Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Fatai Anifowose"'
Autor:
Babatunde Anifowose, Fatai Anifowose
Publikováno v:
Environmental Advances, Vol 17, Iss , Pp 100554- (2024)
Scientific predictions are a key component of Environmental Impact Assessments (EIA), which can indicate the level of change within an environmental sphere (e.g., soil). As part of the EIA process, decision-making in mitigating complex environmental
Externí odkaz:
https://doaj.org/article/7cbbd798eecd44ca9ae2448e0d870995
Publikováno v:
Applied Computing and Geosciences, Vol 16, Iss , Pp 100095- (2022)
The current utility of mud gas data is typically limited to geological and petrophysical correlation, formation evaluation, and fluid typing. A critical and comprehensive review of the literature on mud gas data revealed that the mud gas data is abun
Externí odkaz:
https://doaj.org/article/057e77376f0f4421a7f0a0c11d99676a
Publikováno v:
Day 1 Sun, February 19, 2023.
The utility of advanced mud gas (AMG) data has been limited to fluid typing and petrophysical correlations. There is the need to extend the utility to real-time reservoir characterization prior to wireline logging and geological core description. Our
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da2ae09f6596f2bea7da75f28c6c1da9
https://doi.org/10.2118/213339-ms
https://doi.org/10.2118/213339-ms
Publikováno v:
Journal of Structural Integrity and Maintenance. 5:252-264
This article exhibits an experimental study carried out to investigate the combined effect of Silica Fume (SF) and Metakaolin (MK) on the fresh and hardened properties of concrete. The replacement ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c03964c67cd1bfe2286a2ac9820d017e
https://doi.org/10.1080/24705314.2020.1783120
https://doi.org/10.1080/24705314.2020.1783120
Publikováno v:
Day 2 Tue, February 22, 2022.
Porosity, a critical property of petroleum reservoirs, is a key controlling factor of the reservoir storage capacity. It has been conventionally measured from core plugs. Empirical correlations, statistical, and machine learning methods have been emp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d9b71611cb1a35a1f85cbed88efa859
https://doi.org/10.2523/iptc-22061-ea
https://doi.org/10.2523/iptc-22061-ea
Autor:
Ashhad Imam, Fatai Anifowose
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d77b76af44592d8e060f82d47ecdfec7
https://doi.org/10.2139/ssrn.4047147
https://doi.org/10.2139/ssrn.4047147
Publikováno v:
Day 2 Tue, October 19, 2021.
Reservoir rock textural properties such as grain size are typically estimated by direct visual observation of the physical texture of core samples. Grain size is one of the important inputs to petrophysical characterization, sedimentological facies c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5b9d154020e4296f30bed89c13e8dfe3
https://doi.org/10.2118/205156-ms
https://doi.org/10.2118/205156-ms
Autor:
Fatai Anifowose
Publikováno v:
Day 1 Mon, October 18, 2021.
The petroleum industry has continued to show more interest in the application of artificial intelligence (AI). Most professional gatherings now have sub-themes to highlight AI applications. Similarly, the number of publications featuring AI applicati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f62dc389de4ec37b86c93ffcb8d30ee2
https://doi.org/10.2118/208875-ms
https://doi.org/10.2118/208875-ms
Publikováno v:
In Expert Systems With Applications 2010 37(7):5353-5363
Publikováno v:
Journal of Petroleum Science and Engineering. 176:762-774
One of the recipes for the big data and artificial intelligence paradigms is multi-dimensional data integration for improved decision making in petroleum reservoir characterization. Various machine learning (ML) techniques have been applied. However,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c920bea85a536a872d4bd27cb3f96715
https://doi.org/10.1016/j.petrol.2019.01.110
https://doi.org/10.1016/j.petrol.2019.01.110