Application of Radial Basis Function (RBF) neural networks to estimate oil field drilling fluid density at elevated pressures and temperatures
National Iranian Oil Company (NIOC), Corporate Planning Management, 3rd floor, 3rd NIOC Headquarter, No. 18, Roodsar Street, Hafez Street, Tehran, Iran
2 Young Researchers and Elites Club, North Tehran Branch, Islamic Azad University, Tehran, Iran
Accepted: 29 March 2019
The petroleum industry today has no choice, but to explore new and ever more deep and challenging pay zones as the most of the shallow oil and gas producing pay zones are severely depleted during the years of production. For improved drilling fluid performance in deep and hostile environment wells, accurate knowledge about the fluid density at high temperature and pressure conditions is an essential step. To achieve this mission, this study is aiming at developing a new computer-based tool is designed and applied for accurate calculation of drilling fluid density at HPHT conditions. In order to seek the comprehensiveness of the developed tool, four different kinds of fluids including water based, oil based, Colloidal Gas Aphron (CGA) based and also synthetic fluids are selected for modeling purpose. Radial Basis Function (RBF) network is considered as the modeling network. The results calculated via the proposed algorithm are compared to data reported in the literature. To make a judgment based on various statistical quality measures, it is concluded that the developed tool is reliable and efficient for density calculations of various fluids at extreme conditions.
© A.S. Rahmati & A. Tatar, published by IFP Energies nouvelles, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.