Artificial Neural Network Based Model for Crude Oil Viscosity Prediction
Publish place: 07th International Congress on Chemical Engineering
Publish Year: 1390
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICHEC07_615
تاریخ نمایه سازی: 25 فروردین 1394
Abstract:
Viscosity is a crucial physical property of crude oil which is used in the calculations of formation evaluation, fluid flow through porous media and the design of production and surface facilities, and pipeline. A feed-forward back-propagation neural network model with with Levenberg- Marquardt training algorithm is presented based on 357 data sets of Iranian crudes for estimation of saturated and undersaturated oil viscosity. The developed model is tested and compared to someempirical corrolations by 90 data sets. The neural network model is generally more accurate than correlations. It outperformed corrolations with highest corrolation coeficients and lowest average absolute relative errors.
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Authors
Siyamak Moradi
Petroleum University of Technology, Abadan Faculty of Petroleum Engineering, Northern Bowarde, Abadan, Iran
Jamshid Moghadasi
Petroleum University of Technology, Abadan Faculty of Petroleum Engineering, Northern Bowarde, Abadan, Iran
Koorosh Kazemi
Petroleum University of Technology, Abadan Faculty of Petroleum Engineering, Northern Bowarde, Abadan, Iran
Saadat Mohammad Hosein Zadeh
Petroleum University of Technology, Abadan Faculty of Petroleum Engineering, Northern Bowarde, Abadan, Iran
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