TY - GEN
T1 - Prediction of Oil Well Flowing Bottom-hole Pressure in Petroleum Fields
AU - Awadalla, Medhat
AU - Yousef, Hassan
AU - Al-Hinai, Ahmed
AU - Al-Shidani, Ali
N1 - Publisher Copyright:
© IEOM Society International.
PY - 2016
Y1 - 2016
N2 - Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure is a dominant process especially in wells lifted with electrical submersible pumps. However intervening a well is occasionally an exhaustive task, associated with production risk, and interruption. The empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper proposes Feed- Forward Neural Network with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells using real measured data from different oil fields. Intensive experiments have been conducted and the standard statistical analysis has been accomplished on the achieved results to validate the models' prediction accuracy. The obtained results show that the proposed artificial neural network is capable of estimating the Flowing Bottom-Hole Pressure with high accuracy.
AB - Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure is a dominant process especially in wells lifted with electrical submersible pumps. However intervening a well is occasionally an exhaustive task, associated with production risk, and interruption. The empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper proposes Feed- Forward Neural Network with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells using real measured data from different oil fields. Intensive experiments have been conducted and the standard statistical analysis has been accomplished on the achieved results to validate the models' prediction accuracy. The obtained results show that the proposed artificial neural network is capable of estimating the Flowing Bottom-Hole Pressure with high accuracy.
KW - Back-propagation algorithm
KW - Flowing bottom-hole pressure
KW - Forward neural network
UR - http://www.scopus.com/inward/record.url?scp=84988732664&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84988732664
SN - 9780985549749
T3 - Proceedings of the International Conference on Industrial Engineering and Operations Management
SP - 3007
EP - 3017
BT - 6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
PB - IEOM Society
T2 - 6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016
Y2 - 8 March 2016 through 10 March 2016
ER -