Search Results (788)

Staged multi-cluster fracturing in horizontal wells is a key technology for efficiently developing unconventional oil and gas reservoirs. Extreme Limited-Entry Fracturing (ELF) and Temporary Plugging Fracturing (TPF) are effective techniques to enhance the uniformity of fracture stimulation within a stage. However, in fractured reservoirs, the propagation morphology of multiple intra-stage fractures and fluid distribution patterns becomes significantly more complex under the influence of ELF and TPF. This complexity results in a lack of theoretical guidance for optimizing field operational parameters. This study establishes a competitive propagation model for multiple hydraulic fractures (HFs) within a stage under ELF and TPF conditions in fractured reservoirs based on the Displacement Discontinuity Method (DDM) and fluid mechanics theory. The accuracy of the model was verified by comparing it with laboratory experimental results and existing numerical simulation results. Using this model, the influence of ELF and TPF on intra-stage fracture propagation morphology and fluid partitioning was investigated. Results demonstrate that extremely limited-entry perforation and ball-sealer diversion effectively mitigate the additional flow resistance induced by both the stress shadow effect and the connection of natural fractures (NFs), thereby mitigating uneven fluid distribution and imbalanced fracture propagation among clusters. ELF artificially creates extremely high perforation friction by drastically reducing the number of perforations or the perforation diameter, thereby forcing the fracturing fluid to enter multiple perforation clusters relatively uniformly. Compared to the unlimited-entry scheme (16 perforations/cluster), the limited-entry scheme (5 perforations/cluster) yielded a 37.84% improvement in fluid distribution uniformity and reduced the coefficient of variation (CV) for fracture length and fluid intake by 54.28% and 44.16%, respectively. The essence of the TPF is non-uniform perforation distribution, which enables the perforation clusters with large fluid intake to obtain more temporary plugging balls (TPBs), so that their perforation friction can be increased and their fluid intake can be reduced, thereby diverting the fluid to the perforation clusters with small fluid intake. Deploying TPBs (50% of total perforations) at the mid-stage of fracturing (50% time) increased fluid distribution uniformity by 37.86% and reduced the CV of fracture length and fluid intake by 72.54% and 58.39%, respectively. This study provides methodological and modeling foundations for systematic optimization of balanced stimulation parameters in fractured reservoirs. Full article

Slickwater fracturing has become an adopted technology for enhancing hydrocarbon recovery from unconventional, low-permeability reservoirs such as shale and tight formations, owing to its ability to generate complex fracture networks at a low cost. Polyacrylamide and polyacrylamide-based gels serve as key additives in these fluids, primarily functioning as drag reducers and thickeners. However, downhole environments of high-temperature (>120 °C) and high-salinity (>1 × 10⁴ mg/L) reservoirs pose challenges, leading to thermal degradation and chain collapse of conventional polyacrylamide, which results in performance loss. To address these limitations, synthesis methods including aqueous solution polymerization, inverse emulsion polymerization, and aqueous dispersion polymerization have been developed. This review provides an overview of molecular design methods aimed at enhancing performance stability of polyacrylamide-based polymers under extreme conditions. Approaches for improving thermal stability involve synthesis of ultra-high-molecular-weight polyacrylamide, copolymerization with resistant monomers, and incorporation of nanoparticles. Methods for enhancing salt tolerance focus on grafting anionic, cationic, or zwitterionic side chains onto the polymer backbone. The drag reduction mechanisms and gelation methods of these polymers in slickwater fracturing fluids are discussed. Finally, this review outlines research directions for developing next-generation polyacrylamide polymers tailored for extreme reservoir conditions, offering insights for academic research and field applications. Full article

Gas injection is a well-established method for enhancing oil recovery by improving oil mobility, primarily through viscosity reduction. While its application in heavy oil reservoirs is extensively studied, the specific impact of carbon dioxide (CO₂) injection pressure on fluid viscosity reduction and the ultimate recovery factor from light oil reservoirs has not been fully investigated. To address this gap, this experimental study systematically explores the effects of CO₂ injection pressure and reservoir permeability on light oil recovery. This study conducted miscible, near-miscible, and immiscible gas injection experiments on two core samples with distinct permeabilities (13.4 md and 28 md), each saturated with light oil. CO₂ was injected at five different pressures, including conditions ranging from immiscible to initial reservoir pressure. The primary metrics for evaluation were the recovery factor (measured at gas breakthrough, end of injection, and abandonment pressure) and the viscosity reduction of the produced oil. The results conclusively demonstrate that CO₂ injection significantly enhances light oil production. A direct proportional relationship was established between both the injection pressure and the recovery factor and between permeability and overall oil production at the gas breakthrough. However, a key finding was the inverse relationship observed between permeability and viscosity reduction: the lower-permeability sample (13.4 md) consistently exhibited a greater percentage of viscosity reduction across all injection pressures than the higher-permeability sample (28 md). This unexpected trend is aligned with the inverse relationship between the permeability and the recovery factor after the gas breakthrough. This outcome suggests that enhanced CO₂ solubility, driven by higher confinement pressures within the nanopores of the lower-permeability rock, promotes a localized, near-miscible state. This effect was even evident during immiscible injection, where the low-permeability sample showed a noticeable viscosity reduction and superior long-term production. These findings highlight the critical role of pore-scale confinement in governing CO₂ miscibility and its associated viscosity reduction, which should be incorporated into enhanced oil recovery design for unconventional reservoirs. 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 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

The emergence of new hard and extra-hard materials has led to the development of new technologies capable of processing them, known as unconventional technologies. Electrical discharge machining (EDM) is a very common unconventional technology in the manufacturing industry, used to process special materials. The primary benefit is the ability to machine various complex shapes at a reduced cost. This study addressed the use of intensive machining regimes that would enhance productivity while also maintaining a high quality of the resulting surface. The experimental setup was designed according to a D-optimal response surface method, and the results were statistically processed using ANOVA. The results revealed that it is possible to achieve both high productivity and good surface quality, but it was also found that increasing the processing parameters is feasible only to a certain extent. 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

Acoustic metamaterials are artificially engineered materials composed of subwavelength structural units, whose effective acoustic properties are primarily determined by structural design rather than intrinsic material composition. By introducing local resonances, these materials can exhibit unconventional acoustic behavior, enabling enhanced sound insulation beyond the limitations of conventional structures. In this study, a thin plate (thin sheet) refers to a structural element whose thickness is much smaller than its in-plane dimensions and can be accurately described using classical thin-plate vibration theory. When resonant mass blocks are attached to a thin plate, a thin-plate acoustic metamaterial is formed through the coupling between plate bending vibrations and local resonances. Thin-plate acoustic metamaterials exhibit excellent sound insulation performance in the low- and mid-frequency ranges. Multilayer configurations and the combination with porous materials can effectively broaden the insulation bandwidth and improve overall performance. However, the large number of structural parameters in multilayer composite thin-plate acoustic metamaterials significantly increases design complexity, making conventional trial-and-error approaches inefficient. To address this challenge, a neural-network-based inverse design framework is proposed for multilayer composite thin-plate acoustic metamaterials. An analytical model of thin-plate metamaterials with multiple attached cylindrical masses is established using the point matching and modal superposition methods and validated by finite element simulations. A multilayer composite unit cell is then constructed, and a dataset of 30,000 samples is generated through numerical simulations. Based on this dataset, a forward prediction network achieves a test error of 1.06%, while the inverse design network converges to an error of 2.27%. The inverse-designed structure is finally validated through impedance tube experiments. The objective of this study is to establish a systematic theoretical and neural-network-assisted inverse design framework for multilayer thin-plate acoustic metamaterials. The main novelties include the development of an accurate analytical model for thin-plate metamaterials with multiple attached masses, the construction of a large-scale simulation dataset, and the proposal of a neural-network-assisted inverse design strategy to address non-uniqueness in inverse design. The proposed approach provides an efficient and practical solution for low-frequency sound insulation design. 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

The transition to a circular economy and the pursuit of environmental sustainability are driving humanity to develop alternative technologies for producing a range of bioproducts. In this context, microbial-mediated fermentation processes have gained prominence. Although yeasts are well known for their ability to produce alcohols, they can also generate a wide range of value-added bioproducts. At the same time, microalgae emerge as an advantageous unconventional raw material, as their cultivation does not require arable land, thus avoiding competition with food production. To meet this demand, this study aimed to produce biocomposites through submerged fermentation using biomass from the microalgae Chlorella sp. Enzymatic hydrolysis was optimized using a 2² Central Composite Rotational Design (CCRD), with algal biomass and enzyme mass as independent variables. This step was followed by fermentation with the yeast Wickerhamomyces sp. UFFS-CE-3.1.2. The enzyme alpha amylase employed is of commercial origin, commonly used in the brewing industry, characterized by its easy accessibility and lower environmental impact compared to chemical hydrolysis methods. The results demonstrated that the combination of microalgae biomass with the enzyme preparation led to the production of several compounds of interest, such as highly active enzymes, mainly protease (560 U/mL), catalase (3381 U/mL), and peroxidase (277 U/mL), as well as other compounds, such as glycerol (32.5 g/L) and acetic acid (22.8 g/L). These products have wide industrial applications and a strong market demand, reinforcing the potential of the yeast–microalgae synergy for the sustainable production of high-value biocompounds, which represents a matrix of environmentally friendly products. Full article

Background: Seronegative rheumatoid arthritis (SNRA), defined by the absence of rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPA), represents 20–30% of rheumatoid arthritis cases. Once considered a milder phenotype, SNRA is now recognised as a heterogeneous entity in which a substantial subset of patients develops structural progression comparable to seropositive RA. The binary RF/ACPA-based definition is increasingly viewed as insufficient, as the broader anti-modified protein antibody (AMPA) family—including antibodies against carbamylated, acetylated and malondialdehyde–acetaldehyde–modified proteins—indicates that many “seronegative” patients may harbour unconventional humoral autoimmunity undetected by standard assays. Objectives: To synthesise contemporary insights into the epidemiology, immunopathology, diagnostic challenges and therapeutic management of SNRA, with emphasis on erosive versus non-erosive phenotypes and the implications of the AMPA paradigm. Methods: A comprehensive literature search of PubMed, Cochrane Library and Google Scholar identified randomised trials, observational cohorts and systematic reviews, with focus on studies published within the past decade. Results: SNRA displays partially distinct immune features, including lower formation of tertiary lymphoid structures and variable activation of innate inflammatory circuits. However, the traditional adaptive–versus–innate dichotomy is overly reductionist. Growing evidence suggests that unconventional humoral responses directed against non-classical post-translational modifications may be present in a proportion of RF/ACPA-negative patients. Additional qualitative dimensions—such as IgA isotypes and fine-specificity profiles—represent further heterogeneity with potential prognostic significance. Although ACPA remains the strongest predictor of erosive progression, up to one-third of seronegative patients develop erosions within five years. The 2010 ACR/EULAR criteria may delay diagnosis in SNRA. Cytokine inhibitors and JAK inhibitors show largely serostatus-independent efficacy, whereas B-cell and T-cell–targeted therapies demonstrate attenuated responses in SNRA. Conclusions: SNRA is clinically and immunologically diverse. Integrating the AMPA framework is essential for refining classification and prognostication. Distinguishing erosive from non-erosive forms may guide treatment, while future work should prioritise biomarkers predicting progression and therapeutic response. Full article

Imaging logging serves as a critical technology for identifying and characterizing fractures in unconventional oil and gas reservoirs. Despite significant progress in deep learning for automated fracture recognition in this field, the integration of fracture interpretation with large language models remains insufficient. To address this, this paper constructs a Chinese fracture image–text pair dataset covering multiple scenarios and proposes “FracLogGPT”, a three-stage multimodal large language model with a parameter scale of approximately 7 billion. Using Qwen2.5-VL-7B as the baseline model, this study employs Domain-Adaptive pre-training (DAPT) to tailor the model to geological and logging contexts. Efficient Supervised Fine-Tuning (SFT) is achieved via the LoRA method, while output style alignment is accomplished through Direct Preference Optimization (DPO) combined with expert preference data. Experimental results on an independent test set show that FracLogGPT achieves a Count-F1 of 0.70 for fracture-count classification, with location and morphology consistency accuracies of 0.49 and 0.43, respectively, and higher text-level BLEU and ROUGE-L scores than larger, non-domain-adapted external models evaluated under the same conditions. Comparative experiments across stages validate the effectiveness of the proposed workflow. In summary, “FracLogGPT” achieves automated identification and expert-like description of imaging logging fractures with approximately 7 billion parameters, providing a reusable training pathway and evaluation workflow for intelligent imaging logging interpretation. Full article

The irreducible water saturation of reservoirs seriously restricts the efficient drainage of unconventional energy sources. NMR logging can be used to determine parameters such as total porosity, effective porosity, irreducible water saturation, and permeability, which play an important role in oil and gas identification. T_{2 cut off} value identification using the NMR T₂ spectrum is the key to clarifying the irreducible water saturation of unconventional reservoirs. In this paper, saturation and centrifugal T₂ spectra of sandstone and coal samples are used to study and calculate the T_{2 cut off} value, with methods including single fractal dimension, multi-fractal dimension, and spectrum morphological discrimination; in addition, the applicability of these three methods in characterizing T_{2 cut off} is discussed. According to the morphological difference of the saturated T₂ spectrum, relationships between morphological parameters and the T_{2 cut off} of four types of sample are described. The parameters related to T_{2 cut off} can be divided into two types: (1) the first type includes morphological parameters main peak position (T_M) and smaller-pore volume percentage (SPVP); with an increase of T_{2 cut off}, T_M increases linearly and SPVP decreases exponentially, and the correlation between SPVP and T_{2 cut off} is stronger than that of T_M. (2) The other type includes fractal parameters D₂ (fractal dimension of larger pore), D₋₁₀ – D₁₀, and D₋₁₀/D₁₀; with the increase of T_{2 cut off}, single and multi-fractal dimensions all increase linearly, and the correlation between D₂ and T_{2 cut off} is stronger than that of the multi-fractal dimension. When calculating the T_{2 cut off} of samples with macro-pores developed, spectrum morphological methods should be used preferentially, while the fractal dimension discrimination methods need be used for the T_{2 cut off} of samples with developed micro-pores. Then, the T_{2 cut off} value prediction and evaluation system are described. The overall results of this work can provide a theoretical basis for the inversion of bound water content in the original formation. Full article