000			02874nam a2200313 4500
005			20260521171525.0
008			260224s2018 xxu esp
041			_aEspañol
082			_a068.82 553.28 C62 2018 0015647
100	1		_aGallart, Diego _9858
100	1		_aOrtíz, Alberto Cesar _948516
100	1		_aBernhardt, Carolina _9857
100	1		_aHryb, Damián _9706
100	1		_aRodríguez Martino, Julio César _958925
100	1		_aHorowitz, Gabriel _91736
111	2		_aCongreso de Exploración y Desarrollo de Hidrocarburos (10mo. : 2018 nov. 5 - 9 : Mendoza) _9573
111	2		_aJornadas de Geotecnología (6tas. : 2018 nov. 5 - 9 : Mendoza) _9574
245	0	0	_aAnálisis multivariado para la predicción de fluidos a partir de la mineralogía de yacimientos no convencionales
260			_c2018
270			_a04/07/2022 ; 13/05/2019
300			_a18 p. ; 9-27
520			_aMultivariate analysis. Prediction of fluids from mineralogy variables on unconventional reservoirs.The characterization of the pore spaces and volumes of fluids from data of T1T2 NMR in an unconventional reservoir allows the understanding of the characteristics of it at nanoscale. For these signals to acquire meaning and relevance in the prediction of ";sweet spots" or places of hydrocarbon interest, it is necessary to analyze them in relation to the mineralogy and content of organic matter (kerogen) of the rock.The multivariate analysis techniques used have been oriented in this direction, which has allowed to consider the influence of each parameter on the others, revealing correlations that would be very difficult to discover and analyze using traditional methods.The present work was carried out in different stages beginning by studying the correlations between different mineralogical variables, organic matter, fluids and NMR T1T2 porosity in an unconventional reservoir well belonging to YPF. The degree of correlation between variables was studied, eliminating those redundant, and 4 machine learning models were used to predict the objective variables where the smallest error obtained in the prediction was around 10%.The relative importance of the variables in the prediction was analyzed, which in some cases coincide with the existing physical models and in others they lead to rethinking some assumptions about them, providing new knowledge to this type of analysis.The models created with the well data analyzed can be tested with data from other wells to measure their efficiency and thus become a predictive tool for poral and fluid characteristics of shale reservoirs.
650		0	_aCiencia de datos _958926
650		0	_aMineralogía _9840
650		0	_aNo convencionales _9838
856			_uhttps://biblioteca.iapg.org.ar/ArchivosAdjuntos/Conexplor2018/SGeot/1632.pdf
942			_cCONGTP _2ddc
999			_c192875 _d192875