Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines

Girma Tadesse Chala and Berihun Mamo Negash
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Girma Tadesse Chala: Department of Mechanical (Well) Engineering, International College of Engineering and Management, P.O. Box 2511, CPO Seeb, Muscat 111, Oman
Berihun Mamo Negash: Department of Petroleum Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia

Energies, 2022, vol. 15, issue 5, 1-14

Abstract: Wax deposition and gelation of waxy crude oil in production pipelines are detrimental to crude oil transportation from offshore fields. A waxy crude oil forms intra-gel voids in pipelines under cooling mode, particularly below the pour point temperature. Consequently, intrusion of non-reacting gas into production pipelines has become a promising method to lessen the restart pressure required and clear the clogged gel. A trial-and-error method is currently employed to determine the required restart pressure and restart time in response to injected gas volume. However, this method is not always accurate and requires expert knowledge. In this study, predictive models based on an Artificial Neural Network (ANN) and multilinear regression are developed to predict restart pressure and time as a function of seabed temperature and non-reacting gas injected volume . The models’ outcomes are compared against experimental results available from the literature. The empirical models predicted the response variables with an absolute error of below 5% compared to the experimental studies. Thus, such models would allow accurate estimation of restart pressure , thereby improving transportation efficiency in offshore fields.

Keywords: waxy crude oil; restart pressure; restart time; ANN; multilinear regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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