Search Results (48)

Sulfide solid electrolytes (SSEs) are promising for all-solid-state lithium batteries (ASSLBs) due to their high ionic conductivity, mechanical deformability, and interfacial compatibility. However, SSE interfaces with anodes, cathodes, conductive additives, and current collectors are unstable, triggering safety failures like capacity degradation, internal resistance build-up, thermal runaway, and short circuits. This review summarizes recent progress on interface-induced safety failure mechanisms in sulfide-based ASSLBs, focusing on interface types, failure mechanisms, and thermal/mechanical degradation under multi-field coupling. We survey interface modification strategies and highlight advanced characterization techniques for probing interfacial phenomena. Key challenges and future research directions are discussed. Integrating recent findings, we identify interfacial instability as the primary bottleneck governing safety failures, providing a theoretical and technical framework for rational interface design, performance optimization, and safety enhancement. Throughout this review, we use SSE as the standard abbreviation for sulfide solid electrolytes. Full article

Multistage hydraulic fracturing in horizontal wells induces significant spatial heterogeneity in fracture networks because of the complex interactions between reservoir geological characteristics and fracturing operations. This heterogeneity is difficult to capture using conventional productivity models, even when realistic fracture geometry is considered, resulting in large prediction deviations. To address this issue, this study proposes a physics-data hybrid productivity prediction model optimized by a production-informed loss function, with a tight sandstone gas reservoir in the Ordos Basin used as the case study. By integrating field data with asymmetric-fracture numerical simulations, eight controlling parameters, including formation pressure and reservoir thickness, were identified through weighted sensitivity analysis. On this basis, a refined productivity forecasting model was established by combining the Goose Optimization Algorithm (GOOSE) with long short-term memory (LSTM). Four physics-data hybrid models were developed based on the GOOSE-LSTM framework. Among them, the loss-function-optimized model exhibited the best performance, achieving an R² of 0.953 and a prediction error below 0.05. The proposed methodology provides a refined decision-making tool for fracturing scheme optimization and development plan formulation, thereby improving productivity forecasting accuracy and supporting the efficient development of target horizontal-well intervals. Full article

Gas kick events in drilling operations are characterized by strong coupling dynamics, subtle early-stage evolution, and severe class imbalance, which limit the effectiveness of conventional feature-independent monitoring methods. To address these challenges, this paper proposes a structure-aware intelligent monitoring framework for early gas kick detection. First, multivariate drilling parameters are modeled as an interacting graph, and a graph neural network (GNN) is introduced to capture relational dependencies and anomaly propagation behaviors at the structural level. Second, to mitigate abnormal sample scarcity and enhance temporal discriminability, a representation enhancement strategy integrating conditional tabular generative adversarial networks (CTGAN) and shapelet-based temporal patterns is developed. Finally, a multi-level interpretability mechanism combining graph attention analysis and SHAP attribution is constructed to provide transparent insights into both structural interactions and feature contributions. Experiments conducted on real drilling datasets demonstrate that the proposed GNN baseline achieves the highest accuracy (0.7302) among various machine learning and deep learning models. With representation enhancement, the GNN+CTGAN+Shapelet model further improves accuracy to 0.7507 and F1-score to 0.7347, validating the effectiveness of the enhancement strategy. Interpretability results reveal that the model decisions are primarily driven by flow-rate and standpipe-pressure-related temporal evolution patterns, which are consistent with drilling engineering knowledge. Overall, the proposed framework provides a structurally consistent, robust, and interpretable solution for intelligent gas kick monitoring in modern drilling operations. Full article

To accurately quantify pore pressure uncertainty and associated kick risk, this paper proposes a dual-phase pre-drilling risk assessment framework based on Bayesian Long Short-Term Memory (BLSTM) networks, integrating formation pressure prediction with distribution interference analysis. First, the effects of two Bayesian layer optimization methods—Monte Carlo dropout and Bayes-by-Backprop—on deep learning networks were systematically evaluated. The optimized Bayes-by-Backprop-LSTM model was subsequently selected for uncertainty prediction of formation pore pressure. Finally, kick risk was quantified by analyzing the interference between predicted pressure distributions and the safety margin of designed drilling mud density. The BLSTM models uncertainty regression between well-log parameters and formation pore pressure labels. Using the Bayes-by-Backprop strategy, it generates probabilistic pressure predictions. By incorporating the designed drilling mud density of target wells, kick risk probability is calculated through distribution interference criteria, where the overlapping area between pore pressure distributions and mud density safety boundaries is mapped to risk probability. Validation experiments utilized five types of well-log parameters from three wells in EAST CHINA. Key results demonstrate: (1) The BLSTM regression model achieved a mean absolute error (MAE) of 0.037 on test wells, representing a 26.7% reduction compared to conventional LSTM, with the 95% confidence interval coverage reaching 69.6%. (2) In the 3893–4048 m interval of a test well, interference areas exceeding thresholds indicated 60% kick risk probability. Spatial correlation with actual kick events revealed risk points undetectable by conventional pore pressure prediction methods. This study establishes a comprehensive risk assessment paradigm encompassing pore pressure uncertainty regression prediction and probabilistic risk calculation, providing drilling engineering with a framework that combines physical interpretability and statistical reliability. Full article

Thermal insulation is necessary for deepwater wells to achieve safe and effective production. Based on the comparison of different thermal insulation measures and the control requirements, this paper proposes two indicators to analyze thermal insulation performance. A model is established by considering the wellbore radial thermal resistance and wellbore-formation heat transfer process in order to calculate the two indicators. The analysis shows that there exists an overlapping effective range between vacuum-insulated tubing and insulation-coated tubing, and a similar overlap is observed between insulating liquid and insulated tubing. When comparable insulation performance can be achieved, insulating liquid should be prioritized, while vacuum-insulated tubing should be considered only as the final option. Under high production or a high geothermal gradient, annular temperature change is the primary control objective, whereas under low-production or low-temperature conditions, wellhead temperature becomes the dominant control target. The combination of insulated tubing and insulating liquid exhibits pronounced synergistic effects. In the case of a well under high-temperature and high-production conditions, the composite insulation reduces annular temperature change by 64.26%, and in low-temperature, low-production wells, it increases wellhead temperature by 100.43%. In practical applications, insulating fluids should be preferred, with insulated tubing employed as a supplementary measure. Full article

Conventional drilling parameter optimization, heavily reliant on lagging lithology data from periodic mud logging, suffers from significant delays between formation change detection and parameter adjustment. This latency often leads to reduced Rate of Penetration (ROP), accelerated tool wear, and increased risk of drilling complications. To address this, this work introduces a closed-loop machine learning framework for real-time lithology identification and autonomous parameter optimization. Its core is a hybrid deep learning model (1D-CNN-LSTM) that establishes a direct mapping from surface drilling parameters, Weight on Bit (WOB), Rotary Speed (RPM), Torque, ROP, to formation lithology, deliberately excluding dependency on expensive Logging-While-Drilling (LWD) tools to ensure cost-effective and broad applicability. Upon lithology change detection, the system retrieves the historically optimal Mechanical Specific Energy (MSE) value for the identified rock type and solves an inverse MSE model to compute optimal WOB and RPM setpoints within operational constraints. Field validation in a comparative trial demonstrated the framework’s efficacy: the test well achieved a 17.4% increase in ROP, a 37.8% reduction in Non-Productive Time, and an 87.5% decrease in stuck pipe incidents compared to an offset well drilled conventionally. Full article

To address the challenge of low separation efficiency for fine light hydrocarbons in tight gas fields, this study establishes a mathematical model correlating the structural parameters of a cyclonic coalescer with coalesced droplet size. The model was constructed using second-order polynomial basis functions through numerical simulation and response surface methodology. An optimized cyclonic coalescer configuration with enhanced fine droplet coalescence capability was subsequently designed. The performance of the optimized and original configurations was comparatively evaluated through numerical simulations and laboratory experiments. Simulation results indicated that with inlet droplet sizes ranging from 0.1 to 10 µm, the optimized configuration achieved a coalescence efficiency of 90.66% for outlet droplets larger than 100 µm. High-speed photographic analysis revealed that 5–10 µm inlet droplets were coalesced to 50–60 µm diameters, while 50–300 µm inlet droplets formed large-scale liquid flows of 300–500 µm. The optimized configuration exhibited significantly improved coalescence efficiency and operational applicability across varying inlet droplet sizes. This research provides practical insights for enhancing the recovery efficiency of fine light hydrocarbons in gas processing operations. Full article

Polycrystalline Diamond Compacts (PDC) face thermal damage and insufficient wear resistance in complex strata due to the high thermal expansion coefficient of Co binder and its catalysis on diamond graphitization. Existing studies lack a systematic comparison of HfB₂, ZrB₂, and Al-Ni (1.5wt.%Al + 1.5wt.%Ni) on PDC performance under a unified process, and their synergistic mechanism with the PDC matrix remains unclear. Herein, 3wt.% of these additives were incorporated into diamond micropowder to prepare PDC via unified high-temperature and high-pressure (HTHP) sintering. XRD/SEM-EDS characterized the phase/microstructure, while thermal expansion and Vertical Turret Lathe (VTL) tests evaluated their properties. Results: (1) ZrB₂-modified PDC performed the best, with a thermal failure temperature of 800 °C (8.5% higher than the blank group), VTL wear cycles of 110 Pass (22.2% higher), and ZrC (confirmed by XRD) enhancing interface bonding; (2) HfB₂-modified PDC reduced the wear area by 18% (vs. the blank group) via low-expansion HfC (6.5 × 10⁻⁶/°C) and maintained a continuous structure; (3) Al-Ni-modified PDC had a wear ratio of 1.945 × 10⁴ (4.5% higher) but only 60 Pass and structural defects. This study confirms ZrB₂ as the optimal additive for PDC’s comprehensive properties, supporting high-performance PDC development for complex downhole environments. Full article

Data-driven modeling methods have been preliminarily applied in the development of tight-gas reservoirs, demonstrating unique advantages in post-fracturing productivity prediction. However, most of the established predictive models are “black-box” models, which provide productivity predictions based on a set of input parameters without revealing the internal prediction mechanisms. This lack of transparency reduces the credibility and practical utility of such models. To address the challenges of poor performance and low trustworthiness of “black-box” machine learning models, this study explores a data-driven approach to “black-box” predictive modeling by integrating ensemble learning with interpretability methods. The results indicate the following: The post-fracturing productivity prediction model for tight-gas reservoirs developed in this study, based on ensemble learning, achieves a goodness of fit of 0.923, representing a 26.09% improvement compared to the best-performing individual machine learning model. The stacking ensemble model predicts post-fracturing productivity for horizontal wells more accurately and effectively mitigates the prediction biases of individual machine learning models. An interpretability method for the “black-box” ensemble learning-based productivity prediction model was established, revealing the ranked importance of factors influencing post-fracturing productivity: reservoir properties, controllable operational parameters, and rock mechanics. This ranking aligns with the results of orthogonal experiments from mechanism-driven numerical models, providing mutual validation and enhancing the credibility of the ensemble learning-based productivity prediction model. In conclusion, this study integrates mechanistic numerical models and data-driven models to explore the influence of various factors on post-fracturing productivity. The cross-validation of results from both approaches underscores the reliability of the findings, offering theoretical and methodological support for the design of fracturing schemes and the iterative advancement of fracturing technologies in tight-gas reservoirs. Full article

To investigate hydraulic fracture propagation in multi-layered porous media such as sand–coal interbedded formations, we present a new phase-field-based model. In this formulation, a diffuse fracture is activated only when the local element strain exceeds the rock’s critical strain, and the fracture width is represented by orthogonal components in the x and y directions. Unlike common PFM approaches that map the permeability directly from the damage field, our scheme triggers fractures only beyond a critical strain. It then builds anisotropy via a width-to-element-size weighting with parallel mixing along and series mixing across the fracture. At the element scale, the permeability is constructed as a weighted sum of the initial rock permeability and the fracture permeability, with the weighting coefficients defined as functions of the local width and the element size. Using this model, we examined how the in situ stress contrast, interface strength, Young’s modulus, Poisson’s ratio, and injection rate influence the hydraulic fracture growth in sand–coal interbedded formations. The results indicate that a larger stress contrast, stronger interfaces, a greater stiffness, and higher injection rates increase the likelihood that a hydraulic fracture will cross the interface and penetrate the barrier layer. When propagation is constrained to the interface, the width within the interface segment is markedly smaller than that within the coal-seam segment, and interface-guided growth elevates the fluid pressure inside the fracture. Full article

To improve fracturing support efficiency of terrestrial shale oil reservoirs with uneven proppant placement, this study used complex mesh flat-plate simulations and ANSYS FLUENT (2020) simulations to test four sand addition processes. Proppants were 70/140 mesh quartz sand with a density of 2650 kg/m³ and 40/70 mesh ceramic particles with a density of 2000 kg/m³, and the carrier was hydroxypropyl guar gum fracturing fluid with a viscosity of 4.46–13.4 mPa·s at 25 °C. Alternating sand addition performed best: sand-laying efficiency reached 52 percent, 10 percentage points higher than continuous sand addition and 12 percentage points higher than mixed sand addition; sand embankment void area was 1400 cm², 18.3 percent lower than continuous sand addition; proppant entry into secondary cracks increased 23.8 percent compared with reverse sand addition; at branch crack Position 2, 1.3 m from the inlet and at a 90-degree angle, its equilibrium height was 210 mm and paving rate 0.131. This study fills gaps of no systematic multi-process comparison and insufficient quantification of crack geometry–sand parameter coupling in existing research; its novelty lies in the unified visualization comparison of four processes, revealing geometry–parameter coupling and integrating experiment simulation; the optimal scheme also improves fracture support efficiency 21.5 percent compared with conventional continuous sand addition. Full article

Deep carbonate reservoirs have garnered significant attention and demonstrated great potential for oil and gas exploration in recent years. The Majiagou Formation in the Ordos Basin has received much attention for its deep oil and gas deposits recently. However, the issue of fluid evolution within the great depth has been overlooked, and the relationship between fluid flow and the gas accumulation process remains unclear. This paper aims to explore the fluid evolution and its relationship with the gas accumulation, which poses a challenge for further petroleum exploration. To achieve this, petrological studies on dolomite samples were carried out and four types of secondary cements were identified: early gypsum-moldic pore-filling calcite, late gypsum-moldic pore-filling calcite, dissolution pore-filling calcite and fracture-filling calcite. Subsequently, an interdisciplinary approach that integrates petrography observation, microthermometry, laser Raman analysis of fluid inclusions, and carbon and oxygen isotope tests on these types of cements is employed to elucidate the fluid flow evolution. These investigations revealed that four different stages of inorganic fluid activity were coeval with two stages of organic fluid activity. The two stages of organic fluid flows were significantly important for petroleum accumulation. In the late Triassic to early Jurassic, there was small-scale liquid oil accumulation, which was associated with the second stage of fluids. In the early Cretaceous, there was large-scale gas accumulation, which was associated with the fourth stage of fluids. This research is crucial for understanding the fluid flow process and its relationship with hydrocarbon accumulation in deeply buried carbonate formations. Full article

Natural polymer materials are increasingly utilized in drilling fluid additives. Starch has come to be applied extensively due to its low cost and favorable fluid loss reduction properties. However, its poor temperature resistance and high viscosity limit its application in high-temperature wells. This study innovatively introduces for the first time diatomite as an inorganic material in the modification process of starch-based fluid loss additives. Through synergistic modification with acrylamide and acrylic acid, we successfully resolved the longstanding challenge of balancing temperature resistance with viscosity control in existing modification methods. The newly developed fluid loss additive demonstrates remarkable performance: It remains effective at 160 °C when used independently. When added to a 4% sodium bentonite base mud, it achieves an 80% fluid loss reduction rate—significantly higher than the 18.95% observed in conventional starch-based products. The resultant filter cake exhibits thin and compact characteristics. Moreover, this additive shows superior contamination resistance, tolerating 30% NaCl and 0.6% calcium contamination, outperforming other starch-based treatments. With starch content exceeding 75%, the product not only demonstrates enhanced performance but also achieves significant cost reduction compared to conventional starch products (typically containing < 50% starch content). Full article

To address the technical challenge of high polymer gel viscosity reducers losing viscosity at elevated temperatures and difficulty in controlling fluid loss, a polymer-based nano calcium carbonate composite high-temperature tackifier named GW-VIS was prepared using acrylamide (AM), 2-acrylamido-2-methylpropanesulfonic acid (AMPS), N-vinylpyrrolidone (NVP), and nano calcium carbonate as raw materials through water suspension polymerization. This polymer gel can absorb water well at room temperature and has a small solubility. After a long period of high-temperature treatment, most of it can dissolve in water, increasing the viscosity of the suspension. The structure of the samples was characterized by infrared spectroscopy, thermogravimetric analysis, and scanning electron microscopy, and their performance was evaluated. Rheological tests indicated that the 0.5% water suspension had a consistency coefficient (k = 761) significantly higher than the requirement for clay-free drilling fluids (k > 200). In thermal resistance experiments, the material maintained stable viscosity at 180 °C (reduction rate of 0%), and only decreased by 14.8% at 200 °C. Salt tolerance tests found that the viscosity reduction after hot rolling at 200 °C was only 17.31% when the NaCl concentration reached saturation. Field trials in three wells in the Liaohe oilfield verified that the clay-free drilling fluid supported formation operations successfully. The study shows that the polymer gel has the potential to maintain rheological stability at high temperatures by forming a network structure through polymer chain adsorption and entanglement, with a maximum temperature resistance of up to 200 °C, providing an efficient drilling fluid for deep oil and gas well development. It is feasible to select nano calcium carbonate to participate in the research of high-temperature resistant polymer materials. Meanwhile, the combined effect of monomers with large steric hindrance and inorganic materials can enhance the product’s temperature resistance and resistance to NaCl pollution. Full article

Reservoir evaluation in multi-source systems is challenging because studies generally follow single-source principles. This limitation has substantially hindered the understanding of reservoir and hydrocarbon accumulation processes in source–reservoir systems. This study examines the Dingbian area of the Ordos Basin, China, and investigates the key factors controlling hydrocarbon accumulation in the Chang 9 reservoir of the Triassic Yanchang Formation within a multi-source system. The study area spans approximately 0.9 × 10⁴ km². First, by comparing the biological markers in Chang 9 crude oil with those of potential source rocks, the oil source of the Chang 9 reservoir was identified. The study area was subsequently divided into three provenance zones—northeast, northwest, and central mixed source areas—based on heavy mineral content and the orientation of sedimentary sand bodies. Additionally, well logging data, oil production data, petrographic thin sections, scanning electron microscopy (SEM), and mercury injection porosimetry were used to investigate the reservoir characteristics, oil reservoir features, and crude oil properties across different source areas. The results indicate that the oil source of the Chang 9 reservoir in the Dingbian area is the Upper Chang 7 source rock. The northwest source area exhibits superior reservoir properties compared to the other two zones. In the northwest source area, lithology-structure oil reservoirs are predominant, whereas the central mixed source area is characterized by structural-lithology oil reservoirs, and the northeast source area predominantly features lithology-controlled reservoirs. From the northwest to the central mixed source areas, and finally to the northeast source area, crude oil density and viscosity increase gradually, while the degree of oil–water separation decreases correspondingly. Based on these findings, the study concludes that the distribution of structures, lithology, and source rocks significantly influences the Chang 9 reservoirs in the Dingbian area. The controlling factors of oil reservoirs differ across the various source zones. In multi-source systems, evaluating oil reservoirs based on source zones provides more precise insights into the characteristics of reservoirs in each area. This approach provides more accurate guidance for exploration and development in multi-source regions, as well as for subsequent “reserve enhancement and production increase” strategies. Full article