Search Results (169)

The central and deep reservoirs of the Wushi Sag in the Beibu Gulf Basin, China, are characterized by structurally complex settings, strong heterogeneity, multiple controlling factors for physical properties of reservoirs, rapid lateral variations in reservoir thickness and petrophysical properties, and limited seismic resolution. To address these challenges, this study integrates the INPEFA inflection point technique and Morlet wavelet transform to delineate system tracts and construct a High-Frequency Stratigraphic Framework (HFSF). Sedimentary facies are identified through the integration of core descriptions and seismic data, enabling the mapping of facies distributions. The vertical constraints provided by the stratigraphic framework, combined with the lateral control from facies distribution, which, based on identification with logging data and geological data, support the construction of a geologically consistent low-frequency initial model. Subsequently, geostatistical seismic inversion is performed to derive acoustic impedance and lithological distributions within the central and deep reservoirs. Compared with the traditional methods, the accuracy of the inversion results of this method is 8% higher resolution than that of the conventional methods, with improved vertical resolution to 3 m, and enhances the lateral continuity matched with the sedimentary facies structure. This integrated workflow provides a robust basis for predicting the spatial distribution of sandstone reservoirs in the Wushi Sag’s deeper stratigraphic intervals. Full article

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

Complex carbonate reservoirs feature large-scale karst cavern structures, exhibiting complex pore and bottom water energy distributions, which increase the difficulty of reservoir development and require targeted research. This paper proposes a new method for dynamic reserves calculation in these reservoirs based on the Differential Processing Method (DPM) and aimed at optimizing the development of complex reservoirs. The AD22 unit of the Tarim Oilfield in Xinjiang is taken as the research object, and this reservoir features complex karst and fault characteristics, which traditional reserves calculation methods cannot effectively capture due to its complex heterogeneous distribution. This study constructs a refined reservoir numerical model through 3D geological modeling and impedance inversion techniques, calculates dynamic reserves using the DPM, and compares the result with traditional material balance and production data analysis methods. The results indicate that the DPM has an advantage in estimating the petrophysical parameters and reserve utilization in such reservoirs. The error between the constructed reservoir numerical model and the actual reservoir development historical data is only 2.04%, demonstrating a good reference value. The model shows that more than 60% of the recoverable reserves in the target unit are located in areas shallower than 160 m underground, while the current development degree is only 12.6%. The model shows that the recovery rate is low in the strong bottom water energy areas of the unit, while the recovery potential is high in the weak bottom water areas. Therefore, a differentiated development strategy based on varying bottom water energy is required to enhance development efficiency. The model indicates that this strategy can improve the comprehensive development benefits of the reservoir by 81.66% over the existing baseline, demonstrating significant potential. This study provides new ideas and methods for dynamic reserve estimation and development strategy optimization for complex carbonate reservoirs, verifies the effectiveness of the DPM in evaluating the development of complex bottom water energy reservoirs, and offers data references for related research and field applications. Full article

Understanding diagenetic processes is fundamental to characterizing heterogeneous carbonate reservoirs, where variations in pore structures and mineralogy significantly influence reservoir quality and fluid flow behavior. This study presents an integrated diagenetic classification approach applied to the upper Dalan and Kangan formations in the Persian Gulf. Utilizing extensive core analyses, petrographic studies, scanning electron microscopy (SEM) imaging, and petrophysical data, six distinct diagenetic classes were identified based on the quantification of key processes such as dolomitization, dissolution, cementation, and compaction. The results reveal that dolomitization and dissolution enhance porosity and permeability, particularly in high-energy shoal facies, while cementation and compaction tend to reduce reservoir quality. A detailed petrographic examination and rock typing, including pore type classification and hydraulic flow unit analysis using flow zone indicator methods, allowed the subdivision of the reservoir into hydraulically meaningful units with consistent petrophysical characteristics. The application of the Stratigraphic Modified Lorenz Plot facilitated large-scale reservoir zonation, revealing the complex internal architecture and significant heterogeneity controlled by depositional environments and diagenetic overprints. This diagenetic classification framework improves predictive modeling of reservoir behavior and fluid distribution, supporting the optimization of exploitation strategies in heterogeneous carbonate systems. The approach demonstrated here offers a robust template for similar carbonate reservoirs worldwide, emphasizing the importance of integrating diagenetic quantification with multi-scale petrophysical and geological data to enhance reservoir characterization and management. Full article

The influence of porosity on rock mechanical properties constitutes a critical research focus. This investigation explores the relationship between pore structure parameters and mechanical characteristics through reconstructed numerical models. The study employs an integrated approach combining laboratory experiments and numerical simulations. Initially, high-resolution X-ray computed tomography (CT) was utilized to capture three-dimensional geometric features of Sichuan white sandstone microstructures, complemented by mechanical parameter acquisition through standardized testing protocols. The research workflow incorporated advanced image processing techniques, including adaptive total variation denoising algorithms for CT image enhancement and deep learning-based threshold segmentation for feature extraction. Subsequently, pore structure reconstruction models with controlled porosity variations were developed for systematic numerical experimentation. Key findings reveal a pronounced degradation trend in both mechanical strength and elastic modulus with increasing porosity levels. Based on simulation data, two empirical models were established: a porosity–compressive strength correlation model and a porosity–elastic modulus relationship model. These quantitative formulations provide theoretical support for understanding the porosity-dependent mechanical behavior in rock mechanics. The methodological framework and results presented in this study offer valuable insights for geological engineering applications and petrophysical characteristic analysis. Full article

This study presents a comprehensive multifractal characterization of full-scale pore structures in middle- to high-rank coal reservoirs from the Western Guizhou–Eastern Yunnan Basin and establishes a permeability prediction model integrating fractal heterogeneity and pore throat parameters. Eight coal samples were analyzed using mercury intrusion porosimetry (MIP), low-pressure gas adsorption (N₂/CO₂), and multifractal theory to quantify multiscale pore heterogeneity and its implications for fluid transport. Results reveal weak correlations (R² < 0.39) between conventional petrophysical parameters (ash yield, volatile matter, porosity) and permeability, underscoring the inadequacy of bulk properties in predicting flow behavior. Full-scale pore characterization identified distinct pore architecture regimes: Laochang block coals exhibit microporous dominance (0.45–0.55 nm) with CO₂ adsorption capacities 78% higher than Tucheng samples, while Tucheng coals display enhanced seepage pore development (100–5000 nm), yielding 2.5× greater stage pore volumes. Multifractal analysis demonstrated significant heterogeneity (Δα = 0.98–1.82), with Laochang samples showing superior pore uniformity (D₁ = 0.86 vs. 0.82) but inferior connectivity (D₂ = 0.69 vs. 0.71). A novel permeability model was developed through multivariate regression, integrating the heterogeneity index (Δα) and effective pore throat diameter (D₁₀), achieving exceptional predictive accuracy. The strong negative correlation between Δα and permeability (R = −0.93) highlights how pore complexity governs flow resistance, while D₁₀’s positive influence (R = 0.72) emphasizes throat size control on fluid migration. This work provides a paradigm shift in coal reservoir evaluation, demonstrating that multiscale fractal heterogeneity, rather than conventional bulk properties, dictates permeability in anisotropic coal systems. The model offers critical insights for optimizing hydraulic fracturing and enhanced coalbed methane recovery in structurally heterogeneous basins. Full article

The transient electromagnetic (TEM) method is a widely used geophysical technique for detecting subsurface electrical structures. However, its inversion results are typically limited to resistivity parameters, making it challenging to directly infer key petrophysical properties, such as water saturation and porosity. This study proposes a petrophysics parameter inversion approach based on TEM data. By constructing multiple geoelectric models with varying porosities and water saturation values for numerical simulations, the results demonstrated that both the forward and inversion responses of the TEM field maintained errors within 5%. The inversion procedure begins with the reconstruction of the subsurface resistivity distribution, which reliably reflects the true geoelectric model. Based on the inverted resistivity, the water saturation and porosity parameters are subsequently estimated. The inversion results closely match the overall trend of the actual model and exhibit a clear response at the target layer. Finally, the proposed method is applied to a field test at the Tongxin Coal Mine. By integrating subsurface electrical responses with geological data, the spatial distributions of water saturation and porosity within the coal-bearing strata were delineated. This provides a scientific basis for the detailed characterization of the physical properties of coal and surrounding rock, as well as for understanding the development of pores and fractures in underground strata. Full article

The tight sandstone gas reservoirs in the LX area of the Ordos Basin are characterized by low porosity, poor permeability, and strong heterogeneity, which significantly complicate fluid type identification. Conventional methods based on petrophysical logging and core analysis have shown limited effectiveness in this region, often resulting in low accuracy of fluid identification. To improve the precision of fluid property identification in such complex tight gas reservoirs, this study proposes a hybrid deep learning model named ResViTNet, which integrates ResNet (residual neural network) with ViT (vision transformer). The proposed method transforms multi-dimensional logging data into thermal maps and utilizes a sliding window sampling strategy combined with data augmentation techniques to generate high-dimensional image inputs. This enables automatic classification of different reservoir fluid types, including water zones, gas zones, and gas–water coexisting zones. Application of the method to a logging dataset from 80 wells in the LX block demonstrates a fluid identification accuracy of 97.4%, outperforming conventional statistical methods and standalone machine learning algorithms. The ResViTNet model exhibits strong robustness and generalization capability, providing technical support for fluid identification and productivity evaluation in the exploration and development of tight gas reservoirs. 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

This study incorporates the comprehensively observed proxies of in situ geotechnical, geophysical, petrophysical, and lithological datasets to estimate groundwater presence. Two machine-learning approaches, random forest regression (RFR) and deep neural network (DNN), are applied. The constructed RFR and DNN models are validated using observed depths of groundwater levels at 772 K-NET sites in Japan. The RFR model exhibited effectiveness and robust performance compared to the poor-fitting performance of the DNN model and previous groundwater detection physical-based approaches. The RFR and DNN models yielded a remarkable 1:1 agreement between the observed and predicted groundwater levels at 733 and 470 K-NET sites, respectively. During the RFR training process, all datasets at the 772 K-NET sites were split into training, validating, and unseen testing datasets with the ratio set at 1:1:11. This k-fold cross-validation strategy demonstrates better-fitting performance for the RFR model. The contributions and interactions among the in situ observed proxies utilizing the variance-based global sensitivity analyses can be understood. The P-wave velocity and the standard penetration test values have exhibited prominent contributions among other proxies at groundwater depths. To apply the RFR model at any given site, reliable and detailed P- and S-wave velocity structures are crucial to building the needed source datasets. Full article

Organic matter (OM)-hosted pores play a crucial role in unconventional shale reservoirs, with their development influenced by OM type and thermal maturity across terrestrial, transitional, and marine deposits. In this study, a comparative analysis of porosity and pore structures is presented using organic petrographical, petrophysical, and mineralogical methods on organic-rich samples from diverse depositional environments. A pore evolution model for these sediments in different settings is proposed. Results show that kerogen particles in terrestrial shales at low and moderate thermal maturity (Dameigou Formation and Qingshankou Formation) are mostly nonporous. Transitional shales (Longtan Formation) contain vitrinite and inertinite, with only some inertinite exhibiting visible primary pores. In marine shales at higher maturity (late oil window; Dalong Formation), the interparticle pore space is occupied by solid bitumen, and secondary porosity is present at higher maturity, approaching the thermal gas generation stage. In over-mature marine shales (Wujiaping and Daye Formations), secondary pores are densely distributed within pyrobitumen. A negative correlation between organic carbon content and pore volume is observed in low-maturity lacustrine and transitional shales due to poorly developed kerogen-bound pores and interparticle pore occlusion by solid bitumen. However, over-mature marine shales exhibit a strong positive correlation due to extensive secondary porosity in pyrobitumen. Thus, pore evolution within OM is controlled by kerogen type and maturity. In oil-prone marine and lacustrine shales, secondary porosity in solid bitumen and pyrobitumen increases with thermal maturity. In contrast, terrestrial kerogen rarely forms solid bitumen and mainly develops micropores rather than mesopores at high maturity. Full article

Electrofacies are log-related signatures that reflect specific physical and compositional characteristics of rock units. The concept was developed to encapsulate a collection of recorded well-log responses, enabling the characterization and differentiation of one rock unit from another. The analysis of the lateral and vertical distribution of electrofacies is crucial for understanding reservoir properties; however, well-log analysis can be labor-intensive, time-consuming, and prone to inaccuracies due to the subjective nature of the process. In addition, there is no unique way of reliably classifying logs or deriving electrofacies due to the varying accuracy of different methods. In this study, we develop a workflow that mitigates the variability in results produced by different clustering algorithms using a committee machine. Using several unsupervised machine learning methods, including k-means, k-median, hierarchical clustering, spectral clustering, and the Gaussian mixture model, we predict electrofacies from wireline well log data and generate their 3D vertical and lateral distributions and inferred geological properties. The results from the different methods are used to constitute a committee machine, which is then used to implement electrofacies-guided well placement. 3D distributed petrophysical properties are also computed from core-calibrated porosity and permeability data for reservoir simulation. The results indicate that wells producing from a specific electrofacies, as predicted by the committee machine, have significantly better production than wells producing from other electrofacies. This proposed detailed machine learning workflow allows for strategic decision-making in development and the practical application of these findings for improved oil recovery. Full article

Buried hill reservoirs are characterized by complex formation conditions and highly heterogeneous rock structures, which result in the poor performance of traditional crossplot methods in stratigraphic lithology classification. Logging curves, as comprehensive reflections of various petrophysical properties, are influenced by complex geological factors, leading to overlapping response values even among different lithologies with similar physical properties. This overlap negatively impacts the accuracy of intelligent lithology identification methods. To address this challenge, this study leverages logging response data, experimental data, and mud logging data to propose an optimized inversion method for mineral content, introducing mineral curves to resolve the curve overlap issue. By analyzing six wells in the study area, models were constructed using the calculated mineral content curves and conventional logging features to mitigate the feature overlap. The XGBoost algorithm was employed to identify lithologies by addressing the nonlinear relationships inherent in complex reservoirs. The experimental results indicate that the optimized mineral curves significantly enhance the model’s discriminative capability, effectively addressing the decline in identification accuracy due to feature overlap. Compared to models such as Random Forest (RF) and Support Vector Machine (SVM), the XGBoost model demonstrated superior accuracy and stability, providing a reliable basis for precise reservoir identification in the study area. Full article

Shear wave velocity prediction is critical for applications in petrophysics, reservoir characterization, and unconventional energy resource development. While empirical formulas and theoretical rock physics models offer solutions, they are often limited by geological complexity, high cost, and computational inefficiency. After the emergence of deep learning methods, a series of new approaches have been provided to tackle these problems. In this study, a novel Inception–attention–BiLSTM hybrid network is proposed to enhance shear wave prediction accuracy and stability by integrating the strengths of three components: Inception for multi-scale feature extraction, attention mechanisms for dynamically highlighting key temporal features, and BiLSTM for capturing long-term dependencies in logging data. The test dataset of this network comes from the Jurassic Badaowan Formation in the Junggar Basin, achieving superior performance compared to standalone Inception and BiLSTM networks. The proposed hybrid network demonstrated MAE and R² values of 0.211 and 0.994, respectively, outperforming Inception (MAE 0.671, R² 0.981) and BiLSTM (MAE 0.215, R² 0.991). These results underscore its robustness in handling complex logging data, providing a more accurate and generalizable framework for Vs prediction while addressing limitations of traditional methods. This work highlights the potential of hybrid deep learning architectures in advancing logging data analysis and reservoir characterization. Full article