Search Results (45)

Underground gas storage (UGS) in heterogeneous carbonate reservoirs is crucial for energy security but frequently faces wellbore instability challenges, which traditional static methods struggle to address due to dynamic full life cycle changes. This study systematically analyzes the dynamic evolution of wellbore stress in the Bs8 well (Qianmiqiao carbonate UGS) during drilling, acidizing, and injection-production operations, establishing a quantitative risk assessment model based on the Mohr–Coulomb criterion. Results indicate a significantly higher wellbore instability risk during drilling and initial gas injection stages, primarily manifested as shear failure, with greater severity observed in deeper well sections (e.g., 4277 m) due to higher in situ stresses. During acidizing, while the wellbore acid column pressure can reduce principal stress differences, the process also significantly weakens rock strength (e.g., by approximately 30%), inherently increasing the risk of wellbore instability, though the primary collapse mode remains shallow shear breakout. In the injection-production phase, increasing formation pressure is identified as the dominant factor, shifting the collapse mode from initial shallow shear failure to predominant wide shear collapse, notably at 90°/270° from the maximum horizontal stress direction, thereby significantly expanding the unstable zone. This dynamic assessment method provides crucial theoretical support for full life cycle integrity management and optimizing safe operation strategies for carbonate gas storage wells. Full article

Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) algorithm, an advanced deep reinforcement learning method, was developed to generate faster and more efficient evacuation routes compared to traditional models. The A3C model was tested in various scenarios, including different environmental conditions and numbers of agents, and its performance was compared with the Deep Q-Network (DQN) algorithm. The results showed that A3C achieved evacuations 43.86% faster on average and converged in fewer episodes (100 vs. 250 for DQN). In dynamic environments with moving threats, A3C also outperformed DQN in maintaining agent safety and adapting routes in real time. As the number of agents increased, A3C maintained high levels of efficiency and robustness. These findings demonstrate A3C’s strong potential to enhance evacuation planning through improved speed, adaptability, and scalability. The study concludes by highlighting the practical benefits of applying such models in real-world emergency response systems, including significantly faster evacuation times, real-time adaptability to evolving threats, and enhanced scalability for managing large crowds in high-density environments including airport terminals. The A3C-based model offers a cost-effective alternative to full-scale evacuation drills by enabling virtual scenario testing, supports proactive safety planning through predictive modeling, and contributes to the development of intelligent decision-support tools that improve coordination and reduce response time during emergencies. Full article

The deep and ultra-deep oil and gas resources often have the characteristics of high temperature and high pressure, with complex pressure systems and narrow safety density windows, so risks such as gas invasion and overflow are easy to occur during the drilling. In response to the problems of low management efficiency and large gas kick by traditional gas invasion treatment methods, this paper respectively established and compared three intelligent control models for bottom hole pressure (BHP) based on a PID controller, a fuzzy PID controller, and a fuzzy neural network PID controller based on the non-isothermal gas–liquid–solid three-phase transient flow heat transfer model in the annulus. The results show that compared with the PID controller and the fuzzy PID controller, the fuzzy neural network PID controller can adjust the control parameters adaptively and optimize the control rules in real-time; the efficiency of the fuzzy neural network PID controller to deal with a gas kick is improved by 45%, and the gas kick volume in the process of gas kick is reduced by 63.12%. The principal scientific novelty of this study lies in the integration of a fuzzy neural network PID controller with a non-isothermal three-phase flow model, enabling adaptive and robust bottom hole pressure regulation under complex gas invasion conditions, which is of great significance for reducing drilling risks and ensuring safe and efficient drilling. Full article

This study presents an innovative approach to quantifying uncertainty in Herschel–Bulkley (H-B) fluid flow through rectangular ducts, analyzing four scenarios: uncertain apparent viscosity (Case I), uncertain pressure gradient (Case II), uncertain boundary conditions (Case III) and uncertain apparent viscosity and pressure gradient (Case IV). Using the stochastic finite difference with homogeneous chaos (SFDHC) method, we produce probability density functions (PDFs) of fluid velocity with exceptional computational efficiency (243 times faster), matching the accuracy of Monte Carlo simulation (MCS). Key statistics and maximum velocity PDFs are tabulated and visualized for each case. Mean velocity shows minimal variation in Cases I, III, and IV, but maximum velocity fluctuates significantly in Case I (63.95–187.45% of mean), Case II (50.15–156.68%), and Case IV (63.70–185.53% of mean), vital for duct design and analysis. Examining the effects of different parameters, the SFDHC method’s rapid convergence reveals the fluid behavior index as the primary driver of maximum stochastic velocity, followed by aspect ratio and yield stress. These findings enhance applications in drilling fluid management, biomedical modeling (e.g., blood flow in vascular networks), and industrial processes involving non-Newtonian fluids, such as paints and slurries, providing a robust tool for advancing understanding and managing uncertainty in complex fluid dynamics. Full article

In well drilling operations, interactions between drilling fluid water-based and the well-bore present significant challenges, often escalating project costs and timelines. Particularly, fractures (both induced and natural) and permeable zones at the wellbore can result in substantial mud loss or increased filtration. Addressing these challenges, our research introduces a novel coupled numerical model designed to precisely calculate fluid losses in fractured and permeable zones. For the permeable zone, fundamental variables such as filtration velocity, filtrate concentration variations, permeability reduction, and fluid cake growth are calculated, all based on the law of continuity and convection-dispersion theory. For the fracture zone, the fluid velocity profile is determined using the momentum balance equation and both Newtonian and non-Newtonian rheology. The model was validated against laboratory data and physical models, and adapted for field applications. Our findings emphasize that factors like mud particle size, shear stress, and pressure differential are pivotal. Effectively managing these factors can significantly reduce fluid loss and mitigate formation damage caused by fluid invasion. Furthermore, the understanding gathered from studying mud behavior in both permeable and fractured zones equips drilling personnel with valuable information related to the optimal rheological properties according to field conditions. This knowledge is crucial for optimizing mud formulations and strategies, ultimately aiding in the reduction of non-productive time (NPT) associated with wellbore stability issues. Full article

The instability of wellbore in hard and brittle shale formations is a key bottleneck constraining the safety and efficiency of drilling engineering. Traditional studies focused on drilling fluid density, particle plugging, and chemical inhibition; however, there is a lack of in-depth analysis on the precise control mechanism of wellbore stability by the rheological properties of drilling fluids. Specifically, while traditional methods are limited in addressing mechanical instability in hard brittle shales with pre-existing micro-fractures, rheological control offers a potential solution by influencing pressure transmission within these fractures. To address this research gap, this study aims to reveal the influence of drilling fluid rheological parameters (specifically viscosity and yield point) on the pressure transmission behavior of the micro-fracture network in hard and brittle shale and to clarify the intrinsic mechanism by which rheological properties stabilize the wellbore. Micro-structure analysis confirmed interconnected micro-fractures (0.5–30 μm). A micro-fracture flow model and simulations evaluated viscosity and yield point effects on pressure transmission. A higher viscosity significantly increased the pressure drop (ΔP) near the wellbore, with limited transmission distance effects. The yield point was minimal. The study reveals that optimizing rheology, particularly increasing viscosity, can suppress pore pressure, reduce collapse pressure, and improve stability. The findings support rheological parameter optimization for safer, economical drilling. In terms of rheological parameter optimization design, this study suggests emphasizing the increase in drilling fluid viscosity to effectively manage wellbore stability in hard brittle shale formations. Full article

As the world’s largest open-pit coal producer, China faces severe dust pollution in mining operations. Cabins of mining equipment (electric shovels, haul trucks, drills) exhibit unique micro-environmental contamination due to dual-source pollution (external infiltration and internal secondary dust generation), posing severe health risks to miners. This study focused on electric shovel cabins at the Heidaigou open-pit coal mine to address cabin dust pollution. Through analysis of dust physicochemical properties, a pollution characteristic database was established. Field measurements and statistical methods revealed temporal–spatial variation patterns of dust concentrations, quantifying occupational exposure risks and providing theoretical foundations for dust control. A novel gradient-pressurized air purification system was developed for harsh mining conditions. Key findings include the following. (1) Both coal-shovel and rock-shovel operators were exposed to Level I (mild hazard level), with rock-shovel operators approaching Level II (moderate hazard level). (2) The system reduced respirable dust concentrations from 0.313 mg/m³ to 0.208 mg/m³ (≥33.34% improvement) in coal-shovel cabins and from 0.625 mg/m³ to 0.421 mg/m³ (≥32.64% improvement) in rock-shovel cabins. These findings offer vital guidance for optimizing cabin design, improving dust control, and developing scientific management strategies, thereby effectively protecting miners’ health and ensuring operational safety. Full article

A combination of theoretical analysis, numerical simulation and physical model experiments is used to explore the mechanism of pressure relief and roof blasting effects along the gob-side roadway. The stress and displacement along the gob-side roadway before and after blasting were investigated using discrete unit code (UDEC) software. The results demonstrated that blasting can effectively decrease the peak stress of the coal seam along the gob-side roadway and transfer it to the depth. The maximum displacement of the roof of the gob-side roadway, the coal pillar and the solid coal was reduced from 9.5, 10.8 and 4 cm to 6.5, 2 and 3 cm, respectively, after roof blasting. The experimental results showed that the movement of the overburden strata showed obvious regional characteristics after blasting which included the height of the caving zone on the broken side being 3.3 times higher than that observed on the unbroken side, while the height of the fractured zone was 0.52 times higher. The field application of roof blasting was controlled by a drilling method, micro-seismic monitoring and stress monitoring. The results showed good application effects. This research provides valuable insights for managing the stability of gob-side entries. Full article

Coiled tubing (CT) plays a pivotal role in oil and gas well intervention operations due to its advantages, such as flexibility, fast mobilization, safety, low cost, and its wide range of applications, including well intervention, cleaning, stimulation, fluid displacement, cementing, and drilling. However, CT is subject to fatigue and mechanical damage caused by repeated bending cycles, internal pressure, and environmental factors, which can lead to premature failure, high operational costs, and production downtime. With the development of CT properties and modes of application, traditional fatigue life prediction methods based on analytical models integrated in the tracking process showed, in some cases, an underestimate or overestimate of the actual fatigue life of CT, particularly when complex factors like welding type, corrosive environment, and high-pressure variation are involved. This study addresses this limitation by introducing a comprehensive machine learning-based approach to improve the accuracy of CT fatigue life prediction, using a dataset derived from both lab-scale and full-scale fatigue tests. We incorporated the impact of different parameters such as CT grades, wall thickness, CT diameter, internal pressure, and welding types. By using advanced machine learning techniques such as artificial neural networks (ANNs) and Gradient Boosting Regressor, we obtained a more precise estimation of the number of cycles to failure than traditional models. The results from our machine learning analysis demonstrated that CatBoost and XGBoost are the most suitable models for fatigue life prediction. These models exhibited high predictive accuracy, with R² values exceeding 0.94 on the test set, alongside relatively low error metrics (MSE, MAE and MAPE), indicating strong generalization capability. The results of this study show the importance of the integration of machine learning for CT fatigue life analysis and demonstrate its capacity to enhance prediction accuracy and reduce uncertainty. A detailed machine learning model is presented, emphasizing the capability to handle complex data and improve prediction under diverse operational conditions. This study contributes to more reliable CT management and safer, more cost-efficient well intervention operations. Full article

The diagnosis of overflow risk has always been an important area of research in drilling operations in the field of oil and gas engineering. In the face of the limitations and lag of traditional overflow diagnosis methods, the practical application effect of existing models and methods is not obvious, and there is no integration of the physical model and the intelligent algorithm model for overflow diagnosis, this paper proposes a method of adaptive weight fusion of physical model and intelligent algorithm model diagnosis results. Based on the fusion of the physical model and the intelligent algorithm model, an overflow diagnosis model of managed pressure drilling is established. The research results show that the fusion model in this paper can combine the accuracy weight of the physical model and the intelligent algorithm model for the intelligent diagnosis of overflow risk, which improves the mechanization and interpretability of the model and diagnosis results while ensuring accuracy and efficiency. And the intelligent algorithm model used in the fusion model is superior to other algorithm models. The overflow diagnosis accuracy of the fusion model on the test set samples reaches more than 98%, and the accuracy of the validation set is 94.25%. The content of this study provides guidance for drilling overflow diagnosis and model fusion methods. Full article

Geothermal energy has emerged as a cornerstone in renewable energy, delivering reliable, low-emission baseload electricity and heating solutions. This review bridges the current knowledge gap by addressing challenges and opportunities for engineers and scientists, especially those transitioning from other professions. It examines deep and shallow geothermal systems and explores the advanced technologies and skills required across various climates and environments. Transferable expertise in drilling, completion, subsurface evaluation, and hydrological assessment is required for geothermal development but must be adapted to meet the demands of high-temperature, high-pressure environments; abrasive rocks; and complex downhole conditions. Emerging technologies like Enhanced Geothermal Systems (EGSs) and closed-loop systems enable sustainable energy extraction from impermeable and dry formations. Shallow systems utilize near-surface thermal gradients, hydrology, and soil conditions for efficient heat pump operations. Sustainable practices, including reinjection, machine learning-driven fracture modeling, and the use of corrosion-resistant alloys, enhance well integrity and long-term performance. Case studies like Utah FORGE and the Geysers in California, US, demonstrate hydraulic stimulation, machine learning, and reservoir management, while Cornell University has advanced integrated hybrid geothermal systems. Government incentives, such as tax credits under the Inflation Reduction Act, and academic initiatives, such as adopting geothermal energy at Cornell and Colorado Mesa Universities, are accelerating geothermal integration. These advancements, combined with transferable expertise, position geothermal energy as a major contributor to the global transition to renewable energy. Full article

In geoenergy science and engineering, well placement optimization is the process of determining optimal well locations and configurations to maximize economic value while considering geological, engineering, economic, and environmental constraints. This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. This study proposes a hybrid workflow for determining the locations of production wells during primary oil recovery using a multi-modal convolutional neural network (M-CNN) integrated with an evolutionary optimization algorithm. The particle swarm optimization algorithm provides the M-CNN with full-physics reservoir simulation results as learning data correlating an arbitrary well location and its cumulative oil production. The M-CNN learns the correlation between near-wellbore spatial properties (e.g., porosity, permeability, pressure, and saturation) and cumulative oil production as inputs and output, respectively. The learned M-CNN predicts oil productivity at every candidate well location and selects qualified well placement scenarios. The prediction performance of the M-CNN for hydrocarbon-prolific regions is improved by adding qualified scenarios to the learning data and re-training the M-CNN. This iterative learning scheme enhances the suitability of the proxy for solving the problem of maximizing oil productivity. The validity of the proxy is tested with a benchmark model, UNISIM-I-D, in which four oil production wells are sequentially drilled. The M-CNN approach demonstrates remarkable consistency and alignment with full-physics reservoir simulation results. It achieves prediction accuracy within a 3% relative error margin, while significantly reducing computational costs to just 11.18% of those associated with full-physics reservoir simulations. Moreover, the M-CNN-optimized well placement strategy yields a substantial 47.40% improvement in field cumulative oil production compared to the original configuration. These findings underscore the M-CNN’s effectiveness in sequential well placement optimization, striking an optimal balance between predictive accuracy and computational efficiency. The method’s ability to dramatically reduce processing time while maintaining high accuracy makes it a valuable tool for enhancing oil field productivity and streamlining reservoir management decisions. Full article

With the development of shale gas horizontal well-filling technology, by drilling infill wells between wells, the well spacing is continuously reduced to make shale reservoir reconstruction more effective. However, in shale gas reservoirs in China, the problem of inter-well interference is becoming increasingly serious, which not only affects the production of well groups but also causes wellbore damage. Currently, the process of interference in the production process is unclear. This study addresses the inter-well interference issue in deep shale gas reservoirs. An integrated numerical simulation method combining the Discrete Fracture Network, Finite Element Method, and Finite Difference Method is proposed. A comprehensive reservoir numerical model considering the production process is proposed. According to the actual reservoir model parameters and operation parameters, a multi-factor analysis model under multiple production conditions was established. Cumulative gas production and inter-well interference were analyzed. Finally, a field model was established, and the history matching of formation pressure was carried out. According to the history-matching results, the pressure expansion range in the production stage was analyzed. These research results provide a scientific basis and practical suggestions for the effective management and mitigation of inter-well interference and are expected to play an important role in practical engineering applications. Full article

Drilling fluids are critical in oil and gas well drilling, particularly deep shale gas drilling. In recent years, applying nanoparticles as additives in drilling fluids has received widespread attention to address the various challenges associated with deep shale gas drilling. This study focused on the performance of three nanoparticle-enhanced oil-based drilling fluids (OBDFs), carbon nanotubes (CNTs), silicon dioxide (SiO₂), and aluminums oxide (Al₂O₃) in terms of improving thermal capacity and cooling efficiency. The potential of the nanoparticles to improve the thermal management capability of the drilling fluids was evaluated by measuring specific heat capacity and thermal conductivity. The results showed that CNTs exhibited the most significant improvement, with thermal conductivity increasing by 7.97% and specific heat capacity by 19.38%. The rheological properties and high temperature and high pressure (HTHP) filtration performance of the nanoparticle-enhanced OBDFs were evaluated, demonstrating that CNTs and SiO₂ significantly improved the rheological stability of the drilling fluids and reduced the filtration loss under high temperature conditions. When 3% CNTs were added, the HTHP filtration loss was reduced by 42.86%, exhibiting excellent sealing properties. The cooling effect of different nanoparticles was evaluated by calculating their effects on the bottomhole temperature. The results showed that CNTs performed the best in lowering the bottomhole temperature by 4.53 °C, followed by SiO₂ by 1.47 °C and Al₂O₃ by only 0.88 °C. The results showed that CNTs were the most effective in lowering the bottomhole temperature. These results indicated that nanoparticles as additives to drilling fluids could significantly increase the thermal capacity and cooling efficiency of OBDFs, making them effective additives for high-temperature deep shale gas drilling applications. Full article

The exploration and development of oil and gas resources in deep formations is a key strategic priority for national energy production. However, manual methods for handling gas kicks suffer from low operating accuracy and inefficiency during high-temperature and high-pressure deep well drilling. To address the need for real-time bottomhole pressure prediction and control, an efficient gas–liquid–solid computing model was developed based on the gas slip model and cuttings settling velocity model. By integrating this model with an automatic choke adjustment system, an automatic gas kick attenuation model for deep well drilling was established. Results show that, compared to the driller’s and wait-and-weight methods, the automatic gas kick attenuation method significantly reduces peak choke pressure due to its larger frictional pressure drop and higher cuttings hydrostatic pressure. The automatic attenuation method not only leads to an average reduction of 28.42% in maximum choke/casing pressure but also accelerates gas removal, achieving gas kick attenuation ten times faster than the driller’s method and seven times faster than the wait-and-weight method. The study also investigates the influence of gas solubility, well depth, gas influx volume, formation permeability, and drilling fluid volumetric flow rate on gas kick attenuation characteristics. The findings provide a solid foundation for improving the efficiency of gas kick management in deep well drilling operations. Full article