Zobrazeno 1 - 10
of 175
pro vyhledávání: '"Hongliu ZENG"'
Publikováno v:
Petroleum Exploration and Development, Vol 45, Iss 5, Pp 830-839 (2018)
This study applied seismic-sedimentological workflow to deeply buried marine carbonate sequences in western China. The workflow aimed at integrating core, wire line log and 3D seismic data to investigate the paleogeomorphology qualitatively and reser
Externí odkaz:
https://doaj.org/article/8857b65ccc844c2ca229f9f0b5c2638a
Autor:
Hongliu ZENG, Xianzheng ZHAO, Xiaomin ZHU, Fengming JIN, Yanlei DONG, Yuquan WANG, Mao ZHU, Ronghua ZHENG
Publikováno v:
Petroleum Exploration and Development, Vol 42, Iss 5, Pp 621-632 (2015)
The seismic sedimentology characteristics of sub-clinoformal shallow-water meandering river delta can be comprehensively analyzed by core, well logging and 3-D seismic data. This paper summarizes the sedimentary pattern of sub-clinoformal shallow-wat
Externí odkaz:
https://doaj.org/article/982454a71d754a80bcc3e95372304a2e
Publikováno v:
Petroleum Exploration and Development, Vol 40, Iss 3, Pp 287-295 (2013)
Seismic diagenetic facies is an important control on reservoir quality. This study investigated the feasibility of predicting sandstone diagenetic facies using conventional 3D seismic data by seismic sedimentology and calibrated by laboratory core-an
Externí odkaz:
https://doaj.org/article/de9d87ea40d747a4bf6033f8143f39a3
Publikováno v:
Petroleum Exploration and Development, Vol 39, Iss 3, Pp 295-304 (2012)
This study summarizes the research experiences of non-marine seismic sedimentology in recent years in China and uses Qijia area, Songliao Basin, as a template to establish general guidelines for seismic sedimentology. Basic data sets include stacked
Externí odkaz:
https://doaj.org/article/bfcc500e4fc141d5ab04d80067df89ca
Publikováno v:
GEOPHYSICS. 88:R193-R207
Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb020a83693749d2797792f92cb65f85
https://doi.org/10.1190/geo2021-0726.1
https://doi.org/10.1190/geo2021-0726.1
Autor:
Osareni C. Ogiesoba, Hongliu Zeng
Publikováno v:
Interpretation. 10:T265-T278
We document the results of a 3D seismic investigation undertaken in search of prospective hydrocarbon, sandstone-rich zones within the Eocene Yegua Formation, which is located in a shale-dominated environment in Jackson County, South Texas. Because o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95345f998bb955285fe1aa8ed40c2477
https://doi.org/10.1190/int-2021-0139.1
https://doi.org/10.1190/int-2021-0139.1
Publikováno v:
Interpretation. 9:T1009-T1024
We have developed a new machine-learning (ML) workflow that uses random forest (RF) regression to predict sedimentary-rock properties from stacked and migrated 3D seismic data. The training, validation, and testing are performed with 40 features extr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9a37ca2fa624dc3903d15f72bf154ee
https://doi.org/10.1190/int-2021-0035.1
https://doi.org/10.1190/int-2021-0035.1
Publikováno v:
Second International Meeting for Applied Geoscience & Energy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cfc07596ffe40b226ebcb9d9fb96556f
https://doi.org/10.1190/image2022-3746354.1
https://doi.org/10.1190/image2022-3746354.1
Autor:
Mariana I. Olariu, Hongliu Zeng
Publikováno v:
Second International Meeting for Applied Geoscience & Energy.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2441970e46d9a35c67c04f1cdb4c309
https://doi.org/10.1190/image2022-3745159.1
https://doi.org/10.1190/image2022-3745159.1