首页|Data on Machine Learning Discussed by Researchers at Univer- sity of Science and Technology Beijing (Machine Learning- Based Shale Wettability Prediction: Implications for H2, Ch4 and Co2 Geo-storage)
Data on Machine Learning Discussed by Researchers at Univer- sity of Science and Technology Beijing (Machine Learning- Based Shale Wettability Prediction: Implications for H2, Ch4 and Co2 Geo-storage)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Elsevier
2024 FEB 27 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Shale wettability determines shale gas productivities and gas (H2, CH4 and CO2) geo-storage efficiencies. However, shale wettability is a complex parameter which depends on multiple influencing factors, thus very time-consuming and costly to measure experimentally.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), CNPC Innovation Found, China Postdoctoral Science Foundation, University of Science and Technology Beijing, National Natural Science Foundation of China (NSFC), Australian Research Council.
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningUniversity of Science and Technology Beijing
NETL
NSTL
Elsevier
