Search Results (176)

The sedimentary characteristics and model of the shallow-water delta front are of great significance for the development of oil and gas reservoirs. At present, there are great differences in the understanding of the distribution patterns of estuary dams in the shallow-water delta front. Therefore, this paper reveals the distribution characteristics of estuary dams through the detailed dissection of the Qing 1 Member in the Daqingzijing area and establishes a completely new distribution pattern of estuary dams. By using geological data such as logging and core measurements, sedimentary microfacies at the shallow-water delta front are classified and logging facies identification charts for each sedimentary microfacies are developed. Based on the analysis of single-well and profile facies, the sedimentary evolution laws of the Qing 1 Member reservoirs are analyzed. On this basis, the sedimentary characteristics and model of the lacustrine shallow-water delta front are established. The results indicate that the Qing 1 Member in the Daqingzijing area exhibits a transitional sequence from a delta front to pro-delta facies and finally to deep lacustrine facies, with sediments continuously retrograding upward. Subaqueous distributary channels and estuary dams constitute the skeletal sand bodies of the retrogradational shallow-water delta. The estuary dam sand bodies are distributed on both sides of the subaqueous distributary channels, with sand body development gradually decreasing in scale from bottom to top. These bodies are intermittently distributed, overlapping, and laterally connected in plan view, challenging the conventional understanding that estuary dams only occur at the bifurcation points of underwater distributary channels. Establishing the sedimentary characteristics and model of the shallow-water delta front is of great significance for the exploration and development of reservoirs with similar sedimentary settings. Full article

The drilling core sampling and chemical analysis method for the quantitative determination of solid mineral deposits has several drawbacks, including a low core drilling efficiency, a high core sampling cost, and a long chemical analysis cycle. In current uranium quantification practices, advanced techniques have been developed to preliminarily determine the formation of uranium content based on the interpretation results of natural $\gamma$-ray total logging. However, such methods still require supplementary core chemical analysis to derive the uranium–radium–radon balance coefficient, which is then used for equilibrium correction to obtain the true uranium content within the uranium-bearing layer. Furthermore, conventional prompt neutron time spectrum logging is constrained by low count rates, resulting in slow logging speeds that fail to meet the demands of practical engineering applications. To address this, this study proposes a uranium quantification method that corrects the natural $\gamma$-ray total logging using prompt neutron time spectrum logging. Additionally, a calibration parameter determination method necessary for quantitative interpretation is constructed. Experimental results from standardized model wells indicate that, in sandstone-type uranium deposits, the absolute error of uranium content is within $\pm 0.002\%\mathrm{eU}$, and the relative error is within $\pm 2.5\%$. These findings validate the feasibility of deriving the uranium–radium–radon balance coefficient without relying on core chemical analysis. Compared with the prompt neutron time spectrum logging method, the proposed approach significantly improves the logging speed while producing results that are essentially consistent with those of natural $\gamma$-ray total logging. It provides an efficient and accurate solution for uranium quantitative interpretation. Full article

The Tankou area is a vital production capacity replacement area in the Jianghan oilfield. The recovery of the amount of erosion in Qianjiang Formation and Jinghezhen Formation is significant for studying this area’s tectonic evolution and geothermal history. The target layer, characterised by well-developed plastic materials, intense tectonic deformation, and insufficient well data, fails to meet the applicability criteria of the conventional denudation estimation methods. This study proposes a novel approach based on the structural strain characteristics. The method estimates the stratigraphic denudation by analysing residual formation features and fault characteristics. First, a stress analysis is performed using the fault characteristics, and the change law for the thickness of the target layer is summarised based on the characteristics of the residual strata to recover the amount of erosion in the profile. Second, a grid of the stratigraphic lines in the profiles of the main line and the tie line is used to complete the recovery of the amount of erosion in the plane through interpolation, and the results of the profile recovery are corrected again. Finally, the evolution results of the geological equilibrium method and the stress–strain analysis are compared to analyse the reasonableness of their differences and verify the accuracy of the erosion recovery results. The area of erosion in each layer increases from bottom to top. The amount of denudation in each layer gradually increases from the denudation area near the southern slope to the surrounding area. It converges to 0 at the boundary of the denudation area. The maximum amount of erosion is distributed in the erosion area close to the side of the residual layer with a low dip angle. The specific denudation results are as follows: Qian1 Member + Jinghezhen Formation has a denudation area of 6.3 km² with a maximum denudation thickness of 551 m; Qian2 Member has a denudation area of 2.6 km² with a maximum denudation thickness of 164 m; Qian3 Member has a denudation area of 2.3 km² with a maximum denudation thickness of 215 m; Upper Qian4 Submember has a denudation area of 1.54 km² with a maximum denudation thickness of 191 m; and Lower Qian4 Submember has a denudation area of 1.2 km² with a maximum denudation thickness of 286 m. This method overcomes the conventional denudation restoration approaches’ reliance on well logging and geochemical parameters. Using only seismic interpretation results, it achieves relatively accurate denudation restoration in the study area, thereby providing reliable data for timely analyses of the tectonic evolution, sedimentary facies, and hydrocarbon distribution patterns. In particular, the fault displacement characteristics can be employed to promptly examine how reasonable the results on the amount of denudation between faults are during the denudation restoration process. Full article

This study offers a newly improved Type-II adaptive progressive censoring with data sampled from an inverse XLindley (IXL) distribution for more efficient and adaptive reliability assessments. Through this sampling mechanism, we evaluate the parameters of the IXL distribution, as well as its reliability and hazard rate features. In the context of reliability, to handle flexible and time-constrained testing frameworks in high-reliability environments, we formulate maximum likelihood estimators versus Bayesian estimates derived via Markov chain Monte Carlo techniques under gamma priors, which effectively capture prior knowledge. Two patterns of asymptotic interval estimates are constructed through the normal approximation of the classical estimates and of the log-transformed classical estimates. On the other hand, from the Markovian chains, two patterns of credible interval estimates are also constructed. A robust simulation study is carried out to compare the classical and Bayesian point estimation methods, along with the four interval estimation methods. This study’s practical usefulness is demonstrated by its analysis of a real-world dataset. The results reveal that both conventional and Bayesian inferential methods function accurately, with the Bayesian outcomes surpassing those of the conventional method. Full article

Addressing the critical challenges of sealing integrity and operational optimization in aquifer gas storage (AGS), this study focuses on a block within the Qianjiang Basin to systematically investigate geological modeling and injection–production strategies. Utilizing 3D seismic interpretation, drilling, and logging data, a stochastic geological modeling approach was employed to construct a high-resolution 3D reservoir model, elucidating the distribution of reservoir properties and trap configurations. Numerical simulations optimized the gas storage parameters, yielding an injection rate of 160 MMSCF/day (40 MMSCF/well/day) over 6-month-long hot seasons and a production rate of 175 MMSCF/day during 5-month-long cold seasons. Interval theory was innovatively applied to assess fault stability under parameter uncertainty, determining a maximum safe operating pressure of 23.5 MPa—12.3% lower than conventional deterministic results. The non-probabilistic reliability analysis of caprock integrity showed a maximum 11.1% deviation from Monte Carlo simulations, validating the method’s robustness. These findings establish a quantitative framework for site selection, sealing system evaluation, and operational parameter design in AGS projects, offering critical insights to ensure safe and efficient gas storage operations. This work bridges theoretical modeling with practical engineering applications, providing actionable guidelines for large-scale AGS deployment. Full article

This work aimed to compare the qualitative assessment of valuable oak logs using the conventional method employed in forestry operations with the evaluation based on image outputs from a CT scanning line. A total of 125 oak logs from southwestern Slovakia were analyzed. Quantitative and qualitative features were measured using both approaches, and the logs were classified into quality classes according to the technical conditions of the STN 48 0056 standard, based on their dimensions. The qualitative assessment of logs using CT images revealed additional internal features that often resulted in the downgrading of logs to lower-quality classes. This method of evaluation increased the frequency of logs classified into lower-quality classes. Nearly half of all evaluated logs were classified into the same quality class by both assessment methods. However, 33% of the logs were classified as one quality class lower when assessed using CT images. This discrepancy can be tentatively attributed to the detection of hidden internal features, as well as the enhanced precision in measurement and feature evaluation enabled by 3D models and cross-sectional images from CT scans. Notably, more than 10% of the logs were classified into a higher quality class based on CT images compared to visual assessment. The economic evaluation based on CT classification was 3769 EUR lower than that based on the conventional method. 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

This study presents an innovative approach for repurposing depleted clastic hydrocarbon reservoirs in Hungary as High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems, integrating numerical heat transport modeling and machine learning optimization. A detailed hydrogeological model of the Békési Formation was built using historical well logs, core analyses, and production data. Heat transport simulations using MODFLOW/MT3DMS revealed optimal dual-well spacing and injection strategies, achieving peak injection temperatures around 94.9 °C and thermal recovery efficiencies ranging from 81.05% initially to 88.82% after multiple operational cycles, reflecting an efficiency improvement of approximately 8.5%. A Random Forest model trained on simulation outputs predicted thermal recovery performance with high accuracy (R² ≈ 0.87) for candidate wells beyond the original modeling domain, demonstrating computational efficiency gains exceeding 90% compared to conventional simulations. The proposed data-driven methodology significantly accelerates optimal site selection and operational planning, offering substantial economic and environmental benefits and providing a scalable template for similar geothermal energy storage initiatives in other clastic sedimentary basins. Full article

Proper geologic reservoir characterization is crucial for energy generation and climate change mitigation efforts. While conventional techniques like core analysis and well logs provide limited spatial reservoir information, seismic data can offer valuable 3D insights into fluid and rock properties away from the well. This research focuses on identifying important structural and stratigraphic variations at the Mississippi Canyon Block 118 (MC-118) field, located on the northern slope of the Gulf of Mexico, which is significantly influenced by complex salt tectonics and slope failure. Due to a lack of direct subsurface data like well logs and cores, this area poses challenges in delineating potential reservoirs for carbon storage. The study leveraged seismic multi-attribute analysis and machine learning on 3-D seismic data and well logs to improve reservoir characterization, which could inform field development strategies for hydrogen or carbon storage. Different combinations of geometric, instantaneous, amplitude-based, spectral frequency, and textural attributes were tested using Self-Organizing Maps (SOM) to identify distinct seismic facies. SOM Models 1 and 2, which combined geometric, spectral, and amplitude-based attributes, were shown to delineate potential storage reservoirs, gas hydrates, salt structures, associated radial faults, and areas with poor data quality due to the presence of the salt structures more than SOM Models 3 and 4. The SOM results presented evidence of potential carbon storage reservoirs and were validated by matching reservoir sands in well log information with identified seismic facies using SOM. By automating data integration and property prediction, the proposed workflow leads to a cost-effective and faster understanding of the subsurface than traditional interpretation methods. Additionally, this approach may apply to other locations with sparse direct subsurface information to identify potential reservoirs of interest. Full article

Lithology identification is fundamental for the logging evaluation of natural gas hydrate reservoirs. The Sanlutian field, located in the permafrost zones of the Qilian Mountains (PZQM), presents unique challenges for lithology identification due to its complex geological features, including fault development, missing and duplicated stratigraphy, and a diverse array of rock types. Conventional methods frequently encounter difficulties in precisely discerning these rock types. This study employs well logging and core data from hydrate boreholes in the region to evaluate the performance of four data-driven machine learning (ML) algorithms for lithological classification: random forest (RF), multi-layer perceptron (MLP), logistic regression (LR), and decision tree (DT). The results indicate that seven principal lithologies—sandstone, siltstone, argillaceous siltstone, silty mudstone, mudstone, oil shale, and coal—can be effectively distinguished through the analysis of logging data. Among the tested models, the random forest algorithm demonstrated superior performance, achieving optimal precision, recall, F1-score, and Jaccard coefficient values of 0.941, 0.941, 0.940, and 0.889, respectively. The models were ranked in the following order based on evaluation criteria: RF > MLP > DT > LR. This research highlights the potential of integrating artificial intelligence with logging data to enhance lithological classification in complex geological settings, providing valuable technical support for the exploration and development of gas hydrate resources. Full article

The Carboniferous reservoirs in the northwestern margin of the Junggar Basin exhibit complex lithological assemblages, primarily composed of siltstone, sandy conglomerate, tuff, and igneous rocks. These reservoirs are rich in oil and gas resources but have entered the middle to late stages of development. The reservoir spaces in the Carboniferous system are mainly composed of pores and fractures, resulting in a complex storage system. To provide effective strategies for stabilizing and enhancing production during the middle to late development stages, it is essential to establish a dual-porosity and dual-permeability model based on a clear understanding of lithological distribution patterns. This will facilitate the identification of favorable zones and the proposal of effective development strategies through numerical simulation. The present study systematically identified the lithology of the study area through microscopic lithological identification combined with logging data, conducted reservoir matrix property research under facies constraints, and established a three-dimensional geological model of lithology and physical properties. To more reasonably study the reservoir development process and establish an optimal development plan, a machine learning model for fracture density was trained using imaging logging interpretation results and conventional logging curve data. The model was then utilized to calculate single-well fracture density. Finally, a fracture model of the study area was established based on the collaborative constraints of fracture density and three-dimensional seismic attributes. Using the results of the established dual-porosity and dual-permeability model and production data, reservoir production evaluation and residual oil distribution research were conducted. The results indicate that the southwestern part of the study area features thick sandy conglomerate reservoirs with good physical properties, continuous lateral distribution, and high residual oil content, making it a dominant area favorable for horizontal well development and production. Additionally, reservoir numerical simulation was employed to study enhanced production development strategies. It is recommended to adopt gas–water alternating injection to improve production, with the optimal gas–water injection ratio of 4:1 yielding the maximum reservoir recovery factor. This study provides theoretical and technical support for the development of complex lithologic buried-hill reservoirs in the Carboniferous system of the western margin of the Junggar Basin. Full article

Accurate characterization of subsurface geological structures, particularly those obscured by strong coal-seam reflections, is essential for hydrocarbon exploration in subtle reservoirs. Enhancing seismic resolution remains a pivotal technical challenge in addressing this demand. Here, we present a multitrace reflectivity inversion method guided by geological sparsity principles. This method establishes quantitative relationships between sparse inversion operators and the spatial positions of stratigraphic boundaries. Specifically, by integrating prior geological knowledge, such as stratigraphic boundaries and stable sedimentary structures, as constraint operators within the sparsity matrix, this method results in a geologically interpretable and robust inversion framework. Subsequently, we validated this method through synthetic data and field applications in a carbonate fracture–cavity reservoir in the Ordos Basin of western China. The enhanced seismic resolution demonstrates that our method effectively restores shielded reservoir reflections beneath coal seams. Clearer than conventional sparse inversion techniques, the coherence attribute of the enhanced seismic resolution reveals distinct fracture–cavity geometries. Moreover, integrated analyses of well logs, fracture–cavity characterization, and drilling production data further confirm the accuracy and reliability of the inversion results. In conclusion, this method effectively leverages accurate geological structural information to enhance localized seismic resolution, thereby providing robust support for the exploration of subtle hydrocarbon reservoirs. Full article

Clustering plays a vital role in process mining as it organizes complex event logs into meaningful groups, helping to identify common patterns, outliers, and inefficiencies. This simplification enables organizations to detect bottlenecks and optimize workflows by uncovering trends and variations that might otherwise remain hidden. Fuzzy clustering addresses the challenge of overlapping behaviors, providing actionable insights for targeted improvements and enhanced operational efficiency. Nevertheless, conventional clustering algorithms for process mining focus either on activity sequences or cycle times, resulting in incomplete insights due to the neglect of temporal or structural variations. This work introduces a new fuzzy clustering methodology that incorporates both activity sequences and cycle times through a weighted distance metric. The proposed approach balances the weights of similarity in sequences as well as time variation flexibly using the parameter $\alpha$, enabling clusters to represent both structural as well as performance-based process attributes. Through using fuzzy C-means clustering, the method allows cases to have multiple memberships with different membership degrees, providing flexibility regarding overlapping process behavior. An experimental evaluation using real-life event logs demonstrates the effectiveness of the method in discerning process variants. It yields superior results compared to conventional methods that account for only sequence-based clustering scenarios, as well as time-based clustering methods. The results describe the significant importance of optimizing clustering results by varying $\alpha$, where a balanced weighting ($\alpha =0.5$) gives more meaningful clusters. Ultimately, the framework enhances process mining by offering detailed insights for analyzing operational inefficiencies, bottlenecks, and resource allocation mismatches, providing substantial real-world benefits for industries that demand effective process improvement. Full article

Global water scarcity and antimicrobial resistance (AMR) represent two escalating crises that urgently demand integrated and effective solutions. While wastewater reuse is increasingly promoted as a strategy to alleviate water scarcity, conventional treatment processes often fail to eliminate persistent contaminants and antibiotic-resistant microorganisms. Cold plasma (CP), a non-thermal advanced oxidation process, has demonstrated the strong potential to simultaneously inactivate pathogens and degrade micropollutants. CP generates a diverse mix of reactive oxygen and nitrogen species (ROS and RNS), as well as UV photons and charged particles, capable of breaking down complex contaminants and inducing irreversible damage to microbial cells. Laboratory studies have reported bacterial log reductions ranging from 1 to >8–9 log₁₀, with Gram-negative species such as E. coli and Pseudomonas aeruginosa showing higher susceptibility than Gram-positive bacteria. The inactivation of endospores and mixed-species biofilms has also been achieved under optimized CP conditions. Viral inactivation studies, including MS2 bacteriophage and norovirus surrogates, have demonstrated reductions >99.99%, with exposure times as short as 0.12 s. CP has further shown the capacity to degrade antibiotic residues such as ciprofloxacin and sulfamethoxazole by >90% and to reduce ARGs (e.g., bla, sul, and tet) in hospital wastewater. This perspective critically examines the mechanisms and current applications of CP in wastewater treatment, identifies the operational and scalability challenges, and outlines a research agenda for integrating CP into future water reuse frameworks targeting AMR mitigation and sustainable water management. Full article

Low-permeability sandstone reservoirs have low permeability, but due to their high porosity and difficulty in development, the development difficulty is relatively high. They can fully tap into the high potential of oil and gas resources in low-permeability sandstone reservoirs and occupy an important position in the global energy supply The study area belongs to low-permeability dense sandstone reservoir, and the destination layer has complex lithology, strong physical inhomogeneity, and complicated pore–permeability relationship, so the conventional core pore–permeability regression method and NMR SDR method do not satisfy the requirements of fine evaluation in terms of the accuracy of permeability calculation. According to the principle of resistivity measurement by electromagnetic waves with Logging While Drilling (LWD), this paper analyzes the reasons for the magnitude of resistivity divergence with Logging While Drilling at different exploration depths. There is a “low invasion phenomenon” during the drilling process of the drill bit. The higher the permeability of the formation, the more severe the “low invasion phenomenon”, and the greater the magnitude of resistivity divergence. In this paper, through the conventional log curve response characteristics and correlation analysis, the P40H/P16H parameter were selected to characterize the magnitude of resistivity divergence, and a fine evaluation model of the reservoir based on the P40H/P16H parameter was established in the study area by relying on the theory of the flow unit, and was applied to the prediction of permeability of new wells. The application results show that the calculated permeability is in good agreement with the results of core analysis, which provides a theoretical basis for the fine evaluation of low-permeability tight reservoirs. Full article