Search Results (399)

Shale oil reservoirs are complex multi-scale nanoporous media where fluid transport is governed by coupled micro-mechanisms, demanding a robust modeling framework. This study presents a novel fluid–solid coupling (FSC) numerical model that rigorously integrates the three primary scale-dependent transport phenomena: adsorption in organic nanopores, slip effects in inorganic micropores, and stress-sensitive conductivity in fractures. The model provides essential quantitative insights into the dynamic interaction between fluid withdrawal and reservoir deformation. Simulation results reveal that microstructural properties dictate the reservoir’s mechanical stability. Specifically, larger pore diameters and higher porosity enhance stress dissipation, promoting long-term stress relaxation and mitigating permeability decay. Crucially, tortuosity governs the mechanical response by controlling pressure transmission pathways: low tortuosity causes localized stress concentration, leading to rapid micro-channel closure, while high tortuosity ensures stress homogenization, preserving long-term permeability. Furthermore, high fracture conductivity induces a severe, heterogeneous stress field near the wellbore, which dictates early-stage mechanical failure. This work provides a powerful, mechanism-based tool for optimizing micro-structure and production strategies in unconventional resources. Full article

As global energy demand rises, developing unconventional oil and gas resources has become a strategic priority, with horizontal well technology playing a key role. However, wellbore instability during drilling often leads to irregular geometries, such as enlargement or elliptical deformation, causing issues like increased friction and stuck-pipe incidents. Most studies rely on idealized, regular wellbore models, leaving a gap in understanding cuttings transport in irregular wellbore conditions. To address this limitation, this study employs a coupled CFD-DEM approach to investigate cuttings transport in enlarged wellbores by modeling the two-way interactions between drilling fluid and cuttings. The study analyzes the impact of various factors, including drilling-fluid flow rate, drill pipe rotational speed, rheological parameters, wellbore enlargement ratio, and ellipticity, on wellbore cleaning efficiency. The result indicates that increasing the flow rate in conventional wellbores reduces cuttings volume by 75%, while in wellbores with a 0.7 enlargement ratio, the same flow rate only reduces it by 37.8%, highlighting the limitations of geometric complexity. In conventional wellbores, increasing drill pipe rotation reduces cuttings volume by 42.6%, but in enlarged wellbores, only a 13% reduction is observed, indicating that rotation alone is insufficient in large wellbores. Optimizing drilling fluid rheology, such as by increasing the consistency coefficient from 0.3 to 1.2, reduces cuttings volume by 58.78%, while increasing the flow behavior index from 0.4 to 0.7 results in a 38.17% reduction. Although higher enlargement ratios worsen cuttings deposition, a moderate increase in ellipticity improves annular velocity and enhances transport efficiency. This study offers valuable insights for optimizing drilling parameters in irregular wellbores. Full article

The expansion of unconventional hydrocarbon extraction in the Vaca Muerta Formation (Argentina) has increased the generation of oil-based drill cuttings (OBDCs), a hazardous waste containing up to 20 wt% total petroleum hydrocarbons (TPHs) and trace metals. These characteristics pose risks to soil and groundwater, highlighting the need for sustainable treatment technologies that minimize environmental impacts and enable resource recovery. This study evaluates slow pyrolysis as a thermochemical route for OBDC stabilization and valorization. Representative samples were characterized through proximate, ultimate, and metal analyses, confirming a complex hydrocarbon–mineral matrix with 78.1 wt% ash, 15.9 wt% volatile matter, and 12.5 wt% TPH. Thermogravimetric analysis (10–20 °C min⁻¹), combined with isoconversional methods, identified three pseudo-components with activation energies ranging from 41.9 to 104.5 kJ mol⁻¹. Slow pyrolysis experiments in a fixed bed (400–650 °C) reduced residual TPH to below 1 wt% at temperatures ≥ 400 °C, meeting Argentine criteria for non-hazardous solids. The process also produced a condensed liquid organic fraction, supporting its potential within circular-economy strategies. Overall, the results show that slow pyrolysis is a viable and sustainable technology for reducing environmental risks from OBDC while enabling resource and energy recovery, contributing to a broader understanding of their thermochemical treatment. Full article

As the retrieval of unconventional oil and gas resources extends to the deep and ultra-deep domains, the issue of cement sheath failure in shale oil wellbores seriously endangers wellbore safety, making it imperative to uncover the relevant damage mechanism and develop effective assessment approaches. In response to the limitations of conventional finite element methods in representing mesoscopic damage, in this study, we determined the mesoscopic parameters of cement paste via laboratory calibrations; constructed a 3D casing–cement sheath–formation composite model using the discrete element method; addressed the restriction of the continuum assumption; and numerically simulated the microcrack initiation, propagation, and interface debonding behaviors of cement paste from a mesomechanical viewpoint. The model’s reliability was validated using a full-scale cement sheath sealing integrity assessment apparatus, while the influences of fracturing location, stage count, and internal casing pressure on cement sheath damage were analyzed systematically. Our findings indicate that the DEM model can precisely capture the dynamic evolution features of microcracks under cyclic loading, and the results agree well with the results of the cement sheath sealing integrity evaluation. During the first internal casing pressure loading phase, the microcracks generated account for 84% of the total microcracks formed during the entire loading process. The primary interface (casing–cement sheath interface) is fully debonded after the second internal pressure loading, demonstrating that the initial stage of cyclic internal casing pressure exerts a decisive impact on cement sheath integrity. The cement sheath in the horizontal well section is subjected to high internal casing pressure and high formation stress, resulting in more frequent microcrack coalescence and a rapid rise in the interface debonding rate, whereas the damage progression in the vertical well section is relatively slow. Full article

The imbalance between water supply and demand in the arid and semi-arid regions of northwest China has become increasingly severe, highlighting the urgent need to develop and utilize unconventional water resources. Return flow, originating from canal leakage and field drainage, is widely distributed in these regions. However, as it contains a certain amount of salts, long-term use of return flow can lead to soil salinization and degradation of soil structure. Therefore, the scientific utilization of return flow has become a key issue for achieving sustainable agricultural development and efficient water use in arid areas. This study was conducted in the Yinbei Irrigation District, Ningxia, northwest China. Water samples were collected from the main and branch drainage ditches and analyzed to evaluate the feasibility of using return flow irrigation in the area. In addition, based on two years of continuous field monitoring and HYDRUS model simulations, the long-term dynamics of soil salinity under moderate return flow irrigation over the next 20 years were predicted. The results show that the total salinity of the main return ditches consistently remained below the agricultural irrigation water quality standard of 2000 mg/L, with Na⁺ and SO₄²⁻ as the predominant ions. Seasonal variations in return flow salinity were notable, with higher levels observed in spring compared to summer. Simulation results based on field trial data indicated that soil salinity displayed regular seasonal fluctuations. During the rice-growing season, strong leaching kept the salinity in the plough layer (0–40 cm) low. However, after irrigation ceased, evaporation in autumn and winter led to an increase in surface soil salinity, creating annual peaks. Long-term simulations showed that soil salinity throughout the entire profile (0–100 cm) followed a pattern of “slight increase—gradual decrease—dynamic stability.” Specifically, winter salinity peaks slightly increased during the first two years but then gradually declined, stabilizing after approximately 15 years. This indicates that long-term return-flow irrigation does not result in the accumulation of soil salinity in the plough layer. Full article

The economic importance of Carlin-type gold deposits is complicated by the concealed nature of stratiform gold-bearing zones and their occurrence at depths of several tens of meters or more below the present-day surface. This necessitates the use of a wide range of technologies and unconventional, including cost-effective and environmentally friendly, exploration methods to delineate potentially prospective areas. This study explores the possibilities of applying remote sensing methods to organize prospecting and exploration activities for targeting Carlin-type deposits in a more efficient and cost-effective way. The location of Carlin-type gold deposits within areas of orogenic and post-orogenic magmatism, mantle plumes, and linear crustal structures—as demonstrated by previous research in the Nevada and South China metallogenic provinces—may serve as a basis for developing a conceptual model of their distribution. To this end, we developed the GeoNEM (Geodynamic Numeric Environmental Modeling) software in Python, which enables the analysis of the formation of fold and fault structures, melt emplacement and contamination, as well as the duration and rate of geodynamic processes. GeoNEM is based on the computational geodynamics “marker-in-cell” (MIC) method, which treats geological media as extremely high-viscosity fluids. Locations of the brittle deformations of the crust, the formation of which was simulated numerically, can be detected through lineament analysis of remote sensing images. The spatial distribution of such structures—lineaments—serves as a predictive criterion for assessing the prospectivity of territories for Carlin-type gold deposits. It has been demonstrated that remote sensing provides a modern level of efficiency, cost-effectiveness, and comprehensiveness in approaching the exploration and assessment of new Carlin-type gold deposits. This is particularly important in the context of rational resource utilization and cost reduction. Full article

Characterizing and simulating complex reservoirs, particularly unconventional resources with multiscale and non-homogeneous features, presents significant bottlenecks in cost, efficiency, and accuracy for conventional research methods. Consequently, there is an urgent need for the digital and intelligent transformation of the field. To address this challenge, this paper proposes that the core solution lies in the deep integration of physical mechanisms and data intelligence. We systematically review and define a new research paradigm characterized by the trinity of digital cores (geometric foundation), physical simulation (mechanism constraints), and artificial intelligence (efficient reasoning). This review clarifies the core technological path: first, AI technologies such as generative adversarial networks and super-resolution empower digital cores to achieve high-fidelity, multiscale geometric characterization; second, cross-scale physical simulations (e.g., molecular dynamics and the lattice Boltzmann method) provide indispensable constraints and high-fidelity training data. Building on this, the methodology evolves from surrogate models to physics-informed neural networks, and ultimately to neural operators that learn the solution operator. The analysis demonstrates that integrating these techniques into an automated “generation–simulation–inversion” closed-loop system effectively overcomes the limitations of isolated data and the lack of physical interpretability. This closed-loop workflow offers innovative solutions to complex engineering problems such as parameter inversion and history matching. In conclusion, this integration paradigm serves not only as a cornerstone for constructing reservoir digital twins and realizing real-time decision-making but also provides robust technical support for emerging energy industries, including carbon capture, utilization, and sequestration (CCUS), geothermal energy, and underground hydrogen storage. Full article

Dysprosium is one of the most critical elements for global economies due to its essential role in the green energy transition. Although it is added in small quantities as an alloying element, dysprosium plays a crucial role in NdFeB magnets used in wind turbines and industrial motors. On the other hand, the limited resources and production capacity of dysprosium contribute to supply shortages and raise concerns about its long-term availability. Therefore, there is a need for efficient techniques that will enable the recovery of dysprosium from secondary materials to bridge the gap between supply and demand while addressing the risks associated with securing a stable supply. This review focuses on (bio)hydrometallurgical and solvometallurgical methods for recovering dysprosium from key secondary sources such as spent NdFeB magnets, phosphogypsum, and coal ash. Although these wastes do not always contain high concentrations of dysprosium, they can have a simpler elemental composition compared to primary sources (a few tens or hundreds of ppm Dy) and are more readily available. Spent NdFeB magnets, with a few percent Dy, show the most promise for recycling. In contrast, coal fly ashes (with several ppm Dy), although widely available, bind dysprosium in an inert phase, requiring substantial pretreatment to enhance the release of the desired element. Phosphogypsum, while not yet a significant source of dysprosium (several ppm Dy), is increasingly recognized as a potential source for other rare earth elements. Although conventional hydrometallurgical methods are commonly used, these are typically unselective for dysprosium recovery, whereas unconventional solvometallurgical approaches show preferential extraction of dysprosium over base metals. Full article

The global transition toward electrification has accelerated the need for high-performance, sustainable electric machine designs. Emerging manufacturing techniques, particularly additive manufacturing, have enabled the development of complex and unconventional machine topologies. Designing novel machine topologies often relies on data-driven methods and topology optimization, which can be computationally intensive. Semi-analytic modeling offers an effective middle ground by balancing computational efficiency with modeling accuracy—positioned between fully analytical formulations and resource-intensive numerical simulations. While its advantages are recognized, the current literature lacks a unified overview of semi-analytic approaches applied across coupled multiphysics domains, including electromagnetic, thermal, and structural analyses. This paper addresses that gap by presenting a comprehensive review of recent semi-analytic modeling techniques relevant to electric machine design. The goal is to establish a foundational reference for researchers aiming to incorporate these models into advanced topology optimization frameworks. Full article

With the continuous advancement of shale gas field development, well productivity following initial hydraulic fracturing often declines due to mechanisms such as proppant embedment and fracture conductivity degradation. However, such wells may still retain significant development potential, making re-fracturing crucial for restoring production and highlighting the critical importance of accurate candidate selection for re-fracturing. To improve the precision of candidate well selection for re-fracturing in shale gas wells, this study focuses on a shale gas block in the Southern Chuan Basin. Through comparative analysis of existing selection methods, 14 key parameters were finalized. The threshold values for some of these key parameters were recalibrated based on the specific geological, engineering, and production characteristics of the target block in the Southern Chuan Basin. Furthermore, the AHP-GRA (Analytic Hierarchy Process-Gray Relational Analysis) weighting method was integrated to achieve a balance between empirical knowledge and quantitative objectivity. Ultimately, a more targeted, comprehensive, and combined subjective–objective methodology for selecting re-fracturing candidate wells was developed. A computational tool developed in Python 3.9 was utilized to evaluate 13 candidate wells in the block, successfully identifying three high-priority wells for re-fracturing implementation. The reliability of this selection result was validated by analyzing production data before and after re-fracturing, confirming that the production performance of the selected wells showed relatively significant improvement post re-fracturing, with a notable increase in recovery factor. This model provides critical decision-making support for the low-cost and large-scale development of shale gas. It holds significant theoretical and practical value for promoting the secondary development of mature shale gas wells and contributes positively to the efficient utilization of unconventional natural gas resources and energy security. Full article

Tight sandstone gas constitutes a strategically significant resource in the exploration of unconventional hydrocarbon systems. Current understanding of the geochemical composition and genesis of tight sandstone gas in the Daning–Jixian Block, southeastern Ordos Basin, is insufficient, which hampers a comprehensive assessment of its resource potential. This study is the first to systematically investigate the geochemical characteristics and genetic origin of high-maturity tight sandstone gas in the southeastern Ordos Basin’s Daning–Jixian Block. Gas specimens were systematically acquired from multiple stratigraphic units within the reservoir interval and subjected to compositional and carbon–hydrogen isotope analysis. Compared with other gas fields in the Ordos Basin, the Daning–Jixian Block has higher average methane concentration, and notably lower ethane and propane concentrations; its average δ¹³C₁ and δ²H-CH₄ is heavier, while δ¹³C₂ and δ¹³C₃ are lighter. Based on the δ¹³C₁-δ²H-CH₄ diagram, all gas samples from the block and other basin gas fields fall into the geothermal, hydrothermal and crystalline gas domain, indicating gas genesis associated with over-mature organic matter interacting with external hydrogen. Milkov genetic diagram analysis reveals that the natural gas consists of primarily early-stage kerogen-cracking gas, with a minor contribution from crude oil-derived gas originating from Carboniferous–Permian source rocks. Notably, samples from Daning–Jixian exhibit a unique δ¹³C₁ > δ¹³C₂ reversal, attributed to mixing effects between gas from highly mature kerogen and gas from secondary cracking of crude oil. Consequently, ethane carbon isotopes alone are insufficient for definitive genetic classification. These findings provide a new geochemical interpretation framework for analogous high-maturity tight gas reservoirs. Full article

Although shale gas is an important energy source in the energy transition, its development faces multidimensional challenges across economic, environmental, social and technological domains. Traditional evaluation methods struggle to quantify interdependencies among indicators or capture their overall benefits. To address this, we propose a sustainable development evaluation framework for shale gas that integrates 25 indicators across four dimensions: economic, environmental, social and technical. Entropy weighting is used to determine indicator weights, and principal component analysis (PCA) is applied to reduce dimensionality, Gaussian copula functions are then used to model inter-indicator dependencies, and Monte Carlo simulation (10,000 iterations) is used to quantify the distribution of comprehensive benefits under uncertainty. The key findings are as follows: (1) the environmental and technological dimensions carry the highest weights at 29% and 28%, respectively; (2) the PCA–Monte Carlo (PMC) development model achieves a comprehensive benefit score of 0.567, and 22% higher than the traditional model’s score of 0.467 with a 90% confidence interval of [2%, 46%]; and (3) sensitivity analysis identifies the most influential drivers as the hazardous waste compliance rate (impact coefficient 0.92), the community conflict resolution rate (0.367), and community satisfaction (0.26). The marginal benefits of environmental compliance and social governance substantially exceed those of traditional economic indicators, offering scientific guidance for the green transformation of the shale gas industry. The integrated PCA–copula–Monte Carlo framework also provides a methodological reference for the sustainable assessment of other unconventional resources. Full article

As unconventional hydrocarbon resources gain increasing importance, the risk of lost circulation during drilling operations has also grown significantly. Accurate and reliable risk diagnosis methods are essential to ensure safety and operational efficiency in complex drilling environments. This study proposes a novel lost circulation risk monitoring framework based on time-series shapelet transformation, integrated with Generative Adversarial Network (GAN)-based data augmentation and real-time model updating strategies. GANs are employed to synthesize diverse, high-quality samples, enriching the training dataset and improving the model’s ability to capture rare or latent lost circulation signals. Shapelets are then extracted from the time series using a supervised shapelet transform that searches for discriminative subsequences maximizing the separation between normal and lost-circulation samples. Each time series is subsequently represented by its minimum distances to the learned shapelets, so that critical local temporal patterns indicative of early lost circulation can be explicitly captured. To further enhance adaptability during field applications, a real-time model updating mechanism is incorporated. The system incrementally refines the classifier using newly incoming data, where high-confidence predictions are selectively added for online updating. This strategy enables the model to adjust to evolving operating conditions, improves robustness, and provides earlier and more reliable risk warnings. We implemented and evaluated Support Vector Machine (SVM), k-Nearest Neighbors (kNNs), Logistic Regression, and Artificial Neural Networks (ANNs) on the transformed datasets. Experimental results demonstrate that the proposed method improves prediction accuracy by 6.5%, measured as the accuracy gain of the SVM classifier after applying the shapelet transformation (from 84.7% to 91.2%) compared with using raw, untransformed time-series features. Among all models, SVM achieves the best performance, with an accuracy of 91.2%, recall of 90.5%, and precision of 92.3%. Moreover, the integration of real-time updating further boosts accuracy and responsiveness, confirming the effectiveness of the proposed monitoring framework in dynamic drilling environments. The proposed method offers a practical and scalable solution for intelligent lost circulation monitoring in drilling operations, providing a solid theoretical foundation and technical reference for data-driven safety systems in dynamic environments. Full article

Background: Although modifiers and function words are critical in cognitive linguistic assessments and cognitive training has proven to promote synaptic neural activity, they often receive limited attention, particularly in computational data-scarce settings. This study addresses communication difficulties associated with cognitive impairments using engineering data, a design to improve the evaluation of language attributes, applied specifically to these elements. A framework was developed to analyze potential language alterations resulting from traumatic brain injury (tbi), using narrative samples, primary data, and unconventional methods to overcome the limitations of existing resources. Methods: The core technique involves pairing language attributes based on defined relationships and assessing responses using standard statistical learning methods. Direct and normalized evaluations of variables, calculated using the Northwestern Narrative Language Analysis (nnla) profile from the original data, serve as benchmarks. The Area Under the Curve (auc) metric with the corresponding statistical support are reported. Results: The results indicate that the proposed method revealed informative patterns involving modifiers and function words that remained hidden in the baseline approaches. Although some exceptions were observed, results showed a substantially consistent behavior, and the responses achieved promote their use in a clinical setting. Conclusions: The findings can provide valuable directions for theoretical and applied research in language assessment. Identifying specific points of breakdown within language structures can improve the accuracy of rehabilitation plans and better leverage the neuroplastic response of the brain for recovery. Full article