000			01624nab a2200205 4500
005			20260520001850.0
008			260224s2010 xxu
245	0	0	_aEstimation of sour natural gas water content
260			_a _b _cago. 2010
270			_a20/09/2010 ; 20/09/2010
300			_a5 p. ; 156-160
520			_aTranscripción del resumen del autor. In this paper a new method based an artificial neural network (ANN) for prediction of natural gas mixture water content (NGMWC) is presented. H2S mole fraction, temperature, and pressure have been input variables of the network and NGMWC has been set as network output. Among the 136 data set 80 data have been implemented to find best ANN structure. 56 data have been used to check generalization capability of the best trained ANN. Comparisons show average absolute error (AAE) equal to 1.437 between ANN estimations and unseen experimental data. ANNs also have been compared with two commonly used correlations in gas industry. Results show ANN superiority to correlations. Especially in higher hydrogen sulfide content in spite of ANN good predictions there was considerable deviation between experimental data and common correlations. The proposed ANN model is able to estimate NGMWC as a function of hydrogen sulfide composition up to 89.6 mol%, temperatures between 50 and 350 °F and pressure from 200 up to 3500 psia.
581			_a1-2
773	0		_tJournal of Petroleum Science & Engineering _g73
942			_cARTICULO
100	1		_aShirvany, Y. _946892
100	1		_aZahedi, G. _942853
100	1		_aBashiri, M. _946893
999			_c177004 _d177004