Search Results (125)

Assessment of contaminant dispersal in sandstones requires hydraulic characterization with a combination of datasets that span from the core plugs to wellbores and up to the field scale as the matrix and fractures are both hydraulically conductive. Characterizing the hydraulic properties of the matrix is fundamental because contaminants diffuse into the fractured porous blocks. Fractures are highly conductive, and the determination of the number of hydraulically active rock discontinuities makes discrete fracture network models of solute transport reliable. Recent advances (e.g., active line source temperature logs) in hydro-geophysics have allowed the detection of 40% of hydraulically active fractures in a lithified sandstone. Tracer testing has revealed high (~10⁻⁴–10⁻² ms⁻¹) flow velocities and low (~10⁻²–10⁻⁴) effective porosities. Contaminants can therefore move rapidly in the subsurface. The petrophysical characterization of the plugs extracted from the cores, in combination with borehole hydro-geophysics, allows the characterization of either matrix or fracture porosity, but the volume of sandstone characterized is low. Tracer tests cannot quantify matrix or fracture porosity, but the observation scale is larger and covers the minimum representative volume. Hence, the combination of petrophysics, borehole hydro-geophysics, and tracer testing is encouraged for the sustainable management of solute transport in dual porosity sandstones. Full article

The deep carbonate reservoirs in the Tarim Basin, Xinjiang, China, are widely developed with multi-scale complex reservoir spaces such as fractures, pores, and karst caves under the coupling of abnormal high pressure, diagenesis, karst, and tectonics and have strong heterogeneity. Among them, fracture–cavity carbonate reservoirs are one of the main reservoir types. Revealing the petrophysical characteristics of fracture–cavity carbonate reservoirs can provide a theoretical basis for the log interpretation and geophysical prediction of deep reservoirs, which holds significant implications for deep hydrocarbon exploration and production. In this study, based on the mineral composition and complex pore structure of carbonate rocks in the Tarim Basin, we comprehensively applied classical petrophysical models, including Voigt–Reuss–Hill, DEM (Differential Effective Medium), Hudson, Wood, and Gassmann, to establish a fracture–cavity petrophysical model tailored to the target block. This model effectively characterizes the complex pore structure of deep carbonate rocks and addresses the applicability limitations of conventional models in heterogeneous reservoirs. The discrepancies between the model-predicted elastic moduli, longitudinal and shear wave velocities (Vp and Vs), and laboratory measurements are within 4%, validating the model’s reliability. Petrophysical template analysis demonstrates that P-wave impedance (Ip) and the Vp/Vs ratio increase with water saturation but decrease with fracture density. A higher fracture density amplifies the fluid effect on the elastic properties of reservoir samples. The Vp/Vs ratio is more sensitive to pore fluids than to fractures, whereas Ip is more sensitive to fracture density. Regions with higher fracture and pore development exhibit greater hydrocarbon storage potential. Therefore, this petrophysical model and its quantitative templates can provide theoretical and technical support for predicting geological sweet spots in deep carbonate reservoirs. Full article

Traditional coal quality assessment methods rely exclusively on the laboratory testing of physical samples, which impedes detailed stratigraphic evaluation and limits the integration of intelligent precision mining technologies. To resolve this challenge, this study investigates geophysical logging as an innovative method for coal quality prediction. By integrating scanning electron microscopy (SEM), X-ray analysis, and optical microscopy with interdisciplinary methodologies spanning mathematics, mineralogy, and applied geophysics, this research analyzes the coal quality and mineral composition of the Shanxi Formation coal seams in northern Jiangsu, China. A predictive model linking geophysical logging responses to coal quality parameters was established to delineate relationships between subsurface geophysical data and material properties. The results demonstrate that the Shanxi Formation coals are gas coal (a medium-metamorphic bituminous subclass) characterized by low sulfur content, low ash yield, low fixed carbon, high volatile matter, and high calorific value. Mineralogical analysis identifies calcite, pyrite, and clay minerals as the dominant constituents. Pyrite occurs in diverse microscopic forms, including euhedral and semi-euhedral fine grains, fissure-filling aggregates, irregular blocky structures, framboidal clusters, and disseminated particles. Systematic relationships were observed between logging parameters and coal quality: moisture, ash content, and volatile matter exhibit an initial decrease, followed by an increase with rising apparent resistivity (LLD) and bulk density (DEN). Conversely, fixed carbon and calorific value display an inverse trend, peaking at intermediate LLD/DEN values before declining. Total sulfur increases with density up to a threshold before decreasing, while showing a concave upward relationship with resistivity. Negative correlations exist between moisture, fixed carbon, calorific value lateral resistivity (LLS), natural gamma (GR), short-spaced gamma-gamma (SSGG), and acoustic transit time (AC). In contrast, ash yield, volatile matter, and total sulfur correlate positively with these logging parameters. These trends are governed by coalification processes, lithotype composition, reservoir physical properties, and the types and mass fractions of minerals. Validation through independent two-sample t-tests confirms the feasibility of the neural network model for predicting coal quality parameters from geophysical logging data. The predictive model provides technical and theoretical support for advancing intelligent coal mining practices and optimizing efficiency in coal chemical industries, enabling real-time subsurface characterization to facilitate precision resource extraction. Full article

In this study, we propose a self-adaptive weighted multi-mineral inversion model (SQP_AW) based on Sequential Quadratic Programming (SQP) and the Adam optimization algorithm for the accurate evaluation of mineral content in carbonate reservoir rocks, addressing the high costs of traditional experimental methods and the strong parameter dependence in geophysical inversion. The model integrates porosity curves (compensated density, compensated neutron, and acoustic time difference), elastic modulus parameters (shear and bulk moduli), and nuclear magnetic porosity data for the construction of a multi-dimensional linear equation system, with calibration coefficients derived from core X-ray diffraction (XRD) data. The Adam algorithm dynamically optimizes the weights, solving the overdetermined equation system. We applied the method to the Asmari Formation in the M oilfield in the Middle East with 40 core samples for calibration, achieving a 0.91 fit with the XRD data. For eight additional uncalibrated samples from Well A, the fit reaches 0.87. With the introduction of the elastic modulus and nuclear magnetic porosity, the average relative error in mineral content decreases from 9.45% to 6.59%, and that in porosity estimation decreases from 8.1% to 7.1%. The approach is also scalable to elemental logging data, yielding inversion precision comparable to that of commercial software. Although the method requires a complete set of logging data and further validation of regional applicability for weight parameters, in future research, transfer learning and missing curve prediction could be incorporated to enhance its practical utility. Full article

In-situ chemical oxidation (ISCO) is a common method to remediate chlorinated ethene contaminants in groundwater. Monitoring the effectiveness of ISCO can be hindered because of insufficient observations to assess oxidant delivery. Advantageously, potassium permanganate, one type of oxidant, provides the opportunity to use its strong electrical signal as a surrogate to track oxidant delivery using time-series borehole geophysical methods, like electromagnetic (EM) induction logging. Here we report a passive ISCO (P-ISCO) experiment, using potassium permanganate cylinders emplaced in boreholes, at a chlorinated ethene contamination site, Naval Air Station Pensacola, Florida. The contaminants are found primarily at the base of a shallow sandy aquifer in contact with an underlying silty-clay confining bed. We used results of the time-series borehole logging collected between 2017 and 2022 in 4 monitoring wells to track oxidant delivery. The EM-induction logs from the monitoring wells showed an increase in EM response primarily along the contact, likely from pooling of the oxidant, during P-ISCO treatment in 2021. Interestingly, concurrent natural gamma-ray (NGR) logging showed a decrease in NGR response at 3 of the 4 wells possibly from the formation of manganese precipitates coating sediments. The coupling of time-series logging and well-chemistry data allowed for an improved assessment of passive ISCO treatment effectiveness. Full article

This study focuses on determining the high-frequency decay parameter kappa (k) and its site component (k₀) for sixteen accelerometric stations installed in suitable locations in North Macedonia. Kappa characterizes the attenuation of ground motion at high frequencies, describing the decrease in the acceleration amplitude spectrum. It is defined using a regression line in log-linear space, starting from the point where the S-wave amplitude spectrum begins to decay rapidly. The site characteristics of the stations are determined through geophysical and borehole investigations, as well as HVSR mean curves derived from earthquake data. The strong-motion data used in this analysis originate from earthquake events with a moment magnitude greater than 3 (M_W > 3), an epicentral distance less than 120 km (R_epi < 120 km), and a focal depth lower than 30 km (h < 30 km). The records undergo visual inspection and filtering, with those having a signal-to-noise ratio (SNR) below 3 excluded from further analysis. The study examines the correlation between kappa values and various parameters, including magnitude, epicentral distance, average shear-wave velocity in the top 30 m depth (V_S30), and fundamental site frequency (f₀). The importance of this study is the application in the future evaluation/update of seismic hazard analysis of the region. Full article

Recently, shallow gas fields and hydrate-bearing sand in the deepwater area of the northern South China Sea have been successively discovered, and the accurate prediction of shallow sands is an important foundation. However, most of the current prediction methods are mainly for deep oil and gas reservoirs. Compared with those reservoirs with high degree of consolidation, shallow sandy reservoirs are loose and unconsolidated, whose geophysical characteristics are not well understood. This paper analyzes the logging data of shallow sandy reservoirs recovered in the South China Sea recently, which show that the sand content has a significant influence on Young’s modulus and Poisson’s ratio of the sediments. Therefore, this paper firstly constructs a new petrophysical model of unconsolidated strata targeting sandy content and qualitatively links the mineral composition and the elastic parameters of the shallow marine sediments and defines a new indicator for sandy content: the modified brittleness index (MBI). The effectiveness of MBI in predicting sandy content is then verified by measured well data. Based on pre-stack seismic inversion, the MBI is then inverted, which will identify the sandy deposits. The method proposed provides technical support for the subsequent shallow gas and hydrate exploration in the South China Sea. Full article

The work aims to give a preliminary investigation of the geothermal potentiality of the hot dry granitic rocks in the Hail area, Northern KSA. The Hail area is characterized by a massive exposed belt of radioactive granitic rocks in the southern part, while the northern part is covered by a sedimentary section. A comprehensive methodology utilizing different categories of mineralogical petrographic, geochemical, geophysical well logging and, radiometry datasets, was used to assess the radiogenic heat production capacity of this granite. The measured data are integrated and interpreted to quantify the potential geothermal capacity of the granite and estimate its possible power production. The radioactivity and radiogenic heat production (RHP) of the Hail granites are among the highest recorded values in Saudi Arabia. Land measurements indicate uranium, thorium, potassium, and RHP values of 17.80 ppm, 90.0 ppm, 5.20%, and 11.93 µW/m³, respectively. The results indicated the presence of a reasonable subsurface geothermal reservoir condition with heat flow up to 99.87 mW/M² and a reservoir temperature of 200 °C (5 km depth). Scenarios for energy production through injecting water and high-pressure CO₂ in the naturally/induced fractured rock are demonstrated. Reserve estimate revealed that at a 2% heat recovery level, the Hail granites could generate about 3.15 × 10¹⁶ MWe, contributing to an average figure of 3.43 × 10¹² kWh/y, for annual energy per capita Saudi share. The results of this study emphasized the potential contribution of the Hail granite in the future of the energy mix of KSA, as a new renewable and sustainable resource. It is recommended to enhance the surface geophysical survey in conjunction with a detailed thermo-mechanical laboratory investigation to delineate the subsurface orientation and geometry of the granite and understand its behavior under different temperature and pressure conditions. Full article

The study aims to improve the efficiency of oil field development at the Kalamkas field through the implementation of new methods for analyzing hydrodynamic survey data and monitoring well conditions. It is hypothesized that the use of integrated geophysical and hydrodynamic methods will enhance forecasting accuracy, optimize field operations, and increase the hydrocarbon recovery factor. An integrated approach combining pulsed neutron logging (PNL), acoustic cementometry (AC), inflow and injectivity profile evaluation methods, and specialized software for advanced data interpretation was applied, significantly improving the accuracy of well condition analysis. The analysis enabled the identification of oil and gas saturation intervals, zones of increased water cut, and cementing defects in casing, and allowed for a quantitative assessment of reservoir permeability dynamics. Hydraulic fracturing application resulted in a 10–15% increase in permeability in certain zones, with an average oil recovery factor increase of 5%. Analysis of PNL data demonstrated the transition of oil-saturated reservoirs to water saturation during development, confirmed by geophysical and pressure build-up survey results. The study identified the primary causes of increased water cut and key factors leading to production rate decline. Proposed measures for optimizing operating modes and well grid efficiency contribute to improving existing field management practices. Full article

A multidisciplinary approach was employed to construct a three-dimensional model of the bedrock top surface within the Palermo Plain, Sicily, Italy. This urban area is characterized by a dense and extensive built environment that largely obscures its geological features, thereby emphasizing the value of geophysical methods for enhancing subsurface understanding. In this sector, Numidian Flysch deposits constitute the geological bedrock of the plain. The morphology of the top surface of this unit was reconstructed by integrating borehole stratigraphic data with both passive and active seismic surveys. Ambient noise recordings were analyzed using the Horizontal-to-Vertical Spectral Ratio (HVSR) method to obtain spectral curves. These were then inverted into seismostratigraphic models using shear wave velocity profiles derived by Multichannel Analysis of Surface Waves (MASW) and lithostratigraphic information from borehole logs. Finally, the depth of the top of the Numidian Flysch, determined from both the borehole data and the inverted seismic models, was interpolated to generate a comprehensive 3D model of the bedrock top surface. Full article

Accurate Total Organic Carbon (TOC) prediction in the deeply buried Lower Cambrian Qiongzhusi Formation shale is constrained by extreme heterogeneity (TOC variability: 0.5–12 wt.%, mineral composition Coefficient of Variation > 40%) and ambiguous geophysical responses. This study introduces three key innovations to address these challenges: (1) A Dynamic Weighting–Calibrated Random Forest Regression (DW-RFR) model integrating high-resolution Gamma-Ray-guided dynamic time warping (±0.06 m depth alignment precision derived from 237 core-log calibration points using cross-validation), Principal Component Analysis-Deyang–Anyue Rift Trough Shapley Additive Explanations (PCA-SHAP) hybrid feature engineering (89.3% cumulative variance, VIF < 4), and Bayesian-optimized ensemble learning; (2) systematic benchmarking against conventional ΔlogR (R² = 0.700, RMSE = 0.264) and multi-attribute joint inversion (R² = 0.734, RMSE = 0.213) methods, demonstrating superior accuracy (R² = 0.917, RMSE = 0.171); (3) identification of Gamma Ray (r = 0.82) and bulk density (r = −0.76) as principal TOC predictors, contrasted with resistivity’s thermal maturity-dependent signal attenuation (r = 0.32 at Ro > 3.0%). The methodology establishes a transferable framework for organic-rich shale evaluation, directly applicable to the Longmaxi Formation and global Precambrian–Cambrian transition sequences. Future directions emphasize real-time drilling data integration and quantum computing-enhanced modeling for ultra-deep shale systems, advancing predictive capabilities in tectonically complex basins. Full article

Archaean metamorphic basement reservoirs, characterized by the development of natural fractures, constitute the primary target for oil and gas exploration in the Bozhong Depression, Bohai Bay Basin, Eastern China. Based on analyses of geophysical image logs, cores, scanning electron microscopy (SEM), and laboratory measurements, tectonic fractures are identified as the dominant type of natural fracture. Their development is primarily controlled by lithology, weathering intensity, and faulting. Fractures preferentially develop in metamorphic rocks with low plastic mineral content and are positively correlated with weathering intensity. Fracture orientations are predominantly parallel or subparallel to fault strikes, while localized stress perturbations induced by faulting significantly increase fracture density. Open fractures, constituting more than 60% of the total reservoir porosity, serve as both primary storage spaces and dominant fluid flow conduits, fundamentally governing reservoir quality. Consequently, spatial heterogeneity in fracture distribution drives distinct vertical zonation within the reservoir. The lithological units are ranked by fracture development potential (in descending order): leptynite, migmatitic granite, gneiss, cataclasite, diorite-porphyrite, and diabase. Diabase represents the lower threshold for effective reservoir formation, whereas overlying lithologies may function as reservoirs under favorable conditions. The large-scale compressional orogeny during the Indosinian period marked the primary phase of tectonic fracture formation. Subsequent uplift and inversion during the Yanshanian period further modified and overlaid the Indosinian structures. These structures are characterized by strong strike-slip strain, resulting in a series of conjugate shear fractures. During the Himalayan period, preexisting fractures were primarily reactivated, significantly influencing fracture effectiveness. The development model of the fracture network system in the metamorphic basement reservoirs of the study area is determined by a coupling mechanism of dominant lithology and multiphase fracturing. The spatial network reservoir system, under the control of multistage structure and weathering, is key to the formation of large-scale effective reservoirs in the metamorphic basement. Full article

The accuracy of deep temperature predictions is critical to the precision of geothermal resource exploration, assessment, and the effectiveness of their development and utilization. However, the existing methods encounter significant challenges in predicting the distribution characteristics of deep temperature fields with both efficiency and accuracy. Many of these methods rely on empirical formulas to approximate the relationship between geophysical parameters and temperature. Unfortunately, such approximations often introduce substantial errors, undermining the reliability and precision of the predictions. We present an advanced prediction methodology for deep temperature fields based on the rock resistivity–temperature coupling model (RRTCM). By converting the fixed parameters in the empirical formulas to variables dependent on the formation depth, we establish a dynamic model that correlates rock resistivity with temperature on the basis of limited constrained borehole data. We then input the 2D magnetotelluric inversion results into the model, and the subsurface temperature distribution can be predicted indirectly with high precision on the basis of the resistivity–temperature coupling relationship. We validated this method in the Xiong’an New Area, China, and the determination coefficient ($R^2$) of maximum temperature prediction reached 98.88%. The sensitivity analysis indicates that the prediction accuracy is positively correlated with the number and depth of the constrained boreholes and negatively correlated with the sampling interval of the well logging data. This method robustly supports geothermal resource development and enhances the understanding of geothermal field formation mechanisms. Full article

Unconformity stratigraphic traps are widely developed in the Mahu Sag, on the northwestern margin of the Junggar Basin. It is of great significance for subsequent oil and gas exploration to explore the role of conglomerate accumulation mode and unconformity inner structure in the formation of oil and gas reservoirs. Therefore, this study uses oil and gas geophysical technology combined with geological theory to identify the P/T unconformity structure in the study area, determine the development characteristics and accumulation control of the unconformity structure, and explore the accumulation mode of stratigraphic oil and gas reservoirs. The results show the following: (1) Based on the different logging response characteristics of the upper, middle, and lower layers of the unconformity structure, five types of unconformity structure are divided according to different lithologic combinations. (2) Through experimental and numerical simulation analysis, it was verified that fracture pressure and thickness are important indicators for evaluating the sealing property of unconformity structure. P/T unconformity structure provides good floor conditions for the Baikouquan Formation reservoir, further confirming its key role in the process of oil and gas accumulation and storage. (3) Based on the analysis of actual cases, the accumulation model of stratigraphic oil and gas reservoirs under the control of unconformity structure is summarized as cross-layer accumulation above the source, fault communication source reservoir, unconformity lateral transmission and distribution, and mudstone lateral docking. The research results provide technical support and important reference values for the exploration and development of unconformity-related oil and gas reservoirs in the Junggar Basin. Full article

Geophysical logging curves are crucial for oil and gas field exploration and development, and curve reconstruction techniques are a key focus of research in this field. This study proposes an inversion model for deep resistivity curves in marine carbonate reservoirs, specifically the Mishrif Formation of the Halfaya Field, by integrating a deep learning model called CNN-GRU-ATT, which combines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and the Attention Mechanism (ATT). Using logging data from the marine carbonate oil layers, the reconstructed deep resistivity curve is compared with actual measurements to determine reservoir fluid properties. The results demonstrate the effectiveness of the CNN-GRU-ATT model in accurately reconstructing deep resistivity curves for carbonate reservoirs within the Mishrif Formation. Notably, the model outperforms alternative methods such as CNN-GRU, GRU, Long Short-Term Memory (LSTM), Multiple Regression, and Random Forest in new wells, exhibiting high accuracy and robust generalization capabilities. In practical applications, the response of the inverted deep resistivity curve can be utilized to identify the reservoir water cut. Specifically, when the model-inverted curve exhibits a higher response compared to the measured curve, it indicates the presence of reservoir water. Additionally, a stable relative position between the two curves suggests the presence of a water layer. Utilizing this method, the oil–water transition zone can be accurately delineated, achieving a fluid property identification accuracy of 93.14%. This study not only introduces a novel curve reconstruction method but also presents a precise approach to identifying reservoir fluid properties. These findings establish a solid technical foundation for decision-making support in oilfield development. Full article