Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study

This study aims to develop a universal artificial neural network model for estimating pressure drop at pipelines under multiphase flow conditions. Three phase flow data have been collected from different geographical locations; especially from Middle-Eastern fields in order to construct, test, and v...

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Main Authors: Ayoub, Mohammed Abdalla, Demiral, B.M.R
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.3883 /
Published: 2010
Subjects:
T Technology (General)
Online Access: http://eprints.utp.edu.my/3883/1/PEG-D3-09A-05.pdf
http://eprints.utp.edu.my/3883/
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spelling utp-eprints.38832017-03-20T07:37:09Z Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study Ayoub, Mohammed Abdalla Demiral, B.M.R T Technology (General) This study aims to develop a universal artificial neural network model for estimating pressure drop at pipelines under multiphase flow conditions. Three phase flow data have been collected from different geographical locations; especially from Middle-Eastern fields in order to construct, test, and validate the model. The data covered a wide range of variables such as oil rate (up to 25000 STB/D), water cut (up to 60%), angles of inclination (from -80 to 210), pipe length up to 26.0 km and pressure drop (from 10 to 250 psi). the model has been generated using the Back-propagation technique with Bayesian Regularization training algorithm for predicting pressure drop in pipelines under various angles of inclination. A total number of data points consists of 335 sets has been used for generating, validating, and testing the ANN model. A model performance has been evaluated against the best empirical correlations and mechanistic models (Xiao et al, Gomez et al, and Beggs & Brill). A series of statistical and graphical analysis were conducted to show the superiority of the generated model. A thorough literature review is also conducted to check the applicability of the existed correlations and mechanistic models and their drawbacks compared with the new proposed ANN model. The new developed model outperforms all the investigated models with correlation coefficient reaches 99.91%. 2010 Conference or Workshop Item PeerReviewed application/pdf http://eprints.utp.edu.my/3883/1/PEG-D3-09A-05.pdf Ayoub, Mohammed Abdalla and Demiral, B.M.R (2010) Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study. In: 2010 International Conference on INtegrated Petroleum Engineering and Geosciences, 15-17 June, 2010, Kuala Lumpur, Malaysia. http://eprints.utp.edu.my/3883/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
topic T Technology (General)
spellingShingle T Technology (General)
Ayoub, Mohammed Abdalla
Demiral, B.M.R
Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study
description This study aims to develop a universal artificial neural network model for estimating pressure drop at pipelines under multiphase flow conditions. Three phase flow data have been collected from different geographical locations; especially from Middle-Eastern fields in order to construct, test, and validate the model. The data covered a wide range of variables such as oil rate (up to 25000 STB/D), water cut (up to 60%), angles of inclination (from -80 to 210), pipe length up to 26.0 km and pressure drop (from 10 to 250 psi). the model has been generated using the Back-propagation technique with Bayesian Regularization training algorithm for predicting pressure drop in pipelines under various angles of inclination. A total number of data points consists of 335 sets has been used for generating, validating, and testing the ANN model. A model performance has been evaluated against the best empirical correlations and mechanistic models (Xiao et al, Gomez et al, and Beggs & Brill). A series of statistical and graphical analysis were conducted to show the superiority of the generated model. A thorough literature review is also conducted to check the applicability of the existed correlations and mechanistic models and their drawbacks compared with the new proposed ANN model. The new developed model outperforms all the investigated models with correlation coefficient reaches 99.91%.
format Conference or Workshop Item
author Ayoub, Mohammed Abdalla
Demiral, B.M.R
author_sort Ayoub, Mohammed Abdalla
title Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study
title_short Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study
title_full Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study
title_fullStr Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study
title_full_unstemmed Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study
title_sort development of a universal artificial neural network model for pressure loss estimation in pipeline systems; a comparative study
publishDate 2010
url http://eprints.utp.edu.my/3883/1/PEG-D3-09A-05.pdf
http://eprints.utp.edu.my/3883/
_version_ 1741196071183319040
score 11.62408

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