Predicting pressure losses in the water-assisted flow of unconventional crude with machine learning
Machine learning (ML) is recognized as an efficient prediction tool. However, very few attempts have been made to apply it to model pressure losses in the water-assisted pipeline transportation of unconventional crudes. The performances of conventional ML algorithms for predictions were analyzed in...
| Published in: | Petroleum Science and Technology |
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| Main Author: | |
| Format: | Article |
| Language: | English |
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Taylor and Francis Ltd.
2021
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115717461&doi=10.1080%2f10916466.2021.1980012&partnerID=40&md5=e49af5c174986d6efc76595b08c03f78 |
| Summary: | Machine learning (ML) is recognized as an efficient prediction tool. However, very few attempts have been made to apply it to model pressure losses in the water-assisted pipeline transportation of unconventional crudes. The performances of conventional ML algorithms for predictions were analyzed in the current study based on a dataset comprised of 225 data points and seven input parameters: pipe diameter, average velocity, densities of oil and water, viscosities of oil and water, and water content. Among the algorithms tested, the artificial neural network demonstrated the most promising performance with the coefficient of determination (R2) of 0.99 and mean squared error (MSE) of 0.009. © 2021 Taylor & Francis Group, LLC. |
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| ISSN: | 10916466 |
| DOI: | 10.1080/10916466.2021.1980012 |