Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Shuyi DU"'
Publikováno v:
工程科学学报, Vol 46, Iss 4, Pp 614-626 (2024)
Coalbed methane (CBM) is one of the realistic and reliable strategic supplementary resources of conventional natural gas in China, and intelligent calibration of CBM production capacity is of great importance for developing the natural gas industry.
Externí odkaz:
https://doaj.org/article/7a0915b09e5f4123aa81d5e8403b9d98
Publikováno v:
IEEE Access, Vol 8, Pp 47209-47219 (2020)
Machine learning is becoming prevalent increasingly for reservoir characteristics analysis in the petroleum industry. This investigation proposes an alternative way for evaluating interwell connectivity in oil fields utilizing machine learning. In th
Externí odkaz:
https://doaj.org/article/14f7e4f3b0f84077bd8960949c0e2016
Publikováno v:
Geofluids, Vol 2021 (2021)
Relative permeability is a key index in resource exploitation, energy development, environmental monitoring, and other fields. However, the current determination methods of relative permeability are inefficient and invisible without considering wetti
Externí odkaz:
https://doaj.org/article/09db06e6b3ff42fcbc5702d29f50e854
Publikováno v:
Geofluids, Vol 2021 (2021)
It is important to realize rapid and accurate prediction of fluid viscosity in a multiphase reservoir oil system for improving oil production in petroleum engineering. This study proposed three viscosity prediction models based on machine learning ap
Externí odkaz:
https://doaj.org/article/b3d3e3919f2e43bbb5730b41e15e2dfe
Publikováno v:
Geofluids, Vol 2020 (2020)
With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper prese
Externí odkaz:
https://doaj.org/article/e6d5463702f14a408a56a8742b5975e9
Publikováno v:
Energies, Vol 12, Iss 19, p 3597 (2019)
Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution
Externí odkaz:
https://doaj.org/article/0e54bf68379148b586d9418cb611725e
Autor:
Chiyu Xie1,2 chiyuxie@ustb.edu.cn, Shuyi Du1,2 15702449699@163.com, Jiulong Wang2,3 jlwang@cnic.cn, Junming Lao1,2 junminglao@xs.ustb.edu.cn, Hongqing Song1,2 songhongqing@ustb.edu.cn
Publikováno v:
Advances in Geo-Energy Research. May2023, Vol. 8 Issue 2, p71-75. 5p.
Publikováno v:
Monthly Notices of the Royal Astronomical Society. 522:1697-1705
Machine learning techniques, showing high automation and efficiency in handling large amounts of observation data, have been applied to predict the thermal inertia of Mars from surface kinetic temperatures. We created a large data set from well-estab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36ef0eacfdfb8fb9ecf2443889922cda
https://doi.org/10.1093/mnras/stad1093
https://doi.org/10.1093/mnras/stad1093
Autor:
Ming Yue, Tianru Song, Qiang Chen, Mingxu Yu, Yuhe Wang, Jiulong Wang, Shuyi Du, Hongqing Song
Publikováno v:
Petroleum Science and Technology. :1-23
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ac5ea88a63fbbe21c402f217c599d7e
https://doi.org/10.1080/10916466.2022.2096635
https://doi.org/10.1080/10916466.2022.2096635
Publikováno v:
Geofluids, Vol 2020 (2020)
With the rapid development of computer technology, some machine learning methods have begun to gradually integrate into the petroleum industry and have achieved some achievements, whether in conventional or unconventional reservoirs. This paper prese
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f225289f5e8c5a0cb1c6ef4de1df519
https://doi.org/10.1155/2020/3713525
https://doi.org/10.1155/2020/3713525