Abstract Faulted reservoirs are key targets for geothermal resources as well as lithium extraction but their exploration at great depth faces great technological challenges. Recent advancements in drone and magnetic sensors have enabled the integration of measuring systems on light drones, allowing a great increase of resolution of measurements. This study presents the successful execution of a very-high resolution aeromagnetic survey (VHRAM) at 50 meters above ground level using drones. The results were compared with regional interpretations of the Upper Rhine Graben from a helicopterborne survey and seismic data. The drone-based survey provided a significant increase in resolution, enhancing the interpretation of local geological features, particularly the basement rock structures and the pre- and syn-rift sediments in the fractured areas along the basin borders. A key finding was the ability to detect structural features in the basement at depths of several kilometers which were not observable in the regional survey and interpretation from seismic data. This very high resolution data, when combined with a reinterpretation of historical seismic lines, allowed for a refinement of the geological model, highlighting a structural node in the area. Although the UAV survey covered a relatively small area, its impact was significant, offering valuable insights at the scale of typical exploration permits. These results validate the feasibility and effectiveness of very-high-resolution magnetic surveys (VHRAM) using lightweight drones at low altitudes for fault network characterization in geothermal resource exploration.
Abstract The long-term growth of a continental-shelf delta records combined sea-level fluctuations and sediment supply, by mixed hydrodynamic processes; consequently, its morphology typically features complex asymmetry. Using extensive 3D seismic data, cores, well logs, and provenance data, we present a systematic anatomy of the Early–Middle Miocene Paleo-Pearl River Delta (PPRD). Results show that fifteen depositional sequences can be identified, spanning T60 (~23 Ma) to T35 (~13.8 Ma). Each sequence exhibits a typical four-fold systems tract structure, from each showing typical deltaic planform morphologies and sedimentary facies. Mineralogical data reveal a distinct two-source pattern: the western branch mainly supplied the Enping Sag, whereas the eastern branch fed the Xijiang and Huizhou sags. This pattern is corroborated by 2D and 3D seismic data. Seismic geomorphology further shows an asymmetric morphology, with river channels and mouth bars deflected southwestward and beach and shelf sand ridges widening in the same direction. This asymmetry records a dominant southwestward paleoflow. Considering modern hydrodynamics and regional paleoclimatic evidence, we propose that monsoon-driven longshore currents and the Guangdong Coastal Current (GDCC) likely generated this flow. Reconstructing this paleocurrent system improves our understanding of sediment dynamics and reservoir prediction in the PPRD and comparable marine deltas.
Abstract Critical minerals (CM) are essential for emerging technologies; however, their exploration remains challenging because they occur in complex geological settings and often exhibit weak seismic expression. Volcanic facies (VF) are one of their sources of occurrence, where minerals such as lead, cobalt, copper, and nickel often accumulate and scatter due to hydrothermal processes. This study presents a facies-specific machine learning (ML) workflow that integrates a multi-attribute approach with supervised and unsupervised ML, aiming to resolve VF in a complex geologic setting in the Otway Basin, South Australia. Twenty-two seismic attributes were calculated from which facies-specific attribute combinations were selected. Attributes were then used as input into Self-Organizing Maps (SOM) and Independent Component Analysis (ICA) to find sills, lava flows, and dykes, while Probabilistic Neural Networks (PNN) was applied for supervised classification of pyroclastics, mounds, and laccoliths that were not identified using unsupervised methods. Results indicate that SOM better discriminates between sills and lava and effectively identifies dyke geometry. ICA improved the delineation of dyke boundaries and enhanced the contrast between the intrusions and the background medium, revealing features that were less distinct in SOM results. PNN successfully resolved complex features such as laccoliths. Our methodology demonstrates that specific attribute combinations processed through SOM, ICA, and PNN can effectively discriminate between closely related volcanic features that are structurally favorable for CM accumulation.
Abstract Monitoring injected CO2 saturation is critical for carbon capture and storage (CCS) projects, but its quantification remains challenging due to complex fluid-rock interactions and the subtle seismic signatures of partial saturation. Saturation analysis is an integral aspect, necessitating the use of all available information for estimation. Gas-bearing reservoirs with higher gas saturation often exhibit high attenuation, a key property we leverage. Here, we present an integrated approach combining a physics-guided rock physics model with a multi-parameter, hierarchical viscoelastic full waveform inversion (Q-FWI) to quantify and characterize subsurface CO2 saturation. For this purpose, we adopt a modified-CPET rock physics model to theoretically link elastic (Vp, Vs, ρ) and viscoelastic (Qp, Qs) properties to saturation states. By simulating extreme fluid distribution processes (drainage and imbibition), our approach addresses the scarcity of reliable attenuation measurements and characterizes the distinct effects of partial saturation. We then introduce a multiparameter hierarchical FWI workflow to estimate these subsurface properties. Our Q-FWI sequentially inverts for Vp, Vs and ρ, before estimating Qκ and Qμ simultaneously. The algorithm yields promising results on the synthetic Sleipner model with a multi-component OBN setup, demonstrating its accuracy. The final inverted models for the elastic and viscoelastic subsurface properties (Vp, Vs, ρ, Qκ, and Qμ) delineate the plume geometry and are used to jointly interpret CO2 saturation using Vp, Qp, well logs, and core data-based relationships using a most sensitive regime-dependent strategy based on their sensitivity to different saturation ranges. By rigorously integrating seismic wave imaging (Q-FWI) with rock physics modeling, we establish a robust foundation for quantitative CO2 saturation analysis in complex geologic environments. Our strategy extends readily to applications such as enhanced oil recovery and hydrogen storage.
Abstract This study reported pore pressure prediction challenges in gas hydrate-bearing sediments of the offshore Rakhine–Andaman province in the Bay of Bengal. The seismic velocity-based methods failed to correctly differentiate between hydrate effects and true overpressure. The primary objective was to apply and validate an inverse pressure–temperature modeling approach to improve pore pressure estimation and enhance drilling safety in frontier gas hydrate-prone offshore environments. The method incorporated seismic data interpretation to map the base of the gas hydrate stability zone with inverse modeling of hydrate phase equilibrium equations. The main inputs comprised hydrate thickness, seabed temperature, geothermal gradient, and pressure–temperature relations. The estimated pore pressures were compared against conventional seismic velocity-based predictions and supported using actual well observations. The results demonstrated that the gas hydrate stability zone-related empirical phase equilibrium approach delivered better pore pressure estimates with minimal variation, e.g., 0.55 MPa (=80 psi), and good agreement with observed well data. By contrast, the conventional seismic velocity-based method substantially overestimated pore pressure between 1.4 and 2.8 MPa, likely due to contradictory velocity responses of hydrates and overpressure zones. This can potentially lead to inaccurate mud-weight design and enhanced drilling risk. Field data confirmed that actual pore pressure was only slightly above hydrostatic, i.e., 0.14–0.21 MPa, supporting the validity of this inverse pressure–temperature approach and thus potentially reducing geohazards and allowing safer well planning in gas hydrate provinces. The technique was compared to published pore pressure observations from the Krishna–Godavari (KG) and Mahanadi Basins, confirming its robustness in diverse geographies.
