@inproceedings{fbcdd567996349f5b3bb0fc643abaf19,
title = "A data-driven genetic neuro-fuzzy system to PVT properties prediction",
abstract = "Pressure-Volume-Temperature (PVT) properties are very important in reservoir engineering computations. There are many approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Soft computing techniques and especially artificial neural networks had been utilized in the last decade by researchers to develop more accurate PVT correlations. Unfortunately, the developed neural networks correlations are often limited providing less accurate global correlations are usually. In this paper, a genetic-neuro-fuzzy inference system (GANFIS) is proposed for estimating PVT properties of crude oil systems. Simulation experiments show that the proposed technique outperforms up to date methods.",
keywords = "Correlation, Genetic-neuro-fuzzy inference systems, Neural networks, Pressure-Volume-Temperature",
author = "Amar Khoukhi and Saeed Alboukhitan",
year = "2010",
doi = "10.1109/NAFIPS.2010.5548414",
language = "English",
isbn = "9781424478576",
series = "Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS",
booktitle = "2010 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'2010",
}