Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering

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Resumen

A novel associative model was developed to predict petroleum well performance after remedial treatments. This application is of interest, particularly for non-uniform oilfields such as naturally fractured ones, and can be used in decision support systems for water control or candidate well selection. The model is based on the Gamma classifier, a supervised pattern recognition model for mining patterns in data sets. The model works with multivariate inputs and outputs under the lack of available data and low-quality information sources. An experimental dataset was built based on historical data of a Mexican naturally fractured oilfield. As experimental results show, this classifier-based predictor shows competitive performance compared against other methods.

Idioma originalInglés
Título de la publicación alojadaDatabase and Expert Systems Applications - 24th International Conference, DEXA 2013, Proceedings
Páginas18-25
Número de páginas8
EdiciónPART 2
DOI
EstadoPublicada - 2013
Evento24th International Conference on Database and Expert Systems Applications, DEXA 2013 - Prague, República Checa
Duración: 26 ago. 201329 ago. 2013

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NúmeroPART 2
Volumen8056 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia24th International Conference on Database and Expert Systems Applications, DEXA 2013
País/TerritorioRepública Checa
CiudadPrague
Período26/08/1329/08/13

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