Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir

This study applied a Machine Learning Algorithm based on Random Forest Regression for eliminating the insignificant parameter and evaluating the correlation between each parameter and response parameter on the LSWI process. 1000 experimental designs of LSWI parameters, Reservoir & Injection...

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Autores principales:	Fiki Hidayat, T. Mhd. Sofyan Astsauri
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Low salinity water injection (LSWI) Carbonate Reservoir Sensitivity Analysis Random Forest Algorithm Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/7a59ded17f7046b4944d61dbcc252692
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spelling	oai:doaj.org-article:7a59ded17f7046b4944d61dbcc2526922021-12-02T04:59:37ZApplied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir1110-016810.1016/j.aej.2021.06.096https://doaj.org/article/7a59ded17f7046b4944d61dbcc2526922022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1110016821004579https://doaj.org/toc/1110-0168This study applied a Machine Learning Algorithm based on Random Forest Regression for eliminating the insignificant parameter and evaluating the correlation between each parameter and response parameter on the LSWI process. 1000 experimental designs of LSWI parameters, Reservoir & Injection Temperature, Volume Injection, Formation Water Composition, and Injection Water Composition were build using Design of Experiment on CMOST from Computer Modeling Group with Recovery Factor as the response parameter. Finally, the sensitivity analysis is carried out on Random Forest Regressor based on the decrease in the mean squared error (MSE). The Random Forest Algorithm methods respectively recognized Injection SO42- Composition, Formation Water SO42-Composition dan Volume Injection as the top three of most significant parameters. Five variations of the random state value are applied and the hyperparameters of Random Forest also optimized. Both training and test data, the R2 score respectively are consistently over 0.9 for 5 variations of the random state used. The information about the significant operation parameter of the LSWI process presented in this article is potential bearing the novel to the industry. The insight into those parameters is predicted to be useful to encourage the LSWI implementation on Carbonate Reservoir.Fiki HidayatT. Mhd. Sofyan AstsauriElsevierarticleLow salinity water injection (LSWI)Carbonate ReservoirSensitivity AnalysisRandom Forest AlgorithmEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 3, Pp 2408-2417 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Low salinity water injection (LSWI) Carbonate Reservoir Sensitivity Analysis Random Forest Algorithm Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Low salinity water injection (LSWI) Carbonate Reservoir Sensitivity Analysis Random Forest Algorithm Engineering (General). Civil engineering (General) TA1-2040 Fiki Hidayat T. Mhd. Sofyan Astsauri Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir
description	This study applied a Machine Learning Algorithm based on Random Forest Regression for eliminating the insignificant parameter and evaluating the correlation between each parameter and response parameter on the LSWI process. 1000 experimental designs of LSWI parameters, Reservoir & Injection Temperature, Volume Injection, Formation Water Composition, and Injection Water Composition were build using Design of Experiment on CMOST from Computer Modeling Group with Recovery Factor as the response parameter. Finally, the sensitivity analysis is carried out on Random Forest Regressor based on the decrease in the mean squared error (MSE). The Random Forest Algorithm methods respectively recognized Injection SO42- Composition, Formation Water SO42-Composition dan Volume Injection as the top three of most significant parameters. Five variations of the random state value are applied and the hyperparameters of Random Forest also optimized. Both training and test data, the R2 score respectively are consistently over 0.9 for 5 variations of the random state used. The information about the significant operation parameter of the LSWI process presented in this article is potential bearing the novel to the industry. The insight into those parameters is predicted to be useful to encourage the LSWI implementation on Carbonate Reservoir.
format	article
author	Fiki Hidayat T. Mhd. Sofyan Astsauri
author_facet	Fiki Hidayat T. Mhd. Sofyan Astsauri
author_sort	Fiki Hidayat
title	Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir
title_short	Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir
title_full	Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir
title_fullStr	Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir
title_full_unstemmed	Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir
title_sort	applied random forest for parameter sensitivity of low salinity water injection (lswi) implementation on carbonate reservoir
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/7a59ded17f7046b4944d61dbcc252692
work_keys_str_mv	AT fikihidayat appliedrandomforestforparametersensitivityoflowsalinitywaterinjectionlswiimplementationoncarbonatereservoir AT tmhdsofyanastsauri appliedrandomforestforparametersensitivityoflowsalinitywaterinjectionlswiimplementationoncarbonatereservoir
_version_	1718400896466944000

Applied random forest for parameter sensitivity of low salinity water Injection (LSWI) implementation on carbonate reservoir

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