Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Luanxiao Zhao"'
Publikováno v:
Frontiers in Earth Science, Vol 10 (2023)
Understanding the geological factors behind the physical and elastic properties of marine sediments and unconsolidated rock is essential for the interpretation of geophysical measurements, hazard assessment, and ocean engineering applications. Core a
Externí odkaz:
https://doaj.org/article/301eee9ab1384b8caeda1ff9d87c1d75
Publikováno v:
Natural Gas Industry B, Vol 8, Iss 2, Pp 128-145 (2021)
Since deep-sea gas hydrate-bearing sediments were drilled for the first time in the Blake Ridge in 1970, gas hydrates have been discovered at 53 drill sites in the continental margins of global oceans with international scientific ocean drilling (DSD
Externí odkaz:
https://doaj.org/article/834b17a4b77b42ea9c21cfbcf0a28850
Publikováno v:
Frontiers in Earth Science, Vol 10 (2022)
Diagnosing fractures under compression is of great importance in optimizing hydraulic fracturing stimulation strategies for unconventional reservoirs. However, a lot of information, such as fracture morphology and fracture complexity, is far from bei
Externí odkaz:
https://doaj.org/article/d451846438f24c968e4c73357e15b6b3
Publikováno v:
Lithosphere, Vol 2021, Iss Special 3 (2022)
AbstractThe static bulk modulus of unconsolidated sands is essential for predicting the in situ effective pressure to reduce drilling risks in deepwater reservoirs; however, the dynamic bulk modulus is often more broadly available from seismic or wel
Externí odkaz:
https://doaj.org/article/b6c69ff09dc440caaac0248b908645f8
Autor:
Xuan Qin1 qin.xuan1@gmail.com, Luanxiao Zhao2 zhaoluanxiao@tongji.edu.cn, Jinwan Zhu2 2011475@tongji.edu.cn, De-hua Han1 dhan@central.uh.edu
Publikováno v:
Advances in Geo-Energy Research. Dec2023, Vol. 10 Issue 3, p174-188. 15p.
Publikováno v:
GEOPHYSICS. 88:M185-M200
Seismic fluids prediction under the machine-learning framework is of great significance for the exploration and development of oil and gas resources, geothermal energy exploitation, carbon dioxide sequestration monitoring, and groundwater management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ca31c7acefc34049bd2cfd3229859ab
https://doi.org/10.1190/geo2022-0363.1
https://doi.org/10.1190/geo2022-0363.1
Autor:
Luanxiao Zhao, Zhenjia Cai, Xuan Qin, Yang Wang, Teng Long, De-Hua Han, Fengshou Zhang, Jianhua Geng
Publikováno v:
GEOPHYSICS. 88:MR117-MR126
Organic shales can split into laminae or thin layers due to their fissility and are usually characterized as transverse isotropic media. Understanding their elastic and anisotropic properties plays a significant role in geophysical modeling and imagi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6174c5598be291d637a81a1383082c1
https://doi.org/10.1190/geo2022-0543.1
https://doi.org/10.1190/geo2022-0543.1
Publikováno v:
GEOPHYSICS. 88:B101-B120
Porosity prediction from seismic data in carbonate reservoirs is challenging because the common presence of heterogeneities in carbonates makes it difficult to establish a clear physical relationship between reservoir properties and elastic responses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b22cebeb73e09b6ef3a7224a93117ab4
https://doi.org/10.1190/geo2021-0342.1
https://doi.org/10.1190/geo2021-0342.1
Autor:
Jianchao Cai1 caijc@cup.edu.cn, Luanxiao Zhao2 zhaoluanxiao@tongji.edu.cn, Feng Zhang3 zhangfeng@cup.edu.cn, Wei Wei4 weiwei@cug.edu.cn
Publikováno v:
Advances in Geo-Energy Research. Aug2022, Vol. 6 Issue 4, p271-275. 5p.
Publikováno v:
GEOPHYSICS. 88:M87-M103
Model-data-driven (MDD) generative adversarial networks (GANs) using prestack seismic data to estimate elastic parameters are proposed. First, by traverse sampling of elastic parameter model space and Gaussian sampling, a complete elastic parameter d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::910d1bc007af7aa9fffd8814ad2081ee
https://doi.org/10.1190/geo2022-0314.1
https://doi.org/10.1190/geo2022-0314.1