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Full-Text Articles in Physical Sciences and Mathematics
An Intelligent Water Source Discrimination Method For Water Inrushes From Coal Seam Roofs In The Inner Mongolia-Shaanxi Border Region, Wang Hao, Sun Junqing, Zeng Yifan, Shang Hongbo, Wang Tiantian, Qiao Wei
An Intelligent Water Source Discrimination Method For Water Inrushes From Coal Seam Roofs In The Inner Mongolia-Shaanxi Border Region, Wang Hao, Sun Junqing, Zeng Yifan, Shang Hongbo, Wang Tiantian, Qiao Wei
Coal Geology & Exploration
Water hazard on the coal seam proof induced by high-intensity coal mining are increasingly prominent in the Inner Mongolia-Shaanxi border region. The effective, accurate water-source discrimination of the water inrushes is the key to water hazard prevention. This study investigated three typical mines in the Inner Mongolia-Shaanxi border region. To this end, principal component analysis (PCA) was employed to extract principal components from 80 groups of groundwater samples. Then, with inorganic indicators K++Na+, Ca2+, Mg2+, Cl−, SO4 2−, HCO3 − and TDS and organic indicators UV254 …
Early Warning And Prediction Of Kicks And Lost Circulation Accident During Rescue Drilling Of Mine, Chen Weiming, Wang Jiawen, Fan Dong, Hao Shijun, Zhao Jiangpeng, Qiu Yu
Early Warning And Prediction Of Kicks And Lost Circulation Accident During Rescue Drilling Of Mine, Chen Weiming, Wang Jiawen, Fan Dong, Hao Shijun, Zhao Jiangpeng, Qiu Yu
Coal Geology & Exploration
In order to solve the problems such as the difficulty in early warning and prediction of kicks and lost circulation accidents during emergency rescue drilling of mine, a machine learning-based early for warning and prediction model of drilling process was established. Firstly, the accident characterization parameters of the drilling parameters in the early stage of kicks and lost circulation accidents were analyzed. Secondly, the accident characterization parameters were cleaned and processed. On this basis, XGBoost and early warning model was used to carry out the early diagnosis and identification of kicks and lost circulation accidents. Then, the PSO-LSTM accident development …
Rate-Of-Penetration (Rop) Prediction Model Based On Formation Characteristics Of Extremely Thick Plastic Mudstone In South China Sea, Zeng Xiaolong, Li Qian, Wei Hongchao, Chen Jiahao, Zhu Haiyan
Rate-Of-Penetration (Rop) Prediction Model Based On Formation Characteristics Of Extremely Thick Plastic Mudstone In South China Sea, Zeng Xiaolong, Li Qian, Wei Hongchao, Chen Jiahao, Zhu Haiyan
Coal Geology & Exploration
In terms of petroleum and gas resources, South China Sea is the important energy replacement area in China. However, most of the reservoirs are buried deep, and the strong plasticity of the formation under high confining pressure and the complex geological environment seriously affect the drilling efficiency. It is also very difficult to accurately predict the ROP. Hence, a set of intelligent ROP prediction model was established for the extremely thick mudstone formation with unique viscoelastic and strong plastic characteristics in South China Sea. The model took the actual data of 10 wells in an area of South China Sea …
A New Physics-Informed Method For The Fracability Evaluation Of Shale Oil Reservoirs, Li Yuwei, Li Zijian, Shao Lifei, Tian Fuchun, Tang Jizhou
A New Physics-Informed Method For The Fracability Evaluation Of Shale Oil Reservoirs, Li Yuwei, Li Zijian, Shao Lifei, Tian Fuchun, Tang Jizhou
Coal Geology & Exploration
The accurate evaluation of reservoir fracability is an essential prerequisite for the fracturing design and post-fracturing productivity evaluation of reservoirs. Rock mechanical parameters have been applied to the fracability evaluation of shales presently, exhibiting great field application performance. Accordingly, it is crucial to obtain accurate rock mechanical parameters. This study developed a physics-informed neural network (PINN) model. Driven by data and physical information, the PINN model can accurately predict rock mechanical parameters using only a small amount of data. To verify its performance, the PINN model was compared with the artificial neural network, random forest, and XGBoost models. The comparison …
Prediction Method And Application Of Gas Emission From Mining Workface Based On Stl-Eemd-Ga-Svr, Lin Haifei, Liu Shihao, Zhou Jie, Xu Peiyun, Shuang Haiqing
Prediction Method And Application Of Gas Emission From Mining Workface Based On Stl-Eemd-Ga-Svr, Lin Haifei, Liu Shihao, Zhou Jie, Xu Peiyun, Shuang Haiqing
Coal Geology & Exploration
Accurate prediction of gas emission can provide important basis for mine ventilation and the prevention and measures of gas disasters. In order to improve the prediction accuracy of gas emission in the mining workface, the monitoring data of gas emission were decomposed into the trend term, periodic term and irregular fluctuation term by the Seasonal-Trend decomposition procedure based on Loess (STL) based on the monitoring data of gas emission from the mining workface of Huangling Mine in Shaanxi. Besides, the irregular fluctuation term was further broken down into the Intrinsic Mode Functions (IMFs) components with different characteristics and the residual …