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Impact of Eco-Friendly Drilling Additives on Foaming Properties for Sustainable Underbalanced Foam Drilling Applications.
ACS Omega. 2024 Jan 30;9(6):6719-6730. doi: 10.1021/acsomega.3c07882. eCollection 2024 Feb 13.
ACS Omega. 2024.
PMID: 38371819
Free PMC article.
Impact of Pressure and Temperature on Foam Behavior for Enhanced Underbalanced Drilling Operations.
Gowida A, Elkatatny S, Ibrahim AF, Kamal MS.
Gowida A, et al.
ACS Omega. 2023 Dec 29;9(1):1042-1055. doi: 10.1021/acsomega.3c07263. eCollection 2024 Jan 9.
ACS Omega. 2023.
PMID: 38222667
Free PMC article.
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Application of Silicomanganese Fume as a Novel Bridging Material for Water-Based Drilling Fluids.
Razzaq W, Elkatatny S, Gowida A, Samsuri A.
Razzaq W, et al. Among authors: gowida a.
ACS Omega. 2022 Dec 23;8(1):509-518. doi: 10.1021/acsomega.2c05456. eCollection 2023 Jan 10.
ACS Omega. 2022.
PMID: 36643510
Free PMC article.
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A hybrid data-driven solution to facilitate safe mud window prediction.
Gowida A, Ibrahim AF, Elkatatny S.
Gowida A, et al.
Sci Rep. 2022 Sep 21;12(1):15773. doi: 10.1038/s41598-022-20195-7.
Sci Rep. 2022.
PMID: 36131092
Free PMC article.
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New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data.
Gowida A, Ibrahim AF, Elkatatny S, Ali A.
Gowida A, et al.
ACS Omega. 2022 Apr 18;7(16):13507-13519. doi: 10.1021/acsomega.1c06596. eCollection 2022 Apr 26.
ACS Omega. 2022.
PMID: 35559186
Free PMC article.
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Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application.
Gowida A, Ibrahim AF, Elkatatny S, Ali A.
Gowida A, et al.
Comput Intell Neurosci. 2021 Nov 30;2021:8865827. doi: 10.1155/2021/8865827. eCollection 2021.
Comput Intell Neurosci. 2021.
PMID: 34887917
Free PMC article.
Retracted.
Item in Clipboard
Machine learning application to predict in-situ stresses from logging data.
Ibrahim AF, Gowida A, Ali A, Elkatatny S.
Ibrahim AF, et al. Among authors: gowida a.
Sci Rep. 2021 Dec 6;11(1):23445. doi: 10.1038/s41598-021-02959-9.
Sci Rep. 2021.
PMID: 34873259
Free PMC article.
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Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks.
Gowida A, Elkatatny S, Abdelgawad K, Gajbhiye R.
Gowida A, et al.
Sensors (Basel). 2020 May 14;20(10):2787. doi: 10.3390/s20102787.
Sensors (Basel). 2020.
PMID: 32422960
Free PMC article.
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