Research articles
Numerical and experimental studies of the natural mixing behavior between an uncemented paste backfill and dumped waste rock in stopes from laboratory toward field conditions. Part I: Calibration and validation of a numerical model
Yuyu Zhang, Li Li*, Serge Ouellet, Louis-Philippe Gélinas
Deep Resources Engineering. 2026, 3(1): 100232. 
doi.org10.1016j.deepre.2025.100232.pdf
Brief Introduction: This paper develops a validated DEM model to analyze the mechanical behavior of waste rock and its natural mixing with uncemented paste backfill in underground mining stopes, addressing the challenges of scale and particle size effects.
本文开发了一套可靠的、经过验证的DEM模型，用于分析废石的力学特性及其与未胶结的回填材料在地下采空区中的自然混合行为，解决尺寸效应与粒径效应带来的难题。
Keywords: Waste rocks; Paste backfill; Natural mixing behavior; Repose angle; Scalping method; Rolling resistance coefficient; Numerical modeling; Reliability
Cite: Zhang, Y.Y.; Li, L.; Ouellet, S.; Gélinas, L.-P., Numerical and experimental studies of the natural mixing behavior between an uncemented paste backfill and dumped waste rock in stopes from laboratory toward field conditions. Part I: Calibration and validation of a numerical model, Deep Resources Engineering 2026, 3 (1), 100232. https://doi.org/10.1016/j.deepre.2025.100232
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Multidimensional thorough perception system for surrounding rock disasters in underground engineering and its application
Haoyu Mao, Nuwen Xu*, Peiwei Xiao, Xiang Zhou, Xinchao Ding, Biao Li
Deep Resources Engineering. 2026, 3(1): 100202. doi.org10.1016j.deepre.2025.100202.pdf
Brief Introduction: This paper presents and validates a “point-line-plane-body” multidimensional monitoring–early-warning system—integrating conventional gauges, TGS 360 Pro, seismic-wave CT and microseismic networks—that successfully predicted and prevented a surge-chamber collapse at the Jinchuan hydropower station.
本文提出一种集常规测量仪、TGS 360 Pro、地震波CT和微震台网为一体的“点-线-面-体”多维监测预警系统，并在现场进行了验证，成功预测和预防了金川水电站的调压室坍塌。
Keywords: Underground engineering; Advanced geological prediction; MS monitoring; Multidimensional thorough perception system; Precursory information
Cite: Mao, H.Y.; Xu, N.W.; Xiao, P.W.; Zhou, X.; Ding, X.C.; Li, B., Multidimensional thorough perception system for surrounding rock disasters in underground engineering and its application, Deep Resources Engineering 2026, 3 (1), 100202. https://doi.org/10.1016/j.deepre.2025.100202
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An innovative approach for oil well bottomhole pressure forecasting using Kolmogorov-Arnold Neural Networks (KANs): A case study in an offshore oilfield
Adeboye Adeyinka*, Opedamola Oriola, Olusegun Stanley Tomomewo*
Deep Resources Engineering. 2026, 3(1): 100233. doi.org10.1016j.deepre.2025.100233.pdf
Brief Introduction: This paper presents an innovative Kolmogorov-Arnold Network (KAN) model that significantly enhances the accuracy of bottomhole pressure predictions by utilizing only surface-measured data and engineered features, outperforming both traditional methods and various other machine learning algorithms.
本文提出了一种创新的Kolmogorov-Arnold网络（KAN）模型，在仅利用地面数据和工程特征的情况下，显著提高了井底压力预测的准确性，优于传统方法和其他多种机器学习算法。
Keywords: Time-series forecasting; Kolmogorov-Arnold networks; Interpretable machine learning; Bottomhole pressure prediction; Reservoir engineering; Numerical reservoir simulation
Cite: Adeyinka, A.; Oriola, O.; Tomomewo, O.S., An innovative approach for oil well bottomhole pressure forecasting using Kolmogorov-Arnold Neural Networks (KANs): A case study in an offshore oilfield, Deep Resources Engineering 2026, 3 (1), 100233. https://doi.org/10.1016/j.deepre.2025.100233
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Infrared thermal image detection method of stressed sandstone fracture based on deep learning
Hai Sun*, Xinyi Hou, Liqiang Ma, Wenshuang Gao, Kun Wang
Deep Resources Engineering. 2026, 3(1): 100207. doi.org10.1016j.deepre.2025.100207.pdf
Brief Introduction: This paper develops an SSD-CBAM based deep-learning model that automatically detects sandstone cracks in successive-difference infrared thermal images with 90.2% accuracy, providing a non-contact tool for early warning of rock-mass instability.
本文开发了一种基于SSD-CBAM的深度学习模型，该模型能够自动检测受压状态下砂岩的逐差分红外热图像中的裂缝，准确率达到90.2%，为岩体不稳定的早期预警提供了一种非接触式方法。
Keywords: Rock mass; Crack; Infrared thermal imaging; Convolutional attention mechanism; Deep learning
Cite: Sun, H.; Hou, X.Y.; Ma, L.Q.; Gao, W.S.; Wang, K., Infrared thermal image detection method of stressed sandstone fracture based on deep learning, Deep Resources Engineering 2026, 3 (1), 100207. https://doi.org/10.1016/j.deepre.2025.100207
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Improving deconvolution reliability in well test analysis with an automated objective function
Mina S. Khalaf*
Deep Resources Engineering. 2026, 3(1): 100231. doi.org10.1016j.deepre.2025.100231.pdf
Brief Introduction: This paper introduces a New Objective Function (NOF) for well test deconvolution that simplifies parameter tuning, automates error weighting, and outperforms traditional methods in both synthetic and field cases.
本文提出了一种新的抗噪声目标函数（NOF）用于油井测试反演，简化了参数调整过程并实现了误差权重的自动化处理，在模拟和现场案例中均优于传统方法。
Keywords: Pressure transient analysis; Deconvolution; Objective function optimization; Reservoir characterization; Measurements uncertainty
Cite: Khalaf, M.S., Improving deconvolution reliability in well test analysis with an automated objective function, Deep Resources Engineering 2026, 3 (1), 100231. https://doi.org/10.1016/j.deepre.2025.100231
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Review article
Casing damage in oil and water wells: Causes and prevention
Jiang-Feng Liu, Xin-Yue Zhang*, Li-Yuan Yu*, Xiao-Liang Wang, Yun-Hu Lu, Zi-Hao Zhang, Zhi-Jie Jian
Deep Resources Engineering. 2026, 3(1): 100200. doi.org10.1016j.deepre.2025.100200.pdf
Brief Introduction: This paper systematically analyzes the causes, types, and preventive measures of casing damage in oil and water wells across whole oil exploration stages, and proposes advanced diagnostic and mitigation strategies.
本文系统分析了油气田开发各阶段油水井套管损坏的原因、类型及预防措施，并提出了创新的诊断与治理策略，以增强井的完整性和油田开发效率。
Keywords: Casing damage in oil and water wells; Causes of casing damage; Preventive measures; Treatment measures
Cite: Liu, J.F.; Zhang, X.Y.; Yu, L.Y.; Wang, X.L.; Lu, Y.H.; Zhang, Z.H.; Jian, Z.J., Casing damage in oil and water wells: Causes and prevention, Deep Resources Engineering2026, 3 (1), 100200. https://doi.org/10.1016/j.deepre.2025.100200
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