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Ibrahim, Ahmed Farid (2023) Prediction of shale wettability using different machine learning techniques for the application of CO2 sequestration. International Journal of Coal Geology, 276. Elsevier BV. 104318 doi:10.1016/j.coal.2023.104318

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Reference TypeJournal (article/letter/editorial)
TitlePrediction of shale wettability using different machine learning techniques for the application of CO2 sequestration
JournalInternational Journal of Coal Geology
AuthorsIbrahim, Ahmed FaridAuthor
Year2023 (August)Volume276
PublisherElsevier BV
DOIdoi:10.1016/j.coal.2023.104318Search in ResearchGate
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Mindat Ref. ID16632010Long-form Identifiermindat:1:5:16632010:7
GUID0
Full ReferenceIbrahim, Ahmed Farid (2023) Prediction of shale wettability using different machine learning techniques for the application of CO2 sequestration. International Journal of Coal Geology, 276. Elsevier BV. 104318 doi:10.1016/j.coal.2023.104318
Plain TextIbrahim, Ahmed Farid (2023) Prediction of shale wettability using different machine learning techniques for the application of CO2 sequestration. International Journal of Coal Geology, 276. Elsevier BV. 104318 doi:10.1016/j.coal.2023.104318
In(2023) International Journal of Coal Geology Vol. 276. Elsevier BV

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Ibrahim, Ahmed Farid (2022) Application of various machine learning techniques in predicting coal wettability for CO2 sequestration purpose. International Journal of Coal Geology, 252. 103951pp. doi:10.1016/j.coal.2022.103951
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Ibrahim, Ahmed Farid, Elkatatny, Salaheldin (2023) Data-driven models to predict shale wettability for CO2 sequestration applications. Scientific Reports, 13 (1) doi:10.1038/s41598-023-37327-2
Rezk, Mohamed Gamal, Ibrahim, Ahmed Farid, Adebayo, Abdulrauf R. (2023) Uncertainty quantification for CO2 storage during intermittent CO2-EOR in oil reservoirs. International Journal of Coal Geology, 266. 104177 doi:10.1016/j.coal.2022.104177
Erten, Gamze Erdogan, Erten, Oktay, Karacan, C. Özgen, Boisvert, Jeff, Deutsch, Clayton V. (2023) Merging machine learning and geostatistical approaches for spatial modeling of geoenergy resources. International Journal of Coal Geology, 276. Elsevier BV. 104328 doi:10.1016/j.coal.2023.104328
Anani, Angelina, Adewuyi, Sefiu O., Risso, Nathalie, Nyaaba, Wedam (2024) Advancements in machine learning techniques for coal and gas outburst prediction in underground mines. International Journal of Coal Geology, 285. 104471 doi:10.1016/j.coal.2024.104471
Abdelaal, Ahmed, Ibrahim, Ahmed Farid, Elkatatny, Salaheldin (2022) Empirical correlation for formation resistivity prediction using machine learning. Arabian Journal of Geosciences, 15 (12) doi:10.1007/s12517-022-09511-7
Ibrahim, Ahmed Farid, Alarifi, Sulaiman A., Elkatatny, Salaheldin (2023) Data-driven EUR for multistage hydraulically fractured wells in shale formation using different machine learning methods. Journal of Petroleum Exploration and Production Technology, 13 (4) 1123-1134 doi:10.1007/s13202-022-01602-1
Maxwell, Kane, Rajabi, Mojtaba, Esterle, Joan (2019) Automated classification of metamorphosed coal from geophysical log data using supervised machine learning techniques. International Journal of Coal Geology, 214. 103284 doi:10.1016/j.coal.2019.103284
Al Kalbani, Muhannad, Serati, Mehdi, Hofmann, Harald, Bore, Thierry (2023) A comprehensive review of enhanced in-situ CO2 mineralisation in Australia and New Zealand. International Journal of Coal Geology, 276. Elsevier BV. 104316 doi:10.1016/j.coal.2023.104316
Ibrahim, Ahmed Farid, Nasr-El-Din, Hisham A. (2015) A comprehensive model to history match and predict gas/water production from coal seams. International Journal of Coal Geology, 146. 79-90 doi:10.1016/j.coal.2015.05.004
 
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