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The target reservoir is a typical blocky bottom water reservoir. There are several interlayers of varying scales inside it, which impact the characteristics of the oil production and water breakthrough curves of the wells in the water flooding development oilfield, resulting in strong heterogeneity within the reservoir. The Sangtamu Oilfield has an average well spacing of approximately 600 m, causing a challenge in accurately identifying the range of small-scale interlayer spreading. This, in addition, challenges the subsequent 3D geological modeling process. This paper proposes a method to accurately characterize the size of various interlayers in a reservoir by using production dynamics data from bottom water reservoirs to invert the interlayer scale. By classifying the water breakthrough curve styles derived from theoretical testing, the range of reservoir internal interlayer scales is compared and inverted based on the actual water breakthrough styles of wells in the Sangtamu oil field. In the process, many interlayer scales derived from various types of geologic data acquisition were combined to form a quantitative geologic knowledge database of interlayers that synthesizes both dynamic and static data. This method has been applied in the Tarim Basin's Sangtamu Oilfield, where the reservoir numerical simulation history was used to fit the model. This resulted in an overall fitting coincidence rate of 96% in the Sangtamu Oilfield and a single-well fitting coincidence rate of over 90% in well LN14. This method offers a new viewpoint on characterizing the interlayer's scale in the area of the non-dense well network. [ABSTRACT FROM AUTHOR]

Utilizing short-period dense seismic arrays, ambient noise tomography has proven effective in delineating continuous geological structures, a task critical for characterizing shale gas reservoir configurations. This study deployed 153 short-period seismic stations across the Xiahuayuan District in Zhangjiakou, a region with prospective shale gas deposits, to perform an ambient noise tomography survey. Through a meticulous process involving cross-correlation analysis, dispersion curve extraction, and subsequent inversion, a three-dimensional velocity structure model of the area was constructed. The model discerns subtle velocity changes within the 0–3 km depth interval, achieving a horizontal resolution of approximately 1.5 km in the 0–3 km stratum, thereby effectively delineating the shale reservoir structure. Integration of the velocity model with regional geological data facilitated a comprehensive interpretation and structural analysis of the prospective shale gas zone. Low-velocity anomalies observed within the velocity structure correspond to the spatial distribution of the Xiahuayuan Formation, likely attributable to the prevalent stratum of mudstone shale deposits within this formation. Employing a binary stratigraphic model, the study predicted shale content based on the velocity structure, with predictions exhibiting a moderate correlation (correlation coefficient of 0.58) with empirical data. This suggests the presented method as a viable rapid estimation technique for assessing the shale content of target strata. [ABSTRACT FROM AUTHOR]

This study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) to explore the relationships between environmental factors and the occurrence of various geological hazards, including landslides, slope failures, collapses, ground subsidence, and debris flows. The key findings reveal that annual average precipitation (Pre), profile curvature (Pro_cur), and slope (Slo) are the primary factors influencing the composite geological hazard index (GI) across the province. Importantly, the relationships between these key factors and GI exhibit spatial variability, as evidenced by the random intercept and slope models, highlighting the need for customized mitigation strategies. Additionally, the study demonstrates that land use patterns and stratigraphic stratum lithology significantly impact the cluster-specific relationships between the key factors and GI, emphasizing the importance of natural resource management for effective geological hazard mitigation. The proposed integrated framework provides valuable insights for policymakers and resource managers to develop spatially-aware strategies for geological hazard risk reduction and climate change adaptation. [ABSTRACT FROM AUTHOR]

The comprehension of geothermal systems involves the efficient integration of geological, geophysical and geochemical tools that are crucial in unraveling the distinct features inherent in geothermal reservoirs. We provide a first approach to comprehending the geologically complex geothermal system in the Aachen area, which has been known for its natural thermal spring occurrences since Roman times. Through a comprehensive analysis involving geochemical interpretation of water samples, a review of 2D seismic profiles, stress analysis, and surface geology, a dynamic model has been built, which serves as a conceptual framework providing a clearer understanding of the system. The model characterizes a non-magmatic, detachment fault-controlled convective thermal system, wherein the reservoir exhibits mixed properties of the mainly Devonian carbonate rocks. NW–SE directed fault lines play a pivotal role in fluid transport, enabling the ascent of thermal waters without the need for additional energy. We additionally conducted magnetotelluric (MT) surveys and analyzed apparent resistivity and impedance values obtained through forward modeling, along with an assessment of noise levels. These findings contribute to evaluating the potential use of MT methods in further evaluating the study area and for geothermal energy exploration in general. [ABSTRACT FROM AUTHOR]

This study aims to improve the efficiency of mineral exploration by introducing a novel application of Deep Convolutional Generative Adversarial Networks (DCGANs) to augment geological evidence layers. By training a DCGAN model with existing geological, geochemical, and remote sensing data, we have synthesized new, plausible layers of evidence that reveal unrecognized patterns and correlations. This approach deepens the understanding of the controlling factors in the formation of mineral deposits. The implications of this research are significant and could improve the efficiency and success rate of mineral exploration projects by providing more reliable and comprehensive data for decision-making. The predictive map created using the proposed feature augmentation technique covered all known deposits in only 18% of the study area. [ABSTRACT FROM AUTHOR]

The electrical resistivity tomography (ERT) method has been increasingly integrated with hydrogeological risk mitigation strategies to monitor the internal conditions and the stability of natural and artificial slopes. In this paper, we discuss a case study in which numerical simulations were essential to validate the interpretation of the resistivity images obtained from an ERT monitoring system installed on a critical slope in Italy. An initial analysis of the monitoring data after rainfall events in the study site showed that the resistivity values were decreased only in the central zone along the ERT line, but they were increased in the two sides of the profile. Opposite behaviors were observed during the drying processes following the rainfall events. Core samples show complex geology at the study site, which might justify uneven responses of the different subsurface bodies to meteorological events. However, we decided to investigate the possible inversion artifacts resulting from the individual inversion of the tomographic sections. Forward modeling simulations on simplified time-lapse models of the study site were performed to explore this problem and to compare the individual and time-lapse inversions. Synthetic tests confirmed the nature of these unexpected behaviors and assessed the absolute necessity of a time-lapse approach for a correct inversion of monitoring data in the presence of a complex geological model such as the one of this case study. By applying the time-lapse inversion approach to the real data, the inversion artifact problem was substantially solved, arriving after the proper calibration of the inversion parameters, mainly the time-lapse damping factor and the spatial and temporal roughness constraints, to a reduction in the inversion artifacts to less than 5%. [ABSTRACT FROM AUTHOR]

Controlled-source electromagnetic method (CSEM), as a significant geophysical exploration technique, plays a crucial role in imaging subsurface structures. To enhance the accuracy and efficiency of CSEM simulations, this paper introduces a 3D unstructured vector finite element numerical simulation method based on the third-type boundary condition. Vector finite elements are particularly suitable for handling discontinuities in the electric field normal. They automatically satisfy tangential field continuity and zero-divergence requirements, providing a solid foundation for forward modeling in the CSEM. The adoption of the third-type boundary condition aims to reduce computational scale while ensuring higher simulation accuracy. Using this method, we conducted detailed numerical simulations on various models, including layered models, single-anomaly models, composite-anomaly models, and layered-anomaly models. The experimental results demonstrate that the algorithm accurately reproduces the electromagnetic responses of various geological models. It also exhibits superior computational accuracy under low-frequency conditions, outperforming traditional simulation methods. In summary, the 3D unstructured vector finite element numerical simulation method proposed in this study offers an efficient and reliable solution for CSEM, which is of great significance for advancing CSEM technology, especially in inversion techniques and data interpretation. Future work will focus on further optimizing algorithm performance and exploring its application potential in complex geological environments. [ABSTRACT FROM AUTHOR]

The chances of discovering hidden deposits are higher when exploring deeper into known deposits or historic mines, compared to broad-scale regional exploration. Machine learning algorithms and three-dimensional modeling can effectively identify deep targets and provide quantitative predictions of potential resources. This research paper presents a proposed workflow that utilizes random forest algorithms and a three-dimensional model incorporating geological factors such as strata, lithology, alteration, and primary halo to enhance the accuracy of exploration predictions. The study involved collecting 7949 rock samples from 34 boreholes in eight exploration lines at the Xiongcun No. 2 deposit, and performing geochemical analysis calculations on 18 elements. The methodologies employed can be summarized as follows: (1) establishing and preprocessing the geological dataset of the Xiongcun No. 2 deposit, followed by multivariate statistical analysis, (2) delineating primary halo zoning sequences to identify potential mineralization at greater depths, (3) constructing three-dimensional models incorporating geological and geochemical mineralization information, and (4) utilizing the random forest algorithm to extract exploration criteria and quantitatively predict deep exploration targets. The results indicate a significant mineralization located 300 m to the west–northwest of the No. 2 deposit, within the downward extension of the control depth. The three-dimensional model of the target volume reveals the presence of approximately 0.33 million tons of copper (Cu), 7.6 tons of gold (Au), and 22.8 tons of silver (Ag). [ABSTRACT FROM AUTHOR]

The study and exploration of the subsurface requires the construction of geological models. This task can be difficult, especially in complex geological settings, with various unconformities. These models are constructed from seismic or well data, which can be sparse and noisy. In this paper, we propose a new method to compute a stratigraphic function that represents geological layers in arbitrary settings. This function interpolates the data using piecewise quadratic C 1 Powell–Sabin splines and is regularized via a self-adaptive diffusion scheme. For the discretization, we use Powell–Sabin splines on triangular meshes. Compared to classical interpolation methods, the use of piecewise quadratic splines has two major advantages. First, they have the ability to produce surfaces of higher smoothness and regularity. Second, it is straightforward to discretize high-order smoothness energies like the squared Hessian energy. The regularization is considered as the most challenging part of any implicit modeling approach. Often, existing regularization methods produce inconsistent geological models, in particular for data with high thickness variations. To handle this kind of data, we propose a new scheme in which a diffusion term is introduced and iteratively adapted to the shapes and variations in the data while minimizing the interpolation error. [ABSTRACT FROM AUTHOR]

Gravity inversion methods are able to recover density distributions in the Earth but they require strong constraints based on various prior information. In the present study, our aim is to invert gravity anomalies using existing geological models and density information in orogenic areas like the extensively studied Pyrenees region. Geophysical exploration for resource assessment using gravity and seismic data already accessible in this area has been performed to identify potential fluid/gas reservoirs of economic significance. For a given pre-existing model defined by different geological units and summarizing our knowledge of the area, we aim at retrieving the density distribution within each unit. For this, we use an Alternate Direction Multiplier Method to perform gravity data inversion by constrained by Interval Bound Constraints (IBC-ADMM) defined as bounding distribution intervals of possible density values. To estimate these bounding intervals, we first use a prior density model geologically compatible and obtained using geological information, gravity modelling, seismological data and seismic models. In a second step, we apply a Taguchi statistical analysis on representative density variations inside each unit of the prior density model to estimate their impact on data residuals, and reduce the parameter space prior to inversion. We perform the gravity data inversion constrained by these Taguchi-derived density intervals and we estimate model uncertainties for these density intervals. We apply the technique to the entire Pyrenees range at 2 km resolution. The sensitivity analysis shows that the strongest variations on gravity data are obtained essentially in the Axial Zone, in the middle and lower crust, and in the Upper mantle. Inverted densities are compared to the prior model. They are increased in the central part of the Pyrenees Axial Zone while, in the deeper structures (from middle crust down to upper mantle), they are decreased in the Southern (Spanish) Iberian upper mantle and increased in the Northern (French) Eurasian Upper mantle. A possible shortening of the Iberian lower crust–upper mantle system can be assumed below the western part of the Axial Zone. [ABSTRACT FROM AUTHOR]

Three-dimensional geological modelling algorithms can generate multiple models that fit various mathematical and geometrical constraints. The results, however, are often meaningless to geological experts if the models do not respect accepted geological principles. This is problematic as use of the models is expected for various downstream purposes, such as hazard risk assessment, flow characterization, reservoir estimation, geological storage, or mineral and energy exploration. Verification of the geological reasonableness of such models is therefore important. If implausible models can be identified and eliminated, it will save countless hours and computational and human resources. To begin assessing geological reasonableness, we develop a framework for checking model consistency with geological knowledge and test it with a proof-of-concept tool. The framework consists of a space of consistent and inconsistent geological situations that can hold between a pair of geological objects, and the tool assesses a model's geological relations against the space to identify (in)consistent situations. The tool is successfully applied to several case studies as a first promising step toward automated assessment of geological reasonableness. [ABSTRACT FROM AUTHOR]

Faults can fundamentally change a groundwater flow regime and represent a major source of uncertainty in groundwater studies. Much research has been devoted to uncertainty around their location and their barrier‐conduit behavior. However, fault timing is one aspect of fault uncertainty that appears to be somewhat overlooked. Many faulted models feature consistent layer offsets, thereby presuming that block faulting has occurred recently and almost instantaneously. Additionally, barrier and/or conduit behavior is often shown to extend vertically through all layers when a fault may in fact terminate well below‐ground surface. In this study, we create three plausible geological interpretations for a transect in the Perth Basin. Adjacent boreholes show stratigraphic offsets and thickening which indicate faulting; however, fault timing is unknown. Flow modeling demonstrates that the model with the most recent faulting shows profoundly different flow patterns due to aquifer juxtaposition. Additionally, multiple realizations with stochastically generated parameter sets for layer, fault core, and fault damage zone conductivity show that fault timing influences flow more than layer or fault zone conductivity. Finally, fault conduit behavior that penetrates aquitards has significant implications for transport, while fault barrier behavior has surprisingly little. This research advocates for adequate data collection where faults may cause breaches in aquitards due to layer offsets or conduit behavior in the damage zone. It also promotes the use of multiple geological models to address structural uncertainty, and highlights some of the hurdles in doing so such as computational expense and the availability of seamless geological‐flow modeling workflows. [ABSTRACT FROM AUTHOR]

Carbonate stringers are defined as a slab of carbonate bodies encased inside salt. In Oman, the intra-salt carbonate stringers are a very common target, especially in South Oman Salt Basin (SOSB). These stringers contain a large amount of hydrocarbon resources. In this study, the behavior of carbonate stringer bodies hosted in Ara salt structures from the SOSB is investigated. Both synthetic and real seismic data are use. A geological model is created from the structural interpretation of a newly acquired and processed seismic data from the area. The synthetic model is used to generate different seismic and elastic quantities such as P-wave, S-wave, density, synthetic gathers, and stack seismic data. Different attributes are computed from the synthetic gathers before inverting them to acoustic impedance, shear impedance, and density. In order to help us detect carbonate stringers and predict their distribution,, namely, Artificial Neural Network (ANN), is deployed to combine all the computed attributes into one probability cube. The cube highlights only stringers detected by the trained ANN. The ANN demonstrated a promising capability of detection of the targeted stringers. The above experiment is repeated on real prestack data after converting them to impedances using prestack seismic inversion. Likewise, the ANN was able to delineate the stringers and predict the distribution in the 3-D data. After several tests, a final 3-D distribution model of the stringers was obtained. The cube has allowed us to find new potential zones that might be good prospects in any future drilling in the area. [ABSTRACT FROM AUTHOR]

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To construct an accurate 3D geological model of the grid engineering tower scene, we propose a method for 3D geological modelling of grid engineering towers on the basis of borehole data. The method generates a more accurate 3D geological model by improving the Delaunay triangulation algorithm and combining it with Kriging interpolation to process the borehole data. Furthermore, a detailed adjustment of the model is achieved through the introduction of geological profiles. The 3D geological model is constructed using the operational area of the power grid engineering survey as a case study, thereby accurately reflecting the geological conditions of the region. The results of testing demonstrate that the modelling method in this paper is more accurate than existing methods, and the generated 3D geological model not only provides a scientific foundation for tower placement and line design but also offers valuable insights for advancing the digital transformation of power grid engineering. [ABSTRACT FROM AUTHOR]

Horizontal well hydraulic fracturing technology has significantly enhanced the productivity of shale reservoirs. However, our understanding of the expansion patterns within the complex fracture network and fluid seepage mechanisms under field conditions remains inadequate. Here, this work develops a dynamic geomechanical (DG) model to simulate the complete sequence of operations in hydraulic fracturing. This study utilizes a construction procedure that closely mirrors field practices to establish the DG model. Furthermore, the numerical simulation results of the DG model are calibrated with field data. This work adopts a numerical simulation method that integrates unsteady seepage model for multi-stage fractured horizontal wells with the dilation-recompaction model to develop the DG model. It systematically constructs the geological model of the shale reservoir by utilizing segmented logging data and by segmenting production data. The time series evolution system is developed through an iterative process involving discrete time steps. Results show that the DG model can perform history matching on a multi-stage basis, enabling comprehensive and detailed analysis of the entire reservoir. This process effectively replicates the distribution relationship between each reconstruction zone and the overall productivity. Furthermore, the DG model is capable of accurately simulating the dynamic process of injected high-pressure fluids into the reservoir to fracture the rock and the dynamic evolution law of reservoir properties. Hydraulic fracturing creates a fracture zone that centers on the well's border and spreads outward radially. The injection volume and failure pressure are significantly correlated with the scale of shale reservoir reconstruction. Following the injection of 790.5 m³ of fracturing fluid in the first stage, the fracture half-length can reach around 148 m, essentially fulfilling the design specifications. Permeability can reach up to 86 mD at this moment, and it can even be maintained at the level of 46 mD during production. In conclusion, the DG model broadens the focus of study on the development of shale reservoirs and lays the groundwork for improving productivity and optimizing hydraulic fracturing design. [ABSTRACT FROM AUTHOR]

Instability of landslide accumulation bodies is one of the common geological hazards under the influence of rainfall and water impoundment, especially under the transformation of rainfall patterns caused by global climate changes. Owing to the fact that determining the landslide potential failure mode is vital for preventing landslide disasters, this paper takes the Gongdang landslide as the research object to study the landslide deformation mechanism and predict movement characteristics. Firstly, the geological conditions of the study area and landslide were determined according to the field investigations; secondly, the physical and mechanical parameters of the sliding mass were clarified through laboratory tests. Moreover, the particle flow code (PFC) method was utilized to simulate the potential failure process of the landslide based on the three-dimensional numerical model according to the geological features and the micro-parameters. The results showed that the landslide deformation process lasted approximately 640 s with the stage characteristics of displacement and velocity and presented the evolutionary process with the local instability deformation. The simulation results are of practical significance and application value by effectively illustrating the potential deformation and failure process of the Gongdang landslide, which provides a reference for predicting and preventing the potential failure process of geological hazards in similar engineering through field investigations, laboratory tests, and numerical simulation. [ABSTRACT FROM AUTHOR]

The development and utilization of urban underground space represents a crucial strategy for achieving sustainable urban development. Three-dimensional (3D) geological models provide a data foundation and technical support for research in urban planning and construction, as well as the prevention and control of environmental geological issues. However, current urban 3D geological modeling generally faces the challenge of multi-source heterogeneous modeling data. This often necessitates varying degrees of generalization in data processing, resulting in the majority of current urban 3D geological models being relatively coarse and insufficient to fulfill the demand for detailed geological information in contemporary urban development and management. Therefore, determining how to formulate or optimize the 3D geological modeling schemes to enhance the utilization of multi-source heterogeneous data is a key challenge in current urban 3D geological modeling. This study, taking the 3D geological structure modeling of Wuhan's metropolitan development area (MDA) as an example, develops an automated scheme for standardizing modeling data based on multi-scale geological chronostratigraphy. By utilizing the standardized stratigraphy as a unified and independent geological framework for layered modeling, a high-precision 3D geological model of Wuhan's MDA, characterized by large-scale and ultra-complex geological conditions, is constructed through a methodology that integrates the global discrete constrained points modeling approach with the global layered modeling approach, without generalizing the multi-source heterogeneous modeling data. This research not only holds significant practical implications for the prevention and control of comprehensive urban geological issues in Wuhan but also provides novel technical insights into the methodology of 3D urban geological modeling. [ABSTRACT FROM AUTHOR]

Source time‐reversal imaging based on wave equation theory can achieve high‐precision source location in complex geological models. For the time‐reversal imaging method, the imaging condition is critical to the location accuracy and imaging resolution. The most commonly used imaging condition in time‐reversal imaging is the scalar cross correlation imaging condition. However, scalar cross‐correlation imaging condition removes the directional information of the wavefield through modulus operations to avoid the direct dot product of mutually orthogonal P‐ and S‐waves, preventing the imaging condition from leveraging the wavefield propagation direction to suppress imaging artefacts. We previously tackled this issue by substituting the imaging wavefield with the energy current density vectors of the decoupled wavefield, albeit at the cost of increased computational and storage demands. To balance artifact suppression with reduced computational and memory overhead, this work introduces the Poynting and polarization vectors mixed imaging condition. Poynting and polarization vectors mixed imaging condition utilizes the polarization and propagation direction information of the wavefield by directly dot multiplying the undecoupled velocity polarization vector with the Poynting vector, eliminating the need for P‐ and S‐wave decoupling or additional memory. Compared with scalar cross‐correlation imaging condition, this imaging condition can accurately image data with lower signal‐to‐noise ratios. Its performance is generally consistent with previous work but offers higher computational efficiency and lower memory usage. Synthetic data tests on the half‐space model and the three‐dimensional Marmousi model demonstrate the effectiveness of this method in suppressing imaging artefacts, as well as its efficiency and ease of implementation. [ABSTRACT FROM AUTHOR]

Geological models are built to serve various purposes (e.g. reserve evaluation, rock stability analysis, etc.). Several software packages dedicated to geological modeling and rock stability analysis are currently available. The paper presents the workflow for rock stability analysis performed with Slide3 software from Rocscience. An important step is to build the 3D geological model both from the perspective of geometry and rock properties. Doing this in Slide3 is not an easy task and for this reason, we turned to GEOVIA Surpac. The steps were exemplified for the northern area of the Ruschita marble deposit. [ABSTRACT FROM AUTHOR]

Introduction: This work employs a coupled evaluation model that integrates deterministic coefficients with the Analytic Hierarchy Process to conduct a comprehensive assessment of geological disaster susceptibility in Shenzhen Town, Ninghai County. Methods: Cascading geological disasters induced by typhoons and rainfall in the southeast coastal area of China are a major concern and cause huge losses of life and property every year. To effectively prevent and mitigate such disasters, it is necessary to evaluate the susceptibility of geological disasters. Taking geological disasters in Shenzhen Town, Ninghai County as the research object, eight influencing factors in terms of topographic and geomorphological conditions, engineering geological conditions, and human activities were selected based on the geographic information platform (GIS) in this work. The coupling model of the certainty factor model and analytic hierarchy process method was used to evaluate the susceptibility of geological hazards in the study area. Results: The evaluation results illustrate that the coupling model can accurately and objectively assess the susceptibility of geological hazards in this region, with a high evaluation accuracy of 80.8%. The susceptibility is greatly affected by slope, stratigraphic lithology, and human activities. The areas with extraordinarily high and high susceptibility were identified in the northwestern part of the study, where the ignimbrite is exposed in the steep topography. Discussion: The research method provides a reference for evaluating the susceptibility of geological hazards in the southeastern coastal region of China, and the evaluation results can provide recommendations for decision-making on disaster prevention and mitigation in this region. [ABSTRACT FROM AUTHOR]

The Lintan gold deposit, located in the "gold triangle" of Qianxinan, Guizhou Province, has become a focal point for implementing the "exploring near existing deposits" strategy, aiming to identify another large-scale gold deposit within the region. This study addresses the challenges of deep-edge mineral exploration in the Lintan gold deposit by adopting a metallogenic system perspective. Using a multidisciplinary approach, it integrates geological, geophysical, and geochemical datasets to construct various three-dimensional (3D) visualization and prospectivity models. The research leverages geostatistical theories and methods, multisource digital information analysis, and advanced 3D modeling and visualization techniques to verify mineralization anomalies. These efforts expand the scope of prospectivity evaluation for the deep-edge regions of the Lintan gold deposit into 3D space. In the 3D spatial framework, this study elucidates the metallogenic geological characteristics, geophysical anomalies, and geochemical signatures within the study area. Building upon this foundation, it conducts a comprehensive analysis and evaluation of geological, geochemical, and geophysical prospecting indicators under multisource geoscience datasets. This approach transitions from known to unknown domains, effectively reducing the ambiguities and uncertainties associated with single-source data interpretations. The findings demonstrate that, under the guidance of geological prospectivity models, the effective integration and synthesis of geological, geophysical, and geochemical data can reveal the interrelationships between metallogenic geological bodies and the contributing factors of the metallogenic system. This enables the identification of anomalous information associated with metallogenic geological bodies and facilitates the spatial localization and prediction of target areas for deep-edge mineral resources. The proposed methodology provides novel insights and techniques for deep-edge mineral exploration. Comprehensive analysis indicates significant prospectivity for mineral resource exploration in the deep-edge regions of the Lintan gold deposit. [ABSTRACT FROM AUTHOR]

The Gulf of Suez, which contains Egypt's oldest oil fields, is one of North Africa's most well‐known oil regions. There are more than 80 conventional oil fields in Egypt's Gulf of Suez, some of which have reservoirs that stretch back to the Precambrian and Quaternary. In close proximity to the southern entrance of the Gulf of Suez is the Tawilla West oilfield. The oil field Tawilla West is believed to consist of rotating fault blocks that descend in a south‐west direction. The main producing reservoirs are the Miocene section reservoirs, the Belayim and Kareem sandstones. The current research is focusing on the structural elements affecting this giant field to update the field structural model using the newly processed 3D seismic survey, the acquired data from newly drilled wells and the associated different logging techniques. The seismic information quality varied from poor to fair. The quality of the interpreted stratigraphic horizons and geological faults was mainly controlled by the seismic information quality. The research used seismic attribute analyses to improve interpretation and incorporate additional features, enabling better hydrocarbon potential identification and characterization of the reservoirs. Several geological structure contour maps and cross‐sections were generated to help in delineating and understanding the reservoir's extension. Based on the detailed correlation study, we were able to detect the faults that affected the structure of the Tawilla West field in detail, define their throw amounts and directions, and identify the missed sections across the studied area. This study introduces an updated model scenario to show the differences and their effect on the field development plan and recommendations. By examining subsurface geologic structural characteristics and evaluating petrophysical data, a 3D static reservoir model was created to resolve structural settings and hydrocarbon trapping, providing detailed information on the field and identifying new opportunities for future development. The research discovered that the updated detailed 3D structural model may support the Kareem Reservoir development plans and encourage drilling, workover and dynamic operations to assign development possibilities in the correct area. According to the established model, there are at least three options in the study's attic areas that might boost oil output and oil reserves for the field while avoiding further failures. [ABSTRACT FROM AUTHOR]

Trends are common in regionalized variables, and geostatistical techniques are employed to model these trends, handle non-stationarity, and reduce uncertainty in models. Given the subjectivity involved in modeling and incorporating trends, it is crucial to have tools to identify and improve their inclusion during model development. This study proposes a method to evaluate the significance of trends in geostatistical models. A reference distribution of trend variances derived from stationary unconditional simulations is used to assess the significance of a trend calculated from the available data. Compared to the reference distribution, the trend variance value generates a parameter that offers a quantitative approach to evaluating significance. This parameter indicates the trend's ability to reduce uncertainty in the modeling process. A trend significance classification was established using synthetic variables and validated with various images, including digital elevation models and geological environments (satellite images, seismic data, and outcrop images), covering contexts related to environmental, mining, and petroleum applications. An example from these images was chosen as a case study to demonstrate the potential for reducing uncertainty when incorporating a significant trend in geostatistical models. The significance classification tool enhances understanding of the impact of trends on modeling, particularly regarding uncertainty reduction. It is important to note that more precise models are critical for making more assertive decisions that have significant economic impacts on urban planning projects, petroleum field developments, and mining operations. [ABSTRACT FROM AUTHOR]

We propose a nonisothermal reactive multicomponent multiphase flow model for simulating offshore geological carbon dioxide (CO 2 ) storage. The model considers CO 2 hydration as well as CO 2 dissolution in water in the low-temperature high-pressure deep-ocean environment. This model comprises a chemistry module responsible for all chemical reactions among different phases and a flow module responsible for mass and energy transfer. The chemistry module is based on the mass action law that considers the inhibition effect of salt on CO 2 hydration and dissolution. We implement this model in an open-source Matlab-based code MRST-HYD using a sequential iteration approach. The code has been validated through tests against one theoretical solution and one numerical code, Geoxim, developed at IFP Energies nouvelles. Finally, we apply this code to simulate CO 2 injection into one-dimensional and two-dimensional deep-ocean sediments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have gained popularity in geological deformation monitoring due to their flexibility, cost‐effectiveness, and ease of image acquisition. However, accurate co‐registration of multi‐phase UAV images, essential for effective monitoring, requires numerous ground control points (GCPs), challenging in deformation‐prone areas. This study introduces a UAV‐based monitoring method that eliminates the need for GCPs. Initially, two‐phase images are integrated for joint aerial triangulation. The first‐phase image and tie points undergo bundle adjustment refinement. Control points are identified as tie points with minimal deviation and used to refine the second‐phase image. Three‐dimensional (3D) models are generated from these refined adjustments and differential analysis between the models reveals geological deformation. Experimental results show that this method automatically identifies stable areas and effectively captures both the location and extent of geological deformation, achieving a relative change accuracy of 0.06 m, which is twice the average ground resolution. Compared to traditional aerial triangulation methods without control points, this approach offers more than a tenfold increase in accuracy. The accuracy is on par with methods that utilize multiple control points, indicating the promising potential of this approach for geological deformation monitoring. [ABSTRACT FROM AUTHOR]

This study aims to develop an absolute model of contemporary Vertical Crustal Movements (VCM) and Vertical Land Movements (VLM) in an area of Poland based on GNSS solutions. Velocities at permanent stations were subjected to geological, tectonic, hydrological and mineral information analyses. Reliability analysis and estimation of velocities at individual GNSS stations, comparative analysis of results and spatial analysis were carried out. Vertical velocities were determined using four computational strategies. Daily satellite data in the ITRF2014 system collected from permanent GNSS stations of the Polish part of the ASG EUPOS system were obtained from the Polish Main Office of Surveying and Cartography. All the data were from the 2011 to 2021 time period (approx. 11 years) and obtained in Rinex format. Time series from the Precise Point Positioning (PPP) solution calculated using GipsyX software were used. The absolute vertical crustal velocities obtained for Poland mostly vary between +1.0 and −1.0 mm/yr, which is 95% of the values obtained within local extremes. This region of Poland can be considered tectonically stable and the developed VCM model correlates with the geological and tectonic structure of the region. Taking into account the influence of tectonics, geology, hydrology and location of mineral resources has allowed better interpretation of vertical velocities and correction of the associated model. The proposed computational strategy based on combining data sets developed by different methods gave good results. Plain Language Summary: This study aims to create a detailed model of vertical crustal movements and land movements in Poland using GNSS solutions. We analyzed data from permanent GNSS stations, taking into account relevant geological, tectonic, hydrological, and mineral information. We conducted reliability analysis, estimated velocities at individual stations, compared results, and performed spatial analysis. Four computational strategies were used to determine vertical velocities. Satellite data from 2011 to 2021 were obtained from the ITRF2014 system. The study found that vertical crustal velocities in Poland generally range from +1.0 to −1.0 mm/yr, with some local extremes. Overall, Poland is considered tectonically stable, and the model aligns with the geological and tectonic structure of the region. Considering the influence of tectonics, geology, hydrology, and mineral resources allowed for better interpretation of vertical velocities and model correction. The proposed computational strategy, which combined different methods, yielded positive outcomes. Key Points: The absolute values of VLM and VCM are mostly from +1 to −1 mm/yr. The area of Poland can be considered tectonically stableThe developed VCM model correlates with the geological and tectonic structure of the regionThe method of combining WAV sets based on different computational strategies yielded good results [ABSTRACT FROM AUTHOR]

Reducing the gap between geophysical inversion and geological interpretation can be achieved by integrating geological modelling into geophysical inversion. For this, we use a generalized, iterative level-set gravity inversion scheme in which geological units are deformed automatically. During the inversion process, a regularization term is defined using automated geological modelling to account for geological data and principles. This provides model-dependent geological constraints and encourages geological realism throughout inversion. To alleviate the dependence on the starting model and consider the possibility of features unseen by direct observations, an automated geophysical data-driven method is proposed to insert new rock units in the model. Uncertainty quantification is achieved through the null-space shuttle algorithm, which is used to generate a series of alternative models that are consistent with geophysical data. This methodology is applied to assess the uncertainties of a pre-existing 3-D crustal-scale geological model of the Western Pyrenean orogeny (France, Spain). The area is characterized by a positive gravity anomaly generally attributed to the presence of a shallow mantle body. The impact of variations in shape and density of key crustal and mantle features is investigated. Different scenarios are explored in 3-D space to produce a range of viable, relatively simple crustal-scale models of the area. This application demonstrates the capability and potential of this approach to evaluate alternative interpretations of geophysical data. The results show the plausibility of scenarios with a shorter subducted Iberian lower crust and a denser Axial Zone than in the pre-existing model. [ABSTRACT FROM AUTHOR]

Geophysical data are often integrated into geological data for 3D modeling of underground spaces. However, the existing single-view approach means it is difficult to adequately fuse the valid information between the two types of data, and the complexity of lithological decoding and classification is high. To address this issue, a multi-view ensemble machine learning (ML) framework is proposed. Initially, the original dataset of lithology prediction is constructed by aligning geological and geophysical data with different spatial scales. Next, the dataset is divided into three datasets of structural strength, density, and moisture content according to the lithology properties of the geophysical data. The proposed framework is then used to capture the lithologic characteristics under different views to achieve the prediction of lithologic labels. In this process, a self-attentive mechanism is used to adaptively fuse the valid information under each view. To validate the proposed framework, it is applied to a project in Jiaxing, Zhejiang Province, China. Compared with existing ML methods, the proposed multi-view ensemble ML framework improves modeling accuracy and constructs models with low uncertainty. The framework can be extended to other multi-source data fusion tasks across geoscience domains. [ABSTRACT FROM AUTHOR]

Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to conduct real-time monitoring and early warning of various geological disaster risks. With the growing development of the information age, public attention to disaster relief, casualties, social impact effects, and other related situations has been increasing. Since social media platforms such as Weibo and Twitter contain a vast amount of real-time data related to disaster information before and after a disaster occurs, scientifically and effectively utilizing these data can provide sufficient and reliable information support for disaster relief, post-disaster recovery, and public appeasement efforts. As one of the techniques in natural language processing, the topic model can achieve precise mining and intelligent analysis of valuable information from massive amounts of data on social media to achieve rapid use of thematic models for disaster analysis after a disaster occurs, providing reference for post-disaster-rescue-related work. Therefore, this article first provides an overview of the development process of the topic model. Secondly, based on the technology utilized, the topic models were roughly classified into three categories: traditional topic models, word embedding-based topic models, and neural network-based topic models. Finally, taking the disaster data of "Dongting Lake breach" in Hunan, China as the research object, the application process and effectiveness of the topic model in urban geological disaster information mining were systematically introduced. The research results provide important references for the further practical innovation and expansion of the topic model in the field of disaster information mining. [ABSTRACT FROM AUTHOR]

InSAR has emerged as a leading technique for studying and monitoring ground movements over large areas and across various geodynamic environments. Recent advancements in SAR sensor technology have enabled the acquisition of dense spatial datasets, providing substantial information at regional and national scales. Despite these improvements, classifying and interpreting such vast datasets remains a significant challenge. InSAR analysts and geologists frequently have to manually analyze the time series from Persistent Scatterer Interferometry (PSI) to model the complexity of geological and tectonic phenomena. This process is time-consuming and impractical for large-scale monitoring. Utilizing Artificial Intelligence (AI) to classify and detect deformation processes presents a promising solution. In this study, vertical ground deformation time series from northeastern Italy were obtained from the European Ground Motion Service and classified by experts into different deformation categories. Convolutional and pre-trained neural networks were then trained and tested using both numerical time-series data and trend images. The application of the best performing trained network to test data showed an accuracy of 83 % . Such a result demonstrates that neural networks can successfully identify areas experiencing distinct geodynamic processes, emphasizing the potential of AI to improve PSI data interpretation. [ABSTRACT FROM AUTHOR]

The Mahu sag slope area, which holds significance as an oil and gas resource, still have some underexplored regions because of structural mismatches, presenting a potential challenge to be properly addressed. To resolve, this study conducts a comprehensive investigation concerning structural characteristics, fault combinations, favorable reservoir distribution, reservoir control factors, and oil-water distribution characteristics within the Triassic Baikouquan Formation, evaluating the impact of depositional environments and sedimentary dynamics on reservoir quality. For this purpose, constrained sparse spike inversion and seismic waveform indication inversion were employed to comparatively evaluate oil and gas reservoirs, further integrating petrophysical and geological data with geological modeling to enhance accuracy in complex structural geology and enable high-precision reservoir prediction. The findings elucidated the distribution range of the Baikouquan Formation and the location of oil reservoir sand bodies, as exemplified by well B and identified potential hydrocarbon traps, offering valuable insights into reservoir performance. It demonstrated comparatively reliable effects and considerable predictability power of seismic waveform indication inversion. These outcomes provide a strong foundation for future evaluations and multi-layer system deployment in the region by serving as a novel valuable framework for subsequent development activities not only in the Mahu sag but also in similar regions. [ABSTRACT FROM AUTHOR]

Deep coalbed methane (CBM) resources are enormous and have become a hot topic in the unconventional exploration and development of natural gas. The fractures in CBM reservoirs are important channels for the storage and migration of CBM and control the high production and enrichment of CBM. Therefore, fracture prediction in deep CBM reservoirs is of great significance for the exploration and development of CBM. First, the basic principles of calculating texture attributes by gray-level cooccurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) were introduced. A geological model of the deep CBM reservoirs with fractures was then constructed and subjected to seismic forward simulation. The seismic texture attributes were extracted using the GLCM and GLRLM. The research results indicate that the texture attributes calculated by both methods are responsive to fractures, with the 45° and 135° gray level inhomogeneity texture attributes based on the GLRLM showing better identification effects for fractures. Fracture prediction of a deep CBM reservoir in the Ordos Basin was carried out based on the GLRLM texture attributes, providing an important basis for the efficient development and utilization of deep CBM. [ABSTRACT FROM AUTHOR]

The location algorithm is the core of microseismic monitoring, and the location results directly influence the effectiveness of the subsequent source analysis. We introduced the double-difference (DD) location method in seismic location into microseismic monitoring. We built a travel time equation of the layered velocity model using the ray-tracing theory and analyzed the waveform cross-correlation of the microseismic data using a generalized cross-correlation (GCC) technique. The improved particle swarm algorithm was successfully conducted to improve the randomness of the initial location. We then adopted the DD location method to precisely locate the secondary location, and it showed better location accuracy and computational efficiency than that by a traditional location algorithm based on the simplified geological model. The theoretical simulation and practice results in deep mining show that the proposed method significantly increased the location accuracy, and the results were concentrated and consistent with the field results. Moreover, we verified the importance of the velocity model for location accuracy. This algorithm also increased the ease of identifying fracture regions, providing the necessary theoretical and technical support for the source location of the jointed coal rock mass in deep mining. [ABSTRACT FROM AUTHOR]

Hydraulic conductivity (K) is a crucial parameter in hydrogeology but is highly heterogeneous and anisotropic due to variations in sediment texture, making its large-scale estimation challenging. Traditional laboratory and empirical methods based on grain-size distribution (GSD) analysis from limited data provide local K measurements, resulting in a poor representation of aquifer heterogeneity. In contrast, pumping tests estimate an integrated K value over a section of the aquifer within the cone of depression but still lack the spatial resolution needed to reveal detailed variations in K across larger aquifer extents. In this study, the Di models method was used to simulate local GSD in three-dimensional (3-D) detrital systems. The focus was to explore the potential to estimate K through simulated particle-size fractions derived from a 3-D geological model of the City of Munich. By employing log-cubic interpolation, a complete and accurate representation of the fictive GSD enabled the application of multiple empirical relationships for K estimation. The resulting 3-D K fields preserved the variability in K within each aquifer system. When averaged for each separate aquifer system across different lateral extents, i.e., 50–150 and 550 m, the predicted K values showed success rates of 44–47% with deviations of at least one order of magnitude in 15–19% of cases when compared to 364 K values derived from pumping-test data. The results highlight the ability of the approach to successfully estimate K while accounting for spatial heterogeneity, suggesting its potential for groundwater modeling, aquifer yield assessments and groundwater heat pump system design. [ABSTRACT FROM AUTHOR]

Since its discovery in 2010, the NEAG 2 has been one of the most productive oil fields of the Badr El-Din Petroleum Company (BAPETCO) in the northern Western Desert of Egypt. The Albian-Cenomanian reservoir system has a unique performance but suffers from several issues hindering its production. The latest production report in 2023, NEAG-2 Field was producing 1760 bbls of oil with 36500 bbls of water, i.e., 95% water cut. Despite that, the field has reached a 39% recovery factor but the reservoir forecast suggests a much higher recovery factor. Therefore, the NEAG 2 Field requires a comprehensive geological model to depict its reservoir heterogeneities better. We introduce a solid and integrated workflow to investigate the reservoir characters among different scales of geological heterogeneity and offer solutions to overcome some data gaps. After characterizing the reservoir elements by the structural, stratigraphic, petrographic, and petrophysical analyses, a machine learning-based method was applied to overcome the missing whole rock cores in creating a detailed electro-facies log for all field wells. The Neural-Network algorithm required the facies types to be grouped into definitive reservoir qualities to be applied. The resultant electro-facies log had a very good match with the input logs, which validated the facies grouping. This was followed by the porosity-permeability transforms, estimated from mobility data, to create a permeability curve for all field wells, despite the unavailability of core data. The reservoir was categorized into three rock types, each with a specific range of quality, signifying their different flow abilities which were supported by dynamic data. The Lower Bahariya-Kharita in NEAG 2 was ultimately concluded to be a complex heterogeneous reservoir with varying flow abilities and production behaviors. The recovery factor mismatch is due to unrecovered reserves, and a new production strategy should be introduced to reach the ultimate recovery. This integration of geologic and dynamic data is strongly recommended for any reservoir characterization study to avoid oversimplifying the reservoir system and to design the right reservoir development plan [ABSTRACT FROM AUTHOR]

For a better understanding of the strong heterogeneities of the Mishrif Formation in the H Oilfield of southeast Iraq, the characterization of the carbonate architectures has become one of the key research departments of carbonate rocks. This study aims to reveal the architecture and controlling factors of the carbonate tidal channels in the MB1-2B sub-layer of the Mishrif Formation in response to the delineation of the tidal channels that have hydrocarbon potential. Three architectural elements and three architectural boundaries of the tidal channels were identified by interpreting the cores, well-logging, seismic, and analytical data. The results show that: (1) the architecture characteristics of tidal channels are mainly migrating type in the downstream zone, the side of concave bank of the tidal channels is usually filled with relatively coarse-grained grainstone; (2) the architecture characteristics of tidal channels are mainly swinging type in the upstream zone, showing the high porosity and permeability; (3) the architecture characteristics of tidal channels are mainly vertical-accretion type in the mid-regions, indicating the instantaneous current reversals and high geographical position. This analysis demonstrates that the best reservoir quality within the tidal channels is located in the bend of the tidal channel near the inner lagoon and open sea, it provides the geological models for later exploration and development in the Mishrif Formation. [ABSTRACT FROM AUTHOR]

The Afar region is one of the only places on Earth where magmatic continental rifting and associated ongoing break-up processes are exposed onshore. The several active magmatic segments there are characterized by contrasted morphologies, crustal thicknesses, magma production rates, and magma-tectonic styles. In the Erta Ale Range rift segment, extension is magmatically accommodated, making the range the ideal place to study the magmatic behavior of a mature rift segment. Erta Ale Range comprises sub-segments with magma compositions ranging from basalts to rhyolites, but only the Erta Ale Volcano (EAV) sub-segment is active, where only basaltic compositions have been reported so far. Here, we show for the first time protracted differentiation at EAV that is not expressed volcanically at the surface, but is rather accessible via unique cognate gabbroic and microgabbroic blocks, and recorded by mixing with erupted basaltic magmas. These cognate samples record previously unknown mushy and evolved parts of the EAV plumbing system. To constrain their origin and evolution, we measured the major and trace element compositions of the bulk rocks, interstitial glasses, and melt inclusions. We also measured the oxygen isotopic compositions of olivine crystals, interstitial glasses, and melt inclusions. By combining these results with textural relationships and oxy-thermo-barometry calculations, we discuss magma differentiation and storage conditions, as well as magmatic interactions during transport through the crust. Comparison of our results with rhyolite-MELTS thermodynamic models highlights that protracted fractional crystallization is the main process of magma evolution, and when associated with reactive porous flow is capable of forming the evolved compositions observed (up to 75 wt.% SiO2). We also use the model outputs to quantify distinct steps of igneous differentiation in both shallow and deep crustal reservoirs, and we highlight significant interactions with hydrothermally altered wall rocks. We discuss this model within the geological contexts of the Erta Ale Range rift segment and the larger Afar region, and highlight contrasts with mature oceanic systems to argue that the region is not in the final stages of continental break-up. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The external water pressure of lining is of great significance to the determination of lining thickness and drainage scheme of water-rich deep buried tunnel. The existing research often ignores the analysis of the influence of macroscopic geological conditions on the seepage field, which leads to the lack of reliability evaluation of the external water pressure estimation results and lining design schemes of deep buried tunnels. Based on the Qinling Water Conveyance Tunnel Project of Hanjiang-to-Weihe River Diversion Project, this paper generalizes the deep-buried tunnel in the water-rich area into three representative macro-geological environment models under the watershed, the mountainside area and the river, and studies the influence law and mechanism of geological conditions and engineering measures on the seepage field of surrounding rock and the external water pressure of lining. The results show that: (X) The seepage direction of groundwater around the watershed tunnel is mainly vertical under the condition of the same height of groundwater at the top of the tunnel and impermeable lining. The seepage direction of groundwater around the tunnel under the river is mainly horizontal. The external water pressure reduction coefficient of the watershed tunnel, the tunnel near the mountain area and the tunnel under the river increases in turn. The reduction coefficient of external water pressure of tunnel under watershed increases with the increase of buried depth, which is 0.72,0.77 and 0.84 respectively. The reduction coefficients of the external water pressure of the mountain tunnel and the river tunnel are almost unchanged, which are 0.91, 0.91, 0.93 and 0.95, 0.96, 0.96, respectively. (2) After the same drainage measures are taken, with the increase of buried depth, the reduction coefficients of external water pressure of watershed tunnel are 0.15,0.42,0.64 respectively, and the pressure reduction effect is the best. The external water pressure reduction coefficients of the tunnel near the mountain and the tunnel under the river are 0.48, 0.67, 0.77 and 0.63, 0.80,0.83, respectively, and the pressure reduction effect gradually deteriorates. (3) When the buried depth of the tunnel is 1 200 m, the reduction coefficients of external water pressure of the watershed, mountainside and river tunnel are 0.43~0.73, 0.67~0.89 and 0.80~0.95 respectively with the increase of the permeability coefficient of the surrounding rock, which shows the regularity that the external water pressure increases with the increase of the buried depth of the tunnel. The research results are universal, and the rationality of water pressure resistance design of tunnel lining in water-rich area can be evaluated from the regularity of macroscopic hydrogeological conditions of tunnel and reduction coefficient of external water pressure. [ABSTRACT FROM AUTHOR]

Deep ocean sediments document past environmental changes over space and time. The information gleaned from such deposits allows scientists to test climate models that are used to predict future climate change. However, the causes and consequences of changing climate can be unraveled only if geological data from different regions are synchronized in time so that lead‐lag relationships can be properly established. Synchronization of geological archives across regions requires precise and accurate age models, but available age models are often not sufficiently accurate to rigorously test causality arguments. We therefore propose to launch an international, coordinated effort to revise and recalibrate the dating tools available to paleoclimatologists—that is, the local and regional information obtained from bio‐, magneto‐, and chemo‐stratigraphy as well as radioisotopic geochronology—with the synchronizing tool of astrochronology. Cross‐fertilization of expertise is needed to generate new age models for sediment records from which key climate events have been or can be reconstructed. We expected this initiative could make a significant contribution to the understanding of Earth history, biotic evolution, and particularly, Earth's climate history. Plain Language Summary: Geological records allow scientists to better understand how the natural climate system operates over time and space, but obtaining reliable information requires exact knowledge of the timing of past climate events. Currently, many important climate archives are not sufficiently well synchronized to confidently draw connections between data from different localities. Here we call to the international science community to launch the Time Integrated Matrix for Earth Sciences (TIMES) program with the aim to accurately synchronize age models for critical geological climate records of the last 100 million years. Key Points: Timing is important for comprehending Earth's biological and climatic processes shaping evolution, extinction, and recoveryKey climate proxy records of the last 100 million years are not sufficiently synchronized across regionsA coordinated effort is needed to synchronize regional and global climate proxy records to unravel causes and consequences of climate change [ABSTRACT FROM AUTHOR]

The Ganquan Platform, located in the South China Sea, exhibits distinctive topographic and geomorphological features shaped by complex geological processes. Utilizing high-resolution multibeam bathymetry and multi-channel seismic data, this study provides a comprehensive investigation into the sedimentary evolution and stratigraphic framework of the platform. Morphologically, the platform is identified as an elongated seamount, with water depths ranging from 530 m to 800 m and a maximum elevation of 538.115 m. Seismic facies analysis reveals seven distinct facies, reflecting a dynamic and intricate history of carbonate deposition. The stratigraphic framework delineates three primary evolutionary stages: the growth phase (Early Miocene), characterized by the initial accumulation of carbonate sediments; the flourishing phase (Middle Miocene), marked by extensive deposition and platform expansion; and the submergence phase (Late Miocene), defined by progressive submersion and erosion of carbonate features. This evolutionary trajectory was driven by key factors such as terrigenous clastic input, tectonic activity, sea-level fluctuations, and paleoenvironmental dynamics. The findings contribute to a deeper understanding of the Ganquan Platform's geological history and its role in the broader context of submarine geology in the Xisha region. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the efficiency of various machine learning algorithms integrating with numerical simulations in optimizing oil production for a highly heterogeneous reservoir. An approach leveraging a machine learning workflow for reservoir characterization, history matching, sensitivity analysis, field development and optimization was proposed to accomplish the above goal. A 3D subsurface model representing studied sand-shale sequences was constructed based on geophysical and petrophysical logs, core measurements, and advanced machine learning techniques. After that, a robust sensitivity analysis and history matching process were conducted using a machine learning workflow. The most sensitive control variables were the aquifer properties, permeability heterogeneity in different directions, and water–oil contacts. The history matching results from the constructed geological model showed that the oil rate, water rate, bottom hole pressure, and average reservoir pressure were matched within a 10% deviation from the observed data. Several field development scenarios were generated using the validated model to optimize cumulative oil recovery. Different injection well placement locations, well patterns, and the possibility of converting existing oil-producing wells to water injection wells were investigated. A machine learning-based proxy model was built for the prediction of cumulative oil production and then optimized with hybrid machine learning techniques. The Artificial Neural Network (ANN) algorithm was found to provide higher field cumulative oil production compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) of 3.5% and 26.5%, respectively. Following the detailed proposed machine learning-based workflow, one can effectively decide on the development strategy and apply the findings from this research to their field. [ABSTRACT FROM AUTHOR]

The evaluation of oil- and gas-bearing areas (OGBAs) during the pre-exploration stage has always posed challenges due to the lack of an effective geological evaluation model and validation data. This paper introduces a novel quantitative evaluation method based on the vectorization of key geological factors related to hydrocarbon accumulation. In this study, we focused on the Lianglitage Formation in the Central Uplift Belt and aimed to evaluate the application of the proposed method to the OGBA in the Tarim Basin. First, the reservoir-forming parameters were quantified based on geological analysis and expert experience. Second, the weights of the main parameters were determined using a combination of the gray correlation method and expert knowledge. Finally, the OGBA was evaluated using a multifactor fusion method. The comprehensive evaluation results indicate that the platform margin in the northeastern part of the Katake Uplift shows promise for exploration, while the southern region has a good potential for future exploration. This study emphasizes the significance of selecting key factors and vectorizing evaluation parameter mapping for accurate and quantitative evaluation of an OGBA. The results of this study provide a valuable foundation for evaluating the OGBAs in the Lianglitage Formation within the Tarim Basin and offer a valuable reference for OGBAs in similar regions during the pre-exploration stage. [ABSTRACT FROM AUTHOR]

This study proposes a 3D structural modeling approach based on the discretization of a 2D structural model associated with magnetic data. The approach proposed in this study requires first to identify the parameters involved in the modeling (geographical position, direction, dip direction and dip angle) and then to transform the whole 2D model into fault segments. The first discretization will allow determining the position, the direction and the dip direction of each fault segment. This discretization phase was done by coupling the Excel program to existing GIS software. The last discretization will allow evaluating the variation of inclinations along the fault segment. It required the elaboration of a depth model from the magnetic data grid. It is this model that provided all the necessary data for this last discretization, which was automated thanks to the DipAngle code developed for this study. All these discretizations have provided to the used modeler and all the databases necessary for the realization of the various models. The application of this methodology to the Central Domain of the Mbere Division has made it possible to develop different models that can be used as basic documents in exploration projects. [ABSTRACT FROM AUTHOR]

Three-dimensional geological modeling is essential for simulating natural gas hydrate (NGH) productivity and formulating development strategies. Current approaches primarily concentrate on the single-phase modeling of either hydrate or free gas layers. However, an increasing number of instances suggest that the three-phase coexistence zone, which includes hydrate, gas, and water, is common and has become a focal point of international research, as this type of reservoir may present the most viable opportunities for exploitation. At present, there exists a significant gap in the research regarding modeling techniques for such reservoirs. This study undertakes a comprehensive modeling investigation of the three-phase zone reservoir situated in the sand layer of the Qiongdongnan Basin. By employing deterministic complex geological modeling techniques and integrating existing seismic and logging data, we have developed a three-phase coexistence zone model that precisely characterizes the interactions between geological structures and utilizes them as auxiliary constraints. This approach effectively mitigates the potential impact of complex geological conditions on model accuracy. Through a comprehensive analysis of 105 seismic profiles, we enhanced the model's accuracy, resulting in the creation of a three-phase coexistence zone model comprising 350,000 grids. A comparison between the modeling results and well data indicates a relatively small error margin, offering valuable insights for future development efforts. Furthermore, this method serves as a reference for modeling hydrates in marine environments characterized by three-phase coexistence on a global scale. [ABSTRACT FROM AUTHOR]