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Hindi Research Article
New PVT Correlations Based on Libyan Crudes for Predicting Fluid Physical Properties
Khazam M*, Shlak M and Alkhaboli MDepartment of Petroleum Engineering, University of Tripoli, Libya
- *Corresponding Author:
- M Khazam
Department of Petroleum Engineering
University of Tripoli, Tripoli, Libya
Tel.: +218 21-4627901
E-mail: mohsenkhazam@gmail.com
Received Date: November 15, 2016; Accepted Date: November 22, 2016; Published Date: November 28, 2016
Citation: Khazam M, Shlak M, Alkhaboli M (2016) New PVT Correlations Based on Libyan Crudes for Predicting Fluid Physical Properties. Oil Gas Res 2:122. doi: 10.4172/2472-0518.1000122
Copyright: © 2016 Khazam M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
PVT properties are very important in reservoir and production engineering analyses such as material balance calculations, well testing, reserve estimation, inflow performance, production operations and design of surface facilities. New empirical PVT correlations have been developed for Libyan crudes with reliable degree of accuracy. These include; bubble point pressure (Pb), oil formation volume factor (Bo), gas solubility (Rs), stock tank oil molecular weight (Mwt), dead oil viscosity (μod), saturated oil viscosity (μob), under-saturated oil viscosity (μo), and oil compressibility (Co). Around 300 PVT samples collected exclusively from Libya, mainly Sirte, Ghadames and Murzuq basins, were used in our study to develop the above PVT correlations and covered wide range of API gravity (26 to 51°API) and reservoir temperature (100 to 313°F) normally found in Libyan reservoirs. Minitab regression tool was extensively used in our study to develop the PVT correlations and to statistically appraise them against the industry published correlations. The new proposed PVT correlations have demonstrated much better performance compared to the industry published correlations when tested for Libyan crudes. Also, Artificial Neural Network (ANN) models have been developed for Libyan PVT properties predictions. The models show acceptable accuracy and generally are more accurate than the empirical correlations.
