rocks to determine sediment provenance and basin evolution, and petrogenetic study of coeval magmatic suites (pillow lava, mafic–ultramafic sills, and granitoids) to evaluate their magmatic processes and tectonic setting. Analysis of 1736 detrital zircon U-Pb ages from Middle Tonian strata reveals a four-stage provenance evolution: (1) SW Yangtze sources in a passive margin basin before 870 Ma; (2) bidirectional sources in an 870–835 Ma arc-derived basin; (3) syn-collisional detritus during 835–820 Ma amalgamation; and (4) post-collisional and northern Yangtze inputs in an 800 Ma rifting basin. Geochemical data from ~845–840 Ma basalts and coeval sills reveal calc-alkaline affinities and marked subduction-fluid signatures. Their calculated mantle potential temperature (1404 °C) is significantly lower than that expected for plume-derived melts (1570 °C), which is consistent with melting in a subduction-modified mantle wedge, supporting a continental rear-arc basin setting. The ~845–832 Ma mafic–ultramafic sills exhibit symmetrical geochemical zoning and two-stage emplacement, recording sustained magma recharge in the rear-arc basin. Furthermore, the ~830 Ma Fanjingshan granite is identified as a crust-derived, syn-collisional S-type granite. Synthesizing these findings, we demonstrate that the sedimentary and magmatic records collectively point to plate margin setting. A four-stage tectonic model is suggested: (1) pre-870 Ma passive margin without significant magmatic activity; (2) 870–835 Ma continental arc development at an active continental margin; (3) 835–820 Ma Yangtze–Cathaysia collision; and (4) post-820 Ma post-orogenic rifting. This work provides a robust regional case study, demonstrating that integrating records of deep magmatic processes with coeval shifts in sedimentary provenance and basin architecture is essential to reconstruct the complete evolution of ancient orogens. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

As mineral exploration increasingly targets deeper and more geologically complex terrains, the need for reliable predictive models becomes critical to mitigating exploration risk and improving cost efficiency. Correspondingly, the effectiveness of deep mineral exploration strategies depends substantially on the effectiveness and precision of three-dimensional mineral prospectivity mapping (3D MPM) models. However, the inherent spatial non-stationarity—where ore grade variability changes across geological domains—and the strongly skewed distribution of high-grade samples present a dual challenge. Conventional methods, which primarily rely on mean-based regression, often struggle to adequately address this dual challenge, limiting their predictive performance in complex geological settings. To address these issues, this paper proposes a pinball-loss-guided, global–local fusion Transformer model within a unified framework for 3D MPM. It leverages a multi-head self-attention mechanism with global–local fusion to capture long-range dependencies and global geological contexts, while incorporating local feature extraction modules to adaptively model spatially varying mineralization controls, jointly optimized through a pinball loss function to address mineralization distribution skewness. The proposed framework was first rigorously evaluated using the Xiadian gold deposit as a case study. Bootstrap analysis of the ablation experiments confirmed its predictive performance in terms of quantile-specific accuracy and prediction interval (PI) calibration. Ten rounds of random data splits provided further confirmation of the model’s stability. Subsequently, the validated model was applied to prospectivity mapping in unexplored regions, leading to the delineation of several high-potential exploration targets. Finally, comparative analyses with state-of-the-art machine learning methods were conducted, which further validated the competitive fitting capability of the proposed framework. Full article

Geochemical anomaly detection is a critical stage in mineral exploration, playing a key role in predicting potential mineral targets. Traditional methodologies often struggle to integrate the spatial structure of geochemical data with underlying geological constraints effectively. To address this limitation, we propose GeoCLA, a geochemical anomaly detection framework that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an Attention Mechanism (AM). This integrated spatial-attention architecture captures complex correlations among multiple features to improve anomaly identification. The method constructs spatial sequential samples from geochemical data. The CNNs extract local spatial patterns, the BiLSTM models sequential dependencies, and the AM enhances the representation of critical features. Anomaly scores are computed using the reconstruction error between the model output and the original data. In addition, a fault-distance weighting factor is incorporated to build a comprehensive anomaly evaluation index. The proposed model was applied to the Hatu gold district in Xinjiang, China. Both visual analysis and quantitative evaluation demonstrate effectiveness, achieving a ROC-AUC of 0.86 and a mineral occurrence coverage rate of 97% within moderate-to-high anomaly prospective areas, significantly outperforming baseline methods. Full article

Natural gas hydrates, as an abundant potential energy resource, are widely present in marine sediments. In this paper, a novel method using color recognition technology is proposed for reconstructing marine hydrate reservoirs. By identifying the red, green, and blue values of image colors within the study area’s grid, numerical values are assigned and translated into geological parameters. These parameters are then input into the Computer Modeling Group software to establish heterogeneous reservoirs, and numerical simulations are conducted. The results indicate that this method successfully establishes a correspondence between color features and geological parameters. The reconstructed model images exhibit a high degree of consistency with the original images, allowing for precise parameter readings. The method was applied to hydrate reservoirs in the second trial production area of the South China Sea, the Shenhu SH2 area, and the Nankai Trough. The cumulative gas production obtained through numerical simulation of the reconstructed models closely matched the known production data, with discrepancies of 3.5%, 0.9%, and 7.6%, respectively. These findings confirm the reliability of the model, providing valuable insights for future studies on heterogeneous hydrate reservoirs and extending its application prospects to heterogeneous oil and gas reservoir research. Full article

Seismic attributes are widely used to enhance the interpretation of structural, stratigraphic, and lithologic features in subsurface data. Their effectiveness, however, can be limited by noise, resolution constraints, and processing artifacts. This study suggests new seismic attributes computed using 2D-Variational Mode Decomposition (2D-VMD), which are specifically Mode-Weighted Spectral Discontinuity (MWSD) (in Module and Phase modes), VMD-Directionality Coherence (VDC), Instantaneous Frequency Concentration (IFC-VMD), and Instantaneous Bandwidth Dispersion (IBD-VMD). The proposed 2D-VMD-based attributes are compared with seven key conventional seismic attributes: dip, azimuth, chaos, coherence (semblance), curvature (mean curvature), instantaneous frequency, and instantaneous bandwidth (Hilbert transform). Through applications on simulated and real seismic data, each method is compared in terms of its ability to enhance attribute stability, resolution, and interpretability while mitigating limitations such as noise sensitivity and loss of detail. Results indicate that MWSD (Module) is optimal for amplitude stability, MWSD (Phase) for phase-sensitive applications, VDC for high-resolution structural delineation, IFC-VMD for complex geological settings, and IBD-VMD for abrupt feature changes. The findings demonstrate that these new 2D-VMD-based techniques provide significant advantages over traditional approaches and that combining complementary methods can further improve seismic interpretation outcomes. Full article