Results 41 to 50 of about 3,202 (214)

A Novel Lithology Recognition Framework Based on Auxiliary Classification-Guided Denoising Diffusion and Multi-Scale Deep Learning

Modelling
Lithology identification is a key task in petroleum geological exploration and development, essential for evaluating sweet spots and characterizing reservoirs.
Yong Zhang, Chunsen Wan, Jiajie Yang, Juan Zhai   +3 more
doaj   +1 more source

Upper bound limit analysis of coral reef limestone cavern roof stability incorporating a tension‐shear failure mechanism with tensile‐strength cut‐off

Deep Underground Science and Engineering, EarlyView.
For the roof of coral reef limestone caverns, a novel tension‐shear composite failure mechanism was developed. The most critical tensile crack model was identified using a hybrid optimization algorithm, and the stability of the cavern roof was analyzed accordingly.
Dongsheng Xu, Chenxu Li, Chuantan Hou
wiley   +1 more source

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Rock mechanical properties under reconstructed deep in situ thermo–hydro–mechanical conditions: Implications for CO₂‐related injection scenarios

Deep Underground Science and Engineering, EarlyView.
The graphical abstract illustrates a reconstructed in situ thermo‐hydro‐mechanical (THM) framework in which porosity serves as the central variable linking stress, pore pressure, and temperature to evolving mechanical properties of rocks. Under burial conditions, in situ stress, pore pressure, and temperature jointly govern volumetric strain and ...
Mingyuan Lu, Jun Wang, Zixiong Qi, Ruixuan Chen, Hongtao Wang, Hongwei Zhou   +5 more
wiley   +1 more source

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

Deep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang, Guoyan Zhao, Meng Wang, Taraka Pattela   +3 more
wiley   +1 more source

Lithology and minerals identification from well logs for Mishrif Formation in Ratawi oilfield

Iraqi Journal of Chemical and Petroleum Engineering
   Lithology identification plays a crucial role in reservoir characteristics, as it directly influences petrophysical evaluations and informs decisions on permeable zone detection, hydrocarbon reserve estimation, and production optimization. This paper
Farah A. Radhi, Omar Al-Fatlawi, Ali Kadkhodaie   +2 more
doaj   +1 more source

TAMNet: Temporal and adaptive‐frequency network with MixStyle for cross‐region oil and fluid production forecasting

Deep Underground Science and Engineering, EarlyView.
This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang, Leifeng Meng, Qinghu Fan, Ziang Guo, Ting Bao, Pingdong Zhong, Fulin Luo   +6 more
wiley   +1 more source

Logging-data-driven lithology identification in complex reservoirs: an example from the Niuxintuo block of the Liaohe oilfield

Frontiers in Earth Science
For lithologic oil reservoirs, lithology identification plays a significant guiding role in exploration targeting, reservoir evaluation, well network adjustment and optimization, and the establishment of reservoir models.
Zuochun Fan, Zuochun Fan, Changhao Hu, Shu Jiang, Man Li, Ye Cai, Yue Jiang, Yang Li, Mei Tian   +8 more
doaj   +1 more source

Investigation on the failure evolution characteristics of surrounding rock in deep cross‐fault roadway: Based on physical model test and numerical simulation

Deep Underground Science and Engineering, EarlyView.
This study reveals the failure evolution characteristics of deep cross‐fault roadway surrounding rock under excavation support and periodic weighting. Periodic weighting readily induces fault activation, with the spatial distribution of failed rock masses being controlled by the fault strike and dip.
Tiezhu Li, Suping Peng, Jing Zheng, Zhanhua Yang, Qijun Tian   +4 more
wiley   +1 more source

Real-time lithology identification while drilling based on drilling parameters analysis with machine learning

Geomechanics and Geophysics for Geo-Energy and Geo-Resources
Accurate formation lithology information is crucial for addressing post-mining issues. Artificial intelligence is increasingly vital for lithology identification but faces challenges in underground coal mines, especially in accurately interpreting ...
Kun Li, Ting Ren, Ningping Yao, Jon Roberts, Haitao Song, Zhongbei Li, Chunmiao Liang   +6 more
doaj   +1 more source