Modeling PVT properties of crude oil systems based on type-2 fuzzy logic approach and sensitivity based linear learning method

Authors:
Ali Selamat;S. O. Olatunji;Abdul Azeez Abdul Raheem
Affiliations:
Faculty of Computer and Information Systems, Universiti Teknologi, Malaysia;Faculty of Computer and Information Systems, Universiti Teknologi, Malaysia;Department of Petroleum, King Fahd University of Petroleum and Minerals, Saudi Arabia
Venue:
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Year:
2012

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Abstract

In this paper, we studies on a prediction model of Pressure-Volume-Temperature (PVT) properties of crude oil systems using a hybrid type-2 fuzzy logic system (type-2 FLS) and sensitivity based linear learning method (SBLLM). The PVT properties are very important in the reservoir engineering computations whereby an accurate determination of PVT properties is important in the subsequent development of an oil field. In the formulation used, for the type-2 FLS the value of a membership function corresponding to a particular PVT properties value is no longer a crisp value; rather, it is associated with a range of values that can be characterized by a function that reflects the level of uncertainty, while in the case of SBBLM, the sensitivity analysis coupled with a linear training algorithm by human subject selections for each of the two layers is employed which ensures that the learning curve stabilizes soon and behave homogenously throughout the entire process operation based on the collective intelligence algorithms. Results indicated that type-2 FLS had better performance for the case of dataset with large data points (782-dataset) while SBLLM performed better for the small dataset (160-dataset).