Search Results (54)

Overpressure prediction is critical for safe and efficient drilling, yet remains challenging in complex basins with multiple genetic mechanisms. This study systematically investigates the overpressure origins in the Xihu Sag, East China Sea, a prolific hydrocarbon-bearing sag with widespread overpressure and complex pressure regimes. By integrating well logging data and direct pore pressure measurements from nine wells across three major structural units, the Western Slope Belt, the Western Sub-sag and the Central Inversion Belt, a multi-method diagnostic framework is employed. This combines Bowers’ effective stress analysis with sonic-density cross-plots to discriminate between loading and unloading mechanisms. Results show obvious vertical zoning of pore pressure—normal-pressure zone, overpressure zone, and pressure reversal zone—with distinct horizontal heterogeneity. Results reveal a distinct spatial differentiation in dominant overpressure mechanisms. In the Western Slope Belt, overpressure in the deep Pinghu Formation primarily results from a composite of undercompaction (creating initial pressure seals) and subsequent hydrocarbon generation-induced fluid expansion. In contrast, in the Central Inversion Belt and Western Sub-sag, overpressure is predominantly driven by hydrocarbon charging along faults coupled with tectonic compression, with minimal undercompaction signatures. Previous studies on overpressure genesis in the Xihu Sag have largely focused on the Western Slope Belt. This study expands the analytical scope to the Western Sub-sag and Central Inversion Belt, and conducts a systematic comparative analysis of overpressure genesis across multiple tectonic units. The value of this work lies in the systematic application of classical diagnostic methods to fill the regional research gap regarding the overpressure characteristics of the Huagang Formation and the composite nature of overpressure. With accurately constrained genetic mechanisms, the findings can provide support for optimized drilling fluid design and wellbore stability management, and effectively mitigate deep hydrocarbon exploration risks in this sag and analogous overpressured basins. Full article

Geomechanical properties of rock are essential components in designing hydraulic fracturing procedures. Although ultrasonic measurement methods are usually utilized to obtain static geomechanical properties of rocks in the laboratory, they are limited to borehole locations. To obtain spatial distribution of these properties, a 3D prestack seismic inversion process is employed to derive them dynamically. However, 3D prestack seismic datasets are less readily available compared to 3D poststack seismic. We present a methodology that integrates 3D poststack seismic and wireline log data using a machine learning workflow to compute the dynamic geomechanical properties, namely Young’s modulus (E), Mu-Rho (MR), and brittleness (BRI) volumes, and generate crossplots to characterize the Salt Creek carbonate reservoir in the Midland Basin, Kent County, Texas. Our results show that: (1) Based on the comparison of seismically (dynamically) derived E and BRI maps with litho-facies maps, the zones with the highest porosity (oolites) are characterized by low E and low BRI. (2) Each of these properties is linearly related to the photoelectric factor (PEF) log, which can indicate porosity and calcite richness within a mixed carbonate–siliciclastic system. (3) In a mixed carbonate and siliciclastic system, geomechanical properties, especially E, can be used to identify rigid rock layers within the reservoir and deduce possible lithologies. Finally, when prestack seismic data are unavailable, our workflow offers a quick and inexpensive method to generate dynamic geomechanical property maps to characterize hydrocarbon reservoirs using poststack seismic data and well logs. Full article

Groundwater is a critical resource for domestic, agricultural, and industrial use in the Gilgel Gibe and Dhidhessa catchments of southwestern Ethiopia, where volcanic aquifer systems are the main sources. However, groundwater quality in these catchments has been under pressure from anthropogenic activities such as population growth, land-use changes, and pollution driven by rapid development and poor resource management. This study investigates hydrogeochemical processes and evaluates groundwater quality in volcanic aquifers using hydrochemical analyses and a stable isotope approach applied to 115 water samples. The spatial distribution of various physicochemical and hydrogeochemical parameters shows a distinct contrast between the highland and lowland regions, indicating topography-driven variations in water quality and geochemical processes. In hand-dug wells, springs, and surface waters, the ionic order is Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > NO₃⁻ > Cl⁻ > SO₄²⁻, whereas deep wells show Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻. The predominant groundwater type is Ca-HCO₃, followed by Na-HCO₃ and Ca-NO₃, with other types including Ca-Mg-HCO₃, Ca-Na-HCO₃, and Na-Ca-HCO₃. Water types of Ca-HCO₃ and Ca-Mg-HCO₃ dominate the upland areas, indicating relatively young groundwater with moderate total dissolved solids (TDSs) and enrichment in δ¹⁸O and δ²H, where highly mineralized Na-HCO₃ water types prevail in the deep aquifers of the lowland regions, where δ¹⁸O and δ²H are relatively depleted. Principal component analysis, cross-plots of major cations versus HCO₃⁻, and mineral stability diagrams indicate that aluminosilicate weathering and dissolution are the dominant processes controlling groundwater chemistry in the study area. The higher saturation index values observed in the deep wells indicate water closer to mineral equilibrium, suggesting more extended water–rock interaction relative to the shallow wells. The CO₂ partial pressures calculated using PHREEQC exceed atmospheric levels (~10^−3.5 atm), indicating sources from atmospheric influx, soil, or biogenic activity for most samples, and deeper sources such as mantle degassing may be found in a few deep wells. Scatter plots of Cl⁻ vs. SO₄²⁻ and Cl⁻ vs. NO₃⁻, associated with Ca(NO3)₂, NaNO₃, and CaCl₂ water types, suggest that anthropogenic inputs are the second major factor influencing the area’s water chemistry. Stable isotope analyses and hydrochemical data indicate that groundwater in the area primarily originates from local precipitation, with isotopic signatures reflecting strong groundwater–surface water interaction. These findings improve understanding of regional hydrogeochemistry and groundwater quality and help identify promising zones for sustainable groundwater development. This study provides valuable insights into groundwater resource management both in the study area and in regions sharing comparable geological contexts. Full article

Soil and groundwater contamination across multiple plots in industrial parks is an important environmental management challenge. Because plots often differ in contamination characteristics and remediation requirements, remediation decision-making needs to generate sustainability-oriented alternatives that account for explicit management preferences and trade-offs among carbon emissions, remediation duration, and cost. Although technology screening for individual contaminated plots has been widely investigated, how to use plot-level screening results to support the selection of multi-plot remediation alternatives under multiple objectives remains insufficiently addressed. This study developed a preference-guided multi-objective decision framework for selecting remediation alternatives in contaminated industrial parks. The framework first generates cross-plot remediation alternatives by assigning one feasible technology to each plot and removes alternatives that violate technological compatibility or project-level engineering constraints. It then identifies Pareto non-dominated alternatives using NSGA-II, retains preference-consistent alternatives through LO-based filtering, and selects the final recommendation using ideal-point distance comparison. The framework was applied to a five-plot contaminated industrial park in northern China. Compared with the LO and NSGA-II benchmarks, the hybrid NSGA-II–LO model selected less imbalanced alternatives that retained the preferred-objective advantage while improving the balance among the remaining objectives. The results show that management priorities can substantially affect remediation technology allocation across plots and that preference screening can support the adjustment of remediation alternatives when low-carbon targets, remediation schedules, or budget constraints change. This study provides an operational decision framework for translating plot-level technology screening into sustainability-oriented multi-plot remediation decision-making. Full article

Overpressure is widely developed in the Enping Formation of the Baiyun Sag, Pearl River Mouth Basin, yet its origin has long remained controversial. Previous studies, based on the acoustic velocity-effective stress crossplot, concluded that undercompaction is the dominant overpressure mechanism in this area; however, the significant influence of high total organic carbon (TOC) in the thick mudstone intervals on sonic transit time was not effectively eliminated. In this paper, geochemical logging data are used to perform TOC correction on the sonic transit time of Well BY-X1. Combined with log-curve assemblages and the effective-stress crossplot, the overpressure origin is re-identified, and the development characteristics and quantitative patterns of overpressure are clarified. The results show that the maximum TOC of mudstones in the overpressure intervals of the Enping Formation in the Baiyun Sag reaches 5.8%, exhibiting a strong positive correlation with sonic transit time. After TOC correction, the reduction in sonic transit time ranges from 6.8% to 29.8%, with an average reduction of 16.8% in the lower Enping Formation. Before correction, data points fall within the loading curve region, leading to a potential misinterpretation of undercompaction as the dominant mechanism; after correction, all data points plot along the unloading curve. Combined with the absence of significant shifts in density logs, the maximum formation pressure coefficient of 1.53, and the lack of anomalously high porosity, it is confirmed that the dominant origin of overpressure in this area is fluid expansion driven by hydrocarbon generation, rather than undercompaction. The sonic transit-time correction method for organic-rich mudstones established in this study can significantly improve the accuracy of formation-pressure prediction, providing a quantitative reference for the study of overpressure mechanisms in source-rock systems with high heat flow and hydrocarbon-rich sags. Full article

Disposing of carbon dioxide (CO₂) in the seafloor environment in its hydrate form provides an efficient means of CO₂ storage in virtually unlimited quantity. The process requires that the in situ condition be above the hydrate-forming pressure and below the hydrate-forming temperature and that the bulk CO₂ hydrates have densities greater than seawater density for gravitational stability. The objectives of this study are (1) to find an efficient method for generating stable CO₂ hydrates, (2) to identify the required equipment for efficient production of CO₂ hydrates, and (3) to identify the required water depth in various seawater environments for CO₂ injection. The first objective was achieved using a windowed reactor to observe the floating and settling behavior of generated CO₂ hydrates. CO₂ injection into the chilly water phase and water injection into the cold CO₂ phase were both investigated at various pressures and temperatures. CO₂ injection into the chilly water phase was found to generate bulk CO₂ hydrates of density less than that of water due to the excess CO₂ trapped in the bulk hydrates. Water injection into the cold CO₂ phase was found to generate bulk CO₂ hydrates of density greater than that of water due to the excess water trapped in the bulk hydrates. The second objective was achieved by designing a complete set of equipment to be installed on a ship with an open-bottom reactor assembly attached to the ship. The third objective was achieved by cross-plotting the hydrate-forming pressure curve versus the seawater hydrostatic pressure curve for seven seas and the Arctic Ocean. Results show that the minimum required seawater depth varies from 120 m in the Arctic Ocean to 650 m in the Mediterranean Sea environment. Full article

Accurate identification of flooded layers by cased-hole logging is a critical challenge for fine-scale development and enhanced oil recovery in water-flooded oil fields at medium to high water-cut stages. Conventional methods based on single-series logging or two-dimensional crossplot techniques are inadequate for the fine-scale interpretation of complex low-permeability reservoirs. This paper proposes a novel flooded layer identification method through the deep integration of dynamic and static data. The proposed approach organically couples static open-hole logging data (porosity, resistivity, etc.) with dynamic cased-hole logging data (pulsed neutron macroscopic capture cross-section Σ and carbon–oxygen ratio, C/O) within a three-dimensional (3D) crossplot framework. A multidimensional feature parameter space is constructed, and a spatial distance-ratio model is established to quantitatively calculate the flooding index Fw for continuous evaluation of flood level (non-flooded, weakly flooded, moderately flooded, and strongly flooded). Field application in Well X of a low-permeability oil field successfully identified two ambiguous apparent water layers as weakly flooded layers, previously indistinguishable using traditional 2D methods, with interpretation results highly consistent with subsequent production tests. The identification accuracy reached over 90.7%, providing a scalable technical framework for cased-hole flooded layer evaluation in medium-to-low-permeability complex reservoirs. Full article

Accurate prediction of solvent-bitumen viscosity is essential for the design and optimization of extended-solvent steam-assisted gravity drainage (ES-SAGD) processes, where viscosity reduction governs fluid mobility and recovery efficiency. Due to the highly nonlinear dependence of viscosity on temperature, pressure, solvent concentration, and fluid properties, conventional empirical and thermodynamic models often show limited generality across different operating conditions. In this study, data-driven machine learning techniques were employed to develop predictive models for solvent-bitumen viscosity using an extensive experimental database compiled from literature. The dataset was subjected to systematic preprocessing, including data cleaning, feature standardization, and 80/20 train-test splitting. Two optimized tree-based ensemble algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were trained using hyperparameter tuning. Model performance was evaluated using R², RMSE, and MAE metrics, along with cross-plots, residual analysis, and feature importance evaluation. Results demonstrate that both models successfully capture the strong nonlinear relationships governing viscosity behavior, with XGBoost providing the highest prediction accuracy and the best generalization capability. The R², RMSE, and MAE values for the test dataset are 0.932791, 811.882, and 111.091 for XGBoost, and those values for RF are 0.854601, 1194.149, and 150.703, respectively. Feature importance analysis confirms that temperature and solvent mole fraction are the dominant variables influencing viscosity reduction. The developed model offers a rapid, reliable, and data-driven alternative to experimental and thermodynamic approaches and can be integrated into ES-SAGD simulation and optimization workflows. Full article

The Donghe Sandstone in the Tarim Basin represents marine littoral deposits. Cyclical variations in hydrodynamic conditions during sedimentary evolution led to the widespread development of intercalations/interbeds within the reservoir, which directly impact hydrocarbon development. It is imperative to elucidate the genesis, types, and distribution of these intercalations, and to reveal their controlling effect on residual oil. Based on detailed core observations, the genesis and classification of interbeds in the study area were determined. A three-dimensional cross-plot method was employed to establish interbed identification criteria, and architectural element analysis was used to predict their spatial distribution. Results indicate that interbeds in the study area can be classified into muddy interbeds, calcareous interbeds, and calcareous-muddy interbeds. The heterogeneity of interlayer and intralayer interbeds and sand body connectivity were systematically characterized. This enabled the prediction of distribution patterns and styles of different interbeds within the coastal-plain reservoir, as well as the relationship between residual oil and interbeds. Production practice shows that residual oil is mainly distributed in high-position well areas. This solves the problem of declining reservoir production in the Hadson Oilfield caused by interbed distribution and provides a reference for predicting residual oil distribution in marine coastal sedimentary oilfields. Full article

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

Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article

Aiming at the challenges in accurately identifying complex pore-space types, significant scale variations, and overlapping log responses in carbonate reservoirs, this study takes the Jurassic Da’anzhai Member in the central Sichuan Basin as the research object. By integrating core observations, cast thin sections, scanning electron microscopy, and well log data, the genetic types and log response characteristics of pore spaces at different scales are systematically analyzed. Building on this, a multivariate distribution identification model for pore-space scales is established based on Bayesian discriminant theory. To enhance the model’s identification accuracy, Z-score normalization is introduced to eliminate dimensional differences. Nonlinear combined features, such as the ratio of the compensated acoustic log (AC) to the gamma ray log (GR) and the logarithmic difference between deep and shallow resistivity logs (RT and RI), are constructed to achieve a multidimensional coupling representation of reservoir physical properties; a class-balancing augmentation method based on Gaussian perturbation is adopted to mitigate decision bias caused by sample imbalance. The results show that the improved Bayesian model achieves F1 scores exceeding 0.80 for large-, small-, and micro-scale pore spaces, with an overall identification accuracy of 84.38%, significantly outperforming the conventional crossplot method’s accuracy of 59.38%. Validation through experiments and well log data demonstrates that the model’s identification results are consistent with core and thin-section observations, indicating that this method can effectively identify large-, small-, and micro-scale pore spaces in strongly heterogeneous carbonate reservoirs. This study provides a valuable approach for reservoir log interpretation and favorable reservoir prediction. Full article

The identification of lithology and fluids in reservoirs is the key to the quantitative characterization of gas reservoirs. However, the Ma5₄¹ Member of the Majiagou Formation in the Ordos Basin is characterized by strong reservoir heterogeneity, variable lithologic components and complex gas–water relationships. This leads to severe overlapping of conventional logging responses, posing significant challenges to detailed reservoir evaluation. Taking the Ma5₄¹ Member in the central Shaanbei Slope of the Ordos Basin as the research object, this study adopts the logging curve superposition and reconstruction method to quantitatively identify reservoir lithology and fluid properties, and establishes a set of identification standards for lithology-fluid logging curve superposition and reconstruction. The results show that the lithology identification plate constructed by introducing new parameters eliminates dimensional differences and effectively highlights the response characteristics of different lithologies. It can rapidly and effectively identify limestone, limy dolomite, dolomite, argillaceous dolomite, and mudstone with an identification accuracy exceeding 90% and an average accuracy of over 92%. In terms of fluid identification, the constructed ΔΦ₃–ΔΦ₄–ΔΦ₅ 3D plate successfully achieved the stereoscopic differentiation of gas layers, gas-bearing water layers, water layers, and dry layers. The gas layer identification accuracy reached 93.9%, which is significantly superior to the traditional 2D crossplot method. Applying this model to the plane prediction of lithology in the Ma5₄¹ Member of the study area, it was found that the lithology distribution features “pure in the east and mixed in the west.” The central-eastern and southeastern parts of the study area mainly develop high-quality dolomite and limy dolomite reservoirs, making them favorable areas for natural gas exploration. This study provides effective technical support for the quantitative identification of lithology and fluids in non-cored well sections and improves regional exploration and development efficiency. Full article

Low-resistivity reservoirs characterized by weak log contrasts are highly concealed and therefore difficult to detect using conventional oil–water discrimination methods. Recent exploration and development indicate that low-resistivity reservoirs are widely developed in the Triassic Chang 3 Member of the Zhenbei area, Ordos Basin. However, contrasting tectonic evolution associated with the Tianhuan Depression and complex lithologic assemblages in the western and eastern sectors have resulted in complicated hydrocarbon migration and accumulation processes. In this study, integrated well-log and geochemical data were used to systematically investigate the genesis of low-resistivity reservoirs in the Chang 3 Member and to establish oil–water discrimination charts. Three-dimensional seismic flattening was applied to restore the Late Jurassic paleostructure of the western Chang 3 Member and to analyze its tectonic evolution. Reservoir petrology and pore–throat architecture in the western and eastern areas were comparatively examined using thin-section petrography, field-emission scanning electron microscopy (FESEM), and high-pressure mercury intrusion. Results indicate that the development of low-resistivity reservoirs in the Chang 3 Member is primarily controlled by highly saline formation water and elevated bound-water saturation. Based on these controls, the invasion factor–acoustic transit time cross-plot and the apparent spontaneous potential difference (ΔSP) method effectively discriminate oil- and water-bearing intervals in a total of 25 wells within the study area. Paleostructural restoration reveals that the western Chang 3 Member has undergone a tectonic inversion from a west-high–east-low configuration since the Late Jurassic to the present-day east-high–west-low geometry. Oil–source correlation indicates that hydrocarbons in the Chang 3 reservoirs were mainly derived from the underlying Chang 7 source rocks, whereas the bimodal distribution of fluid-inclusion homogenization temperatures suggests that the reservoirs experienced two distinct charging episodes. Integrated analysis suggests that tectonic inversion during the Yanshanian movement, combined with multistage hydrocarbon charging, led to secondary migration and partial destruction of early-formed reservoirs in the western area, resulting in predominantly scattered accumulations. In contrast, the eastern area experienced relatively limited tectonic modification, and laterally extensive accumulations are controlled by Type I–III lithologic–structural traps formed by the Chang 3 reservoir interval and its overlying strata. These findings provide an important geological basis for the identification of low-contrast reservoirs and for the exploration and development of hydrocarbon accumulations that are jointly controlled by tectonic evolution and lithologic heterogeneity. Full article

The interlayer is a key geological factor that regulates reservoir heterogeneity and remaining oil distribution, and its accurate identification directly affects the reservoir development effect. To address the strong subjectivity of traditional identification methods and the insufficient recognition accuracy of single machine learning models under imbalanced sample distributions, this study focuses on three types of interlayers (argillaceous, calcareous, and petrophysical interlayers) in the W Oilfield, and proposes an accurate identification method integrating the Synthetic Minority Over-Sampling Technique (SMOTE) and heterogeneous ensemble learning. Firstly, the corresponding data set of interlayer type and logging response is established. After eliminating the influence of dimension using normalization, the sensitive logging curves are optimized using the crossplot method, mutual information, and effect analysis. SMOTE technology is used to balance the sample distribution and solve the problem of the identification deviation of minority interlayers. Then, a heterogeneous ensemble model composed of the k-nearest neighbor algorithm (KNN), decision tree (DT), and support vector machine (SVM) is constructed, and the final recognition result is output using a voting strategy. The experiments show that SMOTE technology improves the average accuracy of a single model by 3.9% and effectively improves the model bias caused by sample imbalance. The heterogeneous integration model improves the overall recognition accuracy to 92.6%, significantly enhances the ability to distinguish argillaceous and petrophysical interlayers, and optimizes the F1-Score simultaneously. This method features a high accuracy and reliable performance, providing robust support for interlayer identification in reservoir geological modeling and remaining oil potential tapping, and demonstrating prominent practical application value. Full article