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

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 i...

Full description

Main Authors: Chala, G.T., Negash, B.M.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.28615 /
Published: MDPI 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125167423&doi=10.3390%2fen15051725&partnerID=40&md5=e0cbe30151dd94152326d9d7205e4408
http://eprints.utp.edu.my/28615/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.28615
recordtype eprints
spelling utp-eprints.286152022-03-07T08:15:15Z Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines Chala, G.T. Negash, B.M. 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. MDPI 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125167423&doi=10.3390%2fen15051725&partnerID=40&md5=e0cbe30151dd94152326d9d7205e4408 Chala, G.T. and Negash, B.M. (2022) Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines. Energies, 15 (5). http://eprints.utp.edu.my/28615/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Chala, G.T.
Negash, B.M.
spellingShingle Chala, G.T.
Negash, B.M.
Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines
author_sort Chala, G.T.
title Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines
title_short Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines
title_full Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines
title_fullStr Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines
title_full_unstemmed Artificial Neural Network and Regression Models for Predicting Intrusion of Non-Reacting Gases into Production Pipelines
title_sort artificial neural network and regression models for predicting intrusion of non-reacting gases into production pipelines
publisher MDPI
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125167423&doi=10.3390%2fen15051725&partnerID=40&md5=e0cbe30151dd94152326d9d7205e4408
http://eprints.utp.edu.my/28615/
_version_ 1741197130477862912
score 11.62408

Similar Items