Search Results (14)

Exploration of the Gaolihan (GLH) Sag in the Erlian Basin remains low due to the discontinuity of early-stage 2D seismic surveys that have failed to provide regional coverage, leaving the deep geological framework unclear and hindering hydrocarbon breakthroughs. To address this problem and to evaluate the resource potential of the pre-Cretaceous formation, this study applied an integrated geophysical workflow combining 1:50,000 high-precision gravity–magnetic surveys, gravity–magnetic well–seismic joining in the forward and inversion directions, layer stripping, and time–frequency electromagnetic method (TFEM) profiling. Using logging resistivity curves from wells GC1 and LT1 for electrical calibration, we delineated the fault system, stratigraphic framework, and deep lithology of the GLH Sag. The key results include the following: (1) identification of 12 faults defining a “three sags and three uplifts” structural pattern; (2) burial depths of the Cretaceous, Jurassic, and Carboniferous–Permian basements ranging from 3000 to 3300 m, 4400 to 4600 m, and 6500 to 6700 m, respectively; and (3) recognition of three electrical layers within the Jurassic, with high-resistivity intervals indicating basalt and low-resistivity intervals interpreted as coal-bearing formation of the Lower Jurassic Hongqi formation. Quantitative validation against wells GC1 and LT1 demonstrates that the TFEM-inverted resistivity values show an average absolute deviation of ~2.5 Ω·m from measured logs (relative error <12% for resistivities >5 Ω·m), with an overall stratigraphic boundary consistency exceeding 85%. Lithologic prediction accuracies reach 88% for sandstone/siltstone and 82% for mudstone/carbonaceous mudstone. This integrated workflow proves effective for deep structural imaging and lithology prediction in volcanic-covered, seismically poor areas, and provides a practical reference for similar low-exploration basins. Full article

The Fujin Basin (FJ Basin) is a Cenozoic sedimentary basin located in the northern Junggar Basin. In response to key scientific issues, including unclear stratigraphic distribution and an inadequately constrained structural framework, this study utilizes measured resistivity data acquired through the controlled-source sounding method. By integrating profile interpretation and numerical simulation, the geological structure and stratigraphic distribution of the basin are investigated, and the exploration potential for deep oil and gas resources as well as shallow sandstone-hosted uranium deposits is evaluated. Using known logging resistivity curves, electromagnetic frequency responses at different burial depths were simulated to verify the effectiveness of the acquisition parameters and survey design for detecting strata ranging from shallow Tertiary formations to deep Carboniferous sequences. Based on the processed data, combined with the known physical properties of the strata and comprehensive geological interpretation, the geological structure, stratigraphic distribution, and tectonic framework of the FJ Basin were clarified. Integrated with borehole geochemical data, the CSEM resistivity sections suggest that the Upper Carboniferous Haerjiawu Formation constitutes a potential hydrocarbon source rock within the JTL sag, warranting further drilling verification. In addition, resistivity attributes and dual-frequency phase attributes were employed to predict the metallogenic conditions and favorable targets for shallow Tertiary sandstone-hosted uranium mineralization. The prediction results show strong consistency with known uranium deposits, providing important guidance for uranium exploration. The methodology proposed in this study can serve as a technical reference for integrated multi-mineral exploration in similar regions. Full article

Accurate characterization of subsurface stratigraphy is essential for reliable landslide stability assessment. However, stratigraphic models constructed solely from sparse borehole data are often constrained by incomplete spatial coverage and substantial interpretive uncertainty. To address this issue, this study developed an integrated probabilistic stratigraphic modeling framework that combines borehole data with electrical resistivity tomography (ERT) data. In the proposed framework, borehole logs provide direct lithological labels and spatial prior information, while the inverted ERT resistivity profile is introduced as a continuous geophysical constraint. Specifically, logarithmic resistivity and the borehole-derived expected stratigraphic configuration were combined into a support vector machine classifier to establish a nonlinear mapping between geophysical responses and stratigraphic categories. A bootstrapping strategy was also used to quantify the stratigraphic uncertainty. The proposed method was then applied to the Panzhuangzu Landslide in Henan Province, China. Based on the probabilistic stratigraphic models, multiple plausible stratigraphic realizations were generated, and their corresponding stability responses are evaluated through numerical analysis. Monte Carlo simulations were further performed to examine how stratigraphic uncertainty propagates into landslide stability predictions. The results show that incorporating ERT data improves the geological plausibility of the inferred stratigraphy. Compared with the borehole-only case, the results obtained from the integrated framework exhibited reduced uncertainty in both the inferred stratigraphic model and the corresponding landslide stability assessment. These findings indicate that the proposed borehole–geophysical data fusion method can provide a more reliable geological basis for landslide stability analysis. Full article

The excavation of deep shafts using Vertical Shaft Sinking Machine (VSM) technology in stratified soft soils involves complex soil-structure interaction (SSI) mechanisms that are often oversimplified by conventional numerical approaches. This study develops a robust three-dimensional numerical framework to investigate ground deformation induced by VSM operations, explicitly incorporating the phased construction sequence, segmental lining installation, and site-specific stratigraphy. The model is calibrated and validated against high-resolution field monitoring data, employing a prediction envelope approach and statistical performance metrics (RMSE and $R^2$). The results suggest that ground response during VSM excavation is predominantly stiffness-controlled under the investigated conditions. Mobilized shear stresses remain significantly below the available soil capacity, indicating that deformation under serviceability conditions is driven by progressive strain accumulation. Horizontal displacement profiles suggest a relatively stable depth of influence, indicating that the excavation process amplifies deformations within a pre-established domain without significant deep-seated propagation. Sensitivity analyses indicate soil stiffness modules ($E_{50},E_{oed},E_{ur})$ and the SSI interface factor ($R_{inter}$) as the primary drivers of deformation magnitude. Furthermore, stratigraphic contrasts specifically clay-sand sequences, act as a mechanical filter, concentrating strains in soft layers while limiting vertical propagation through stiffer strata. The proposed framework provides a mechanically coherent basis for serviceability-oriented design, deformation prediction, and risk-mitigation strategies for mechanized shafts in saturated soft ground. Full article

The Huazhuang block, located on the northern slope of the Gaoyou Depression in the Subei Basin of the Jiangsu Oilfield, exhibits complex stratigraphic geomechanical characteristics. During drilling, wellbore instability-related issues, such as obstruction, sticking, pump pressure buildup, bit pressure buildup, and overflow due to abnormally high pressure, prolong the drilling cycle and significantly hinder the safe and efficient development of shale oil. In order to determine the appropriate drilling fluid density and ensure safe and efficient drilling in this block, a comprehensive wellbore profile, incorporating rock mechanical parameters, in-situ stress, and predictions of pore pressure, collapse pressure, lost circulation pressure, and fracture pressure, was established based on laboratory tests and well logging data. This study reveals the mechanisms of wellbore collapse and fluid loss in the Huazhuang block. The results indicate that the second and fourth members of the Funing Formation in the Huazhuang block have a relatively weak and unconsolidated structure with a high content of water-sensitive minerals, leading to significant hydration risks when using water-based drilling fluids. As depth increases, compressive strength, elastic modulus, and cohesion show an increasing trend, while the internal friction angle and Poisson’s ratio gradually decrease. Additionally, in-situ stress increases significantly, meeting the condition of ${\sigma }_{\mathrm{V}}$ > ${\sigma }_{\mathrm{H}}$ > ${\sigma }_h$. Above 3300 m, the equivalent density of formation pore pressure is below 1.20 g/cm³, Whereas below 3300 m, there is significant overpressure, with a maximum equivalent pore pressure density reaching 1.45 g/cm³. The deeper the formation, the narrower the safe density window, making wellbore collapse more likely. To prevent wellbore instability, both the sealing capability and density of the drilling fluid should be considered. Enhancing the sealing performance of the drilling fluid and selecting an appropriate drilling fluid density can help improve wellbore stability. The established rock mechanical parameters and four-pressure prediction profile for the Huazhuang block provide a scientific basis for optimizing wellbore structure design and selecting key engineering parameters. Full article

As oil resources continue to be depleted, traditional extraction technologies face significant challenges, and improving remaining oil recovery has become a critical issue in the development of low-permeability oil fields. The study first establishes a stratigraphic framework through a fine stratigraphic correlation of key wells, followed by a comprehensive characterization of the internal arrangement of sand bodies, with a detailed analysis of the distribution of interlayers and the complexity of sand body stacking patterns. Based on field data, including tracer monitoring and water absorption profiles, the distribution of remaining oil is predicted. The results reveal that sandy debris flow deposition plays a key role in the enrichment of remaining oil, particularly considering how its internal structure impacts reservoir connectivity and waterflood efficiency. The sedimentary pattern in the study area is dominated by debris flow deposition, with poor continuity of sand bodies; over 90% of individual sand bodies encountered during horizontal well drilling have a width of less than 60 m. Sand body stacking types are classified into overlapping, splicing, and isolated types, and different stacking types significantly influence reservoir waterflood efficiency and the distribution of remaining oil. The innovation of this study lies not only in the microstructural investigation of sandy debris flow but also in the comprehensive characterization of its strong heterogeneity. By analyzing the internal configuration of sand bodies, sedimentary microfacies, and physical property distributions, this study uncovers how these factors influence the distribution of remaining oil. This approach provides valuable insights for accurately predicting and enhancing remaining oil potential. Full article

Identifying locations of landslide slip surfaces provides critical information for understanding the volume of landslides and the scale of disasters, both of which are essential for formulating disaster preparedness and mitigation strategies. Based on hydrogeological survey data from 24 deep-seated landslide-prone sites in Taiwan’s mountainous regions, this study developed the hydraulic conductivity potential index (HCPI) using principal component analysis to quantify the hydraulic properties of disturbed rock formations with six geological factors. Then, regression analysis was performed to construct a permeability estimation model for the geological environment of landslides. Finally, the established model was utilized to develop an identification method for potential slip depths in landslide-prone sites. Results indicated a strong relation between HCPI and hydraulic conductivity with a determination coefficient of 0.895. The relation equation confirmed that the data it generated concerning the depths of significant changes in hydraulic conductivity could be used to identify potential slip surfaces. Additionally, this study successfully established a rule for identifying potential slip zones by summarizing data concerning the generated hydraulic conductivity profiles, stratigraphic lithology, existing inclinometer slip depth records, and groundwater level of landslide sites. This identification method was then applied to predict potential slip depths for ten landslide sites where slip surfaces have not yet occurred. These findings offer a new alternative to having early information on potential sliding depths for timely disaster management and control implementation. Full article

This study employs Cone Penetration Tests (CPTu) and Standard Penetration Tests (SPT) to analyze the geotechnical properties of the Magdalena River’s riverbed and banks. While these methods are standard in soil characterization, this research innovatively combines CPTu’s continuous profiling with SPT’s localized sampling to develop a nuanced stratigraphic model of the subsurface. This integrated approach provides a comprehensive view of the soil conditions, which is crucial for understanding sediment variability and stability along the riverbanks. The findings from this methodological integration enhance our ability to predict soil behavior under dynamic riverine conditions, offering valuable insights for erosion control and sustainable river management. The study underscores the practical benefits of synergizing traditional testing methods to address geotechnical challenges in river environments. Full article

Anomalous bright spots, called the string of bead-like response, are typical seismic migration profile features in carbonate fractured reservoirs in the Tarim Basin, and they are indicators of high-quality oil and gas reservoirs. Correctly recognizing the correspondence between fractures and the SBLR can contribute to the efficient drilling of target carbonate fractured reservoirs. Physical models can describe fractured reservoirs more directly and efficiently than real situations and have obvious advantages in accurately and quantitatively designing parameters such as dipping angles and the number of layers of fractured reservoirs. Under such a background, according to the real tectonic characteristics of the Tarim Basin, among the main hydrocarbon reservoirs, fractured reservoirs with various parameters were designed and a physical model was constructed according to the real stratigraphic parameters. After seismic data acquisition and processing, the response characteristics of the string of bead-like response were extracted and summarized from seismic migration profiles for all fractured reservoirs, which provided targeted analyses and discussions on the fracture dipping angle, number of fracture layers, overlying stratigraphic influences, and planar attributes of the fractured reservoirs. In general, the larger the fracture dip, the more difficult it is to identify, while the slope of reflection strength and maximum absolute amplitude attributes can be important markers for fractured reservoir identification. The physical modeling study of fractured reservoirs in this paper can provide a basis for the analysis and prediction of carbonate fractured reservoirs in the Tarim Basin. Full article

Globally, deep-water reservoir systems are comprised of a variety of traps. Lateral and downdip trapping features include sand pinch-outs, truncation against salt or shale diapirs, and monoclinal dip or faulting with any combination of trapping designs; the potential for massive hydrocarbon accumulations exists, representing significant exploration prospects across the planet. However, deep-water turbidites and submarine fans are two different types of traps, which are developed along the upslope and the basin floor fans. Among these two traps, the basin floor fans are the most prolific traps as they are not influenced by sea-level rise, which distorts the seismic signals, and hence provides ambiguous seismic signatures to predict them as hydrocarbon-bearing zones for future explorations. Therefore, the deep-water channel-levee sand systems and basin floor fans sandstone define economically viable stratigraphic plays. The subsurface variability is significant, and hence, characterizing the thick (porous) channelized-basin floor fans reservoir is a challenge for the exploitation of hydrocarbons. This study aims to develop seismic-based attributes and wedge modeling tools to accurately resolve and characterize the porous and gas-bearing reservoirs using high-resolution seismic-based profiles, in SW Pakistan. The reflection strength slices better delineate the geomorphology of sand-filled channelized-basin floor fans as compared to the instant frequency magnitudes. This stratigraphic prospect has an area of 1180 km². The sweetness magnitudes predict the thickness of channelized-basin floor fans as 33 m, faults, and porous lithofacies that complete a vital petroleum system. The wedge modeling also acts as a direct hydrocarbon indicator (DHI) and, hence, should be incorporated into conventional stratigraphic exploration schemes for de-risking stratigraphic prospects. The wedge model resolves a 26-m thick hydrocarbon-bearing channelized-basin floor fans lens with a lateral distribution of ~64 km. Therefore, this wedge model provides ~75% correlation of the thickness of the LSL as measured by sweetness magnitudes. The thickness of shale that serves as the top seal is 930 m, the lateral mud-filled canyons are 1190 m, and the thick bottom seal is ~10 m, which provides evidence for the presence of a vibrant petroleum play. Hence, their reveals bright opportunities to exploit the economically vibrant stratigraphic scheme inside the OIB and other similar global depositional systems. Full article

A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV–LR, IV–SVM, IV–RF, FR–LR, FR–SVM, and FR–RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR–RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR–RF coupling model had the highest accuracy. Under the optimal model FR–RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall. Full article

Sandbody distribution patterns and controls are the most important foundation for petroleum exploration and development, particularly in a lacustrine basin with rapid changes in the sedimentary environment. To provide sedimentologists and petroleum geologists around the world with an analogue for sandstone reservoir prediction, the sedimentary facies of the fourth member of the Shahejie Formation (Sha-4 Member) in the Huimin Depression of the Bohai Bay Basin were analyzed, and the sequence stratigraphic framework was established based on characteristics of spontaneous potential logs and lithology. According to the findings of this study, the Sha-4 Member’s sedimentary environment was dominated by delta front and shallow lake facies. Delta front sandbodies were discovered to retrograde before prograding again throughout the established profile. The Sha-4 Member in the Huimin Depression is divided into a third-order sequence (LSC1), which can be further divided into four fourth-order sequences (MSC1 to MSC4), corresponding to the four sub-members (S4-1 to S4-4). During the development of the MSC1 to MSC4 sequences, the delta depositional scale first decreased and then increased with the changing base level. The maximum flooding surface developed within the MSC3 sequence. The Sha-4 Member sequence model reveals that the deltas in the study area exhibit self-similarity, and delta sandbodies are primarily developed in the MSC1, MSC2, and MSC4, whereas mudstone is largely developed in the MSC3. Full article

In exploration geophysics, seismic impedance is a physical characteristic parameter of underground formations. It can mark rock characteristics and help stratigraphic analysis. Hence, seismic data inversion for impedance is a key technology in oil and gas reservoir prediction. To invert impedance from seismic data, one can perform reflectivity series inversion first. Then, under a simple exponential integration transformation, the inverted reflectivity series can give the final inverted impedance. The quality of the inverted reflectivity series directly affects the quality of impedance. Sparse-spike inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on the inverted reflectivity series. However, the high resolution of sparse-spike-like reflectivity series is obtained at the cost of sacrificing small reflectivity. This is the inherent problem of sparse regularization. In fact, the reflectivity series from the actual impedance well log is not strictly sparse. It contains not only the sparse major large reflectivity, but also small reflectivity between major reflectivity. That is to say, the large reflectivity is sparse, but the small reflectivity is dense. To combat this issue, we adopt elastic-net regularization to replace sparse regularization in seismic impedance inversion. The elastic net is a hybrid regularization that combines sparse regularization and dense regularization. The proposed inversion method was performed on a synthetic seismic trace, which is created from an actual well log. Then, a real seismic data profile was used to test the practice application. The inversion results showed that it provides an effective new alternative method to invert impedance. Full article

Due to the discreteness, sparsity, multidimensionality, and incompleteness of geotechnical investigation data, traditional methods cannot reasonably predict complex stratigraphic profiles, thus hindering the three-dimensional (3D) reconstruction of geological formation that is vital to the visualization and digitization of geotechnical engineering. The machine learning method of relevant vector machine (RVM) is employed in this work to predict the 3D stratigraphic profile based on limited geotechnical borehole data. The hyper-parameters of kernel functions are determined by maximizing the marginal likelihood using the particle swarm optimization algorithm. Three kinds of kernel functions are employed to investigate the prediction performance of the proposed method in both 2D analysis and 3D analysis. The 2D analysis shows that the Gauss kernel function is more suitable to deal with nonlinear problems but is more sensitive to the number of training data and it is better to use spline kernel functions for RVM model trainings when there are few geotechnical investigation data. In the 3D analysis, it is found that the prediction result of the spline kernel function is the best and the relevant vector machine model with a spline kernel function performs better in the area with a fast change in geological formation. In general, the RVM model can be used to achieve the purpose of 3D stratigraphic reconstruction. Full article