000			02036nab a2200205 4500
005			20260520002031.0
008			260224s2011 xxu
245	0	0	_aNeuro-simulation modeling of chemical flooding
260			_a _b _cagos. 2011
270			_a03/04/2012 ; 03/04/2012
300			_a12 p. ; 208 - 219
520			_aTranscripción del resumen del autor. Chemical flooding has proved to enhance oil recovery of reservoirs considerably. Development strategies of this method are more efficient when they consider both aspects of operation (recovery factor, RF) and economics (net present value, NPV). In this study, a multi-layer perceptron (MLP) neural network is developed for modeling of chemical flooding using surfactant and polymer via prediction of both RF and NPV in a unique model. The modeling algorithm is divided into three processes: training, generalization, and operation. In training process, the initial structure of the network is trained, and then the architecture of the trained network is optimized for reduction of prediction errors in generalization process. Furthermore, the optimum structure is compared with other methods like Radial Basis Function (RBF) neural network, quadratic and multi-objective regressions. The optimum architecture of the network contains one hidden layer with 8 neurons and training function of Bayesian regularization. In operation process, sensitivity analysis is studied for evaluating of effective parameters (inputs) on the performance of chemical flooding. The error is always less than 5% during the implementation of all processes. The results demonstrate that neuro-simulation of chemical flooding is reliable, inexpensive, fast in computational effort, and capable in accurate prediction of both RF and NPV in one model.
581			_a2
773	0		_tJournal of Petroleum Science & Engineering _g78
942			_cARTICULO
100	1		_aKarambeigi, M. S. _952301
100	1		_aZabihi, R. _952302
100	1		_aHekmat, Z. _952303
999			_c186972 _d186972