Results 61 to 70 of about 4,702 (274)

Lithology Identification of Buried Hill Reservoirs Based on Support Vector Machine

Cejing jishu
As an unconventional reservoir, the potential mountain bedrock reservoir presents significant challenges for lithology identification compared to traditional clastic reservoirs due to its complex bedrock structure, tectonic features, chemical composition
GAO Yongde, WU Jinbo, SUN Dianqiang
doaj   +1 more source

Critical expansion points: Mechanical signs of surrounding rock instability

Deep Underground Science and Engineering, EarlyView.
This study proposes the Rock Bearing‐Expansion Model (RockBEM) with critical expansion points (CEPs) to quantify post‐peak damage stages in deep‐buried rock masses via plane strain compression test (PSCT). CEP hysteresis&interval ratios reveal bearing performance dynamics and failure severity, advancing mechanistic insights into deep underground rock ...
Jiaqi Wen, Chang Deng, Haijun Wang, Lei Tang, Jihong Yu   +4 more
wiley   +1 more source

Gaborlet‐guided sparse filtering: A novel intelligent method for lithology identification by vibration signals while drilling

Deep Underground Science and Engineering, EarlyView.
The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao, Danqi Zhang, Zhenqi Song, Jinrui Wang, Qi Wang, Heqing Liu   +5 more
wiley   +1 more source

Advancing mine pillar design: Evaluating traditional methods and integrating AI for enhanced stability of pillars in the Great Dyke, Zimbabwe

Deep Underground Science and Engineering, EarlyView.
B1 is bord width 1, B2 is bord width 2, L is the pillar length, W is the pillar width, red color and letter A represent the pillars, and white color and number 1 represent excavated areas. Pstress is the average pillar stress; σv is the vertical component of the virgin stress, MPa; and e is the areal extraction ratio. e = B o B o + B P ${\rm{e}}=\frac{{
Tawanda Zvarivadza, Hendrik Grobler, Peter Apata Olubambi, Moshood Onifade, Manoj Khandelwal   +4 more
wiley   +1 more source

Evaluation of machine learning methods for lithology classification of sandstone-type uranium deposit based on well logging data in the Songliao Basin, Northeast China

Nuclear Engineering and Technology
Lithology identification plays a crucial role in uranium exploration, providing important information about geological conditions, deposit characteristics, and mineralization environments, which is significant for the exploration, development, and ...
Kun Xiao, Changwei Jiao, Changchun Zou, Yaxin Yang, Hongxing Li, Xudong Hu, Mengshi Chen, Dening Yin, Pengbo Yang, Rong He   +9 more
doaj   +1 more source

Fracture evolution of a thick soft protection layer and the water inrush mechanism in overburden under longwall mining

Deep Underground Science and Engineering, EarlyView.
Through shear–tensile creep tests and viscoelastic modeling, the fracture evolution of thick soft protective layers is clarified. Results show thickness‐dependent rheological failure modes that govern four types of roof water inrush, providing a mechanism‐based framework for hazard prediction and control. Abstract In the Jurassic coal‐bearing strata of
Mengnan Liu, Wei Qiao, Xianggang Cheng, Xiangsheng Meng, Peichao Feng   +4 more
wiley   +1 more source

Mechanistic insights across curing regimes for enzymatic and biopolymer‐optimized reinforcement in rock masses

Deep Underground Science and Engineering, EarlyView.
This study examines the effects of curing temperature on the mechanical behavior of underground rock masses treated with enzyme‐induced calcite precipitation (EICP) and an innovative biopolymer‐modified EICP (BP‐EICP). Abstract Biocementation is an innovative and sustainable technique for reinforcing weak and weathered rock masses in natural and ...
Mary C. Ngoma, Oladoyin Kolawole
wiley   +1 more source

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

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu, Tao Li, Ruiqi Yuan, Yue Li, Bingni Wu, Bo Liu, Zhengding Deng, Galindo Rubén, Fausto Molina‐Gómez   +8 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