000			01737nab a2200193 4500
005			20260520002033.0
008			260224s2011 xxu
245	0	0	_aPermeability prediction based on reservoir zonation by a hybrid neural genetic algorithm in one of the Iranian heterogeneous oil reservoirs
260			_a _b _cagos. 2011
270			_a11/04/2012 ; 11/04/2012
300			_a8 p. ; 497 - 504
520			_aTranscripción del resumen del autor Permeability is the most important parameter for precise reservoir description and modeling. Despite the advances and modification in different methods for permeability evaluation such as well testing and well logging, the most exact method is core analysis, which is expensive and time consuming. Because of the well logging data availability in most drilled wells, attempts have been made to utilize artificial neural networks for identification of the relationship, which may exist between the logging data and core permeability. In this study, a new approach based on hybrid neural genetic algorithm has been designed to predict permeability from the well logging data in one of the Iranian heterogeneous oil reservoirs. This approach is based on reservoir zonation according to geology characteristics and sorting the data in the same manner. The predicted permeability was compared to core permeability and it shows that permeability prediction based on designing separate networks for each zone is more accurately than designing single network for all of zones.
581			_a2
773	0		_tJournal of Petroleum Science & Engineering _g78
942			_cARTICULO
100	1		_aKaidani, Hossein _952389
100	1		_aMohebbi, Ali _952390
999			_c187032 _d187032