Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal

Data mining is applied in many areas. In oil and gas industries, data mining may be implemented to support the decision making in their operation to prevent a massive loss. One of serious problems in the petroleum industry is congeal phenomenon, since it leads to block crude oil flow during transpor...

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Autores principales:	Santoso Agus, Wijaya F. Danang, Setiawan Noor Akhmad, Waluyo Joko
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/b4fdc25638fb4d57a3a7fcaad53664ce
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spelling	oai:doaj.org-article:b4fdc25638fb4d57a3a7fcaad53664ce2021-12-02T17:11:56ZComparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal2267-124210.1051/e3sconf/202132502002https://doaj.org/article/b4fdc25638fb4d57a3a7fcaad53664ce2021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/101/e3sconf_icst2021_02002.pdfhttps://doaj.org/toc/2267-1242Data mining is applied in many areas. In oil and gas industries, data mining may be implemented to support the decision making in their operation to prevent a massive loss. One of serious problems in the petroleum industry is congeal phenomenon, since it leads to block crude oil flow during transport in a pipeline system. In the crude oil pipeline system, pressure online monitoring in the pipeline is usually implemented to control the congeal phenomenon. However, this system is not able to predict the pipeline pressure on the next several days. This research is purposed to compare the pressure prediction of the crude oil pipeline using data mining algorithms based on the real historical data from the petroleum field. To find the best algorithms, it was compared 4 data mining algorithms, i.e. Random Forest, Multilayer Perceptron (MLP), Decision Tree, and Linear Regression. As a result, the Linear Regression shows the best performance among the 4 algorithms with R2 = 0.55 and RMSE = 28.34. This research confirmed that data mining algorithm is a good method to be implemented in petroleum industry to predict the pressure of the crude oil pipeline, even the accuracy of the prediction values should be improved. To have better accuracy, it is necessary to collect more data and find better performance of the data mining algorithmSantoso AgusWijaya F. DanangSetiawan Noor AkhmadWaluyo JokoEDP SciencesarticleEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 325, p 02002 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Environmental sciences GE1-350
spellingShingle	Environmental sciences GE1-350 Santoso Agus Wijaya F. Danang Setiawan Noor Akhmad Waluyo Joko Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal
description	Data mining is applied in many areas. In oil and gas industries, data mining may be implemented to support the decision making in their operation to prevent a massive loss. One of serious problems in the petroleum industry is congeal phenomenon, since it leads to block crude oil flow during transport in a pipeline system. In the crude oil pipeline system, pressure online monitoring in the pipeline is usually implemented to control the congeal phenomenon. However, this system is not able to predict the pipeline pressure on the next several days. This research is purposed to compare the pressure prediction of the crude oil pipeline using data mining algorithms based on the real historical data from the petroleum field. To find the best algorithms, it was compared 4 data mining algorithms, i.e. Random Forest, Multilayer Perceptron (MLP), Decision Tree, and Linear Regression. As a result, the Linear Regression shows the best performance among the 4 algorithms with R2 = 0.55 and RMSE = 28.34. This research confirmed that data mining algorithm is a good method to be implemented in petroleum industry to predict the pressure of the crude oil pipeline, even the accuracy of the prediction values should be improved. To have better accuracy, it is necessary to collect more data and find better performance of the data mining algorithm
format	article
author	Santoso Agus Wijaya F. Danang Setiawan Noor Akhmad Waluyo Joko
author_facet	Santoso Agus Wijaya F. Danang Setiawan Noor Akhmad Waluyo Joko
author_sort	Santoso Agus
title	Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal
title_short	Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal
title_full	Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal
title_fullStr	Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal
title_full_unstemmed	Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal
title_sort	comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/b4fdc25638fb4d57a3a7fcaad53664ce
work_keys_str_mv	AT santosoagus comparisonofdataminingalgorithmsforpressurepredictionofcrudeoilpipelinetoidentifycongeal AT wijayafdanang comparisonofdataminingalgorithmsforpressurepredictionofcrudeoilpipelinetoidentifycongeal AT setiawannoorakhmad comparisonofdataminingalgorithmsforpressurepredictionofcrudeoilpipelinetoidentifycongeal AT waluyojoko comparisonofdataminingalgorithmsforpressurepredictionofcrudeoilpipelinetoidentifycongeal
_version_	1718381457288724480

Comparison of data mining algorithms for pressure prediction of crude oil pipeline to identify congeal

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