http://yetl.yabesh.ir/yetl1/handle/yetl/4303706 2026-09-15T03:47:50Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315519 Mechanism of Liquid Nitrogen Freezing–Blasting Synergistic Fracturing for Coal Seam Permeability Enhancement Guo, Wei; Kang, Jianhong; Liang, Zhongqiu; Wang, Tingrong; Si, Sasha; Zhang, Ran; Yang, Chuanheng Abstract. To enhance gas extraction from low-permeability coal seams, this study introduces a synergistic liquid nitrogen (LN2) freezing–blasting method designed to mechanically precondition coal and promote fracture propagation during blasting. Coal specimens with varying moisture contents (MCs) were subjected to controlled LN2 freezing regimes, followed by uniaxial, triaxial, and tensile testing using a coal–rock triaxial creep apparatus. Subsequent blasting experiments under biaxial lateral loading elucidated fracture propagation and coalescence behaviors in frozen coal. The results show that LN2 freezing significantly alters the mechanical behavior of coal and promotes fracture initiation. Under uniaxial loading, specimens frozen for 60 min at 12% MC exhibited the strongest enhancement, with both compressive strength and elastic modulus more than doubling compared with unfrozen dry coal. This strengthening effect became more pronounced under triaxial confinement. In contrast, tensile strength associated with fracture initiation was degraded due to LN2 vaporization-induced thermal shock and frost-heave effects, reaching only 0.65 MPa at 60 min of freezing and 12% MC, a 126% reduction compared with unfrozen dry coal. Subsequent blasting experiments demonstrated that LN2 pretreatment significantly improved fracture development and connectivity, promoting the transfer of blasting energy from the near-field crushed zone to far-field interconnected fractures. Under optimal conditions (60 min of freezing and 12% MC), the brittleness index reached 24.66, surface-penetrating fractures extended up to 108.8 mm, and the crushed zone length was reduced to only 14.7% of that in unfrozen dry coal. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315518 Erosion-Corrosion Effect in the Low-Temperature Heavy Oil Annular Flow Transportation Chen, Yiming; Rui, Zhenhua; Wang, Xiangzeng; Ma, Rui; Cao, Yufeng; Zeng, Fanhua Abstract. According to statistical data, proven heavy oil reserves account for approximately 70% of the world's remaining crude oil resources. As an important alternative energy resource, technologies for the pipeline transportation of high-viscosity heavy oil have developed rapidly. Among the available methods, water-loop transportation, characterized by its low cost and operational simplicity, has emerged as the most promising approach. However, the exceptionally high viscosity of heavy oil intensifies the entrainment of sand particles in the wellbore, significantly reducing sand removal efficiency. Furthermore, erosion of the pipe wall caused by sand particles, combined with the electrochemical corrosion environment created by the water annulus, poses serious threats to pipeline integrity. To investigate the evolution of erosion and corrosion rates during heavy oil–water-loop transportation, an optimized multifactor erosion rate model was developed and coupled with heat transfer and multiphase flow models for numerical simulation. In addition, CO2 corrosion experiments were conducted under actual engineering conditions to account for key influencing factors, including sand particle size, water content, flow velocity, and the thermal effects of heavy oil. The results indicate that erosion in straight pipeline sections during heavy oil annular transportation is negligible, and pipeline damage is primarily caused by CO2-induced corrosion in the aqueous phase. Increases in flow velocity, temperature, and CO2 partial pressure accelerate the corrosion process. Under stable annular flow conditions, erosion is mainly observed in elbows when the water content is low (below 9%), and the lower the water content, the more significant the erosive effect of large particles. When the water content exceeds 9%, no observable erosion occurs in the elbows. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315517 Deep-Learning Prediction of Flowing Bottomhole Pressure in Gas-Lifted Unconventional Wells Jin, Miao; Emami-Meybodi, Hamid Abstract. Accurate prediction of flowing bottomhole pressure (FBHP) is essential for the effective design and optimization of gas-lift systems in unconventional wells. Conventional methods for FBHP estimation often prove inadequate for unconventional wells, either being overly simplistic or computationally expensive. Accordingly, we develop and evaluate several deep learning architectures for predicting FBHP in unconventional shale wells under gas-lift operations. A comprehensive dataset is compiled from 21 oil wells in the Texas Permian Basin Shale, incorporating readily available parameters such as well depth, operating valve depth, and production/injection data. The predictive capabilities of an artificial neural network (ANN), a long short-term memory (LSTM) network, a hybrid LSTM–ANN model, and a transformer model are evaluated. Five of the 21 wells are used to assess the deep learning model performance. The analysis of the results revealed factors that influence the deep learning model's performance. The results show that the transformer model's predictive performance is most reliable across different scenarios and among the four deep learning models, with an error of around 10%. Additionally, selection bias in the training set can significantly affect the model's predictive performance. Furthermore, our analysis reveals that hyperparameter tuning reduces residual errors by refining model parameters, leading to architectures that better capture the patterns in the training data and deliver enhanced predictive accuracy. This work highlights the significant potential of advanced deep learning models as practical tools for optimizing gas-lift operations across a wide range of fluid and reservoir conditions. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4315516 Fracture/Fault Reactivation Determination and Carbon Storage Stability Analysis of CO2 Injection in Shale Reservoirs After Fracturing Wang, Ying; Wu, Ke; Cao, Jiawei; Chen, Siwei; Chen, Zhaowei; Fang, Chao; Liu, Jihan; Zhang, Haozhen; Tan, Peng Abstract. CO2 Geological storage and enhanced oil recovery (CO2-EOR) are key pathways for the low-carbon energy transition. In fractured shale reservoirs, however, injection-induced pore-pressure buildup and stress redistribution may reduce fault stability and threaten storage safety. To quantitatively evaluate fault stability, this study develops a three-dimensional numerical model including injection wells, hydraulic fractures, and high-angle faults, incorporating CO2 adsorption–desorption effects. Fault slip tendency (ST) is adopted as the activation criterion to characterize fault-stability evolution. Sensitivity analyses are conducted for key engineering parameters, including injection rate, cumulative injection volume, injection location, fault–well distance, and fracture half-length. Grey relational analysis is used to identify the main controlling factors. Results demonstrate a pronounced nonlinear response of fault stability to injection parameters. Under the investigated scenarios, the maximum fault slip tendency varies from 0.35 to 0.91, and a warning threshold of ST = 0.8 is used to identify potential fault activation risk. Specifically, increasing the injection rate from 3000 to 12,000 m3/d raises the maximum ST from 0.60 to 0.91, while bottom injection yields a maximum ST of 0.81, higher than top injection (0.52) and middle injection (0.35). Increasing fracture half-length from 90 to 180 m raises the maximum ST from 0.45 to 0.81. Grey relational analysis shows that the relative influence of the investigated parameters is ranked as cumulative injection volume > injection location > fracture half-length > injection rate > fault–well distance. 2026-01-01T00:00:00Z