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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 24th International Conference, DEXA 2013, Proceedings
Pages18-25
Number of pages8
EditionPART 2
DOIs
StatePublished - 2013
Event24th International Conference on Database and Expert Systems Applications, DEXA 2013 - Prague, Czech Republic
Duration: 26 Aug 201329 Aug 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8056 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Database and Expert Systems Applications, DEXA 2013
Country/TerritoryCzech Republic
CityPrague
Period26/08/1329/08/13

Keywords

  • Gamma classifier
  • data mining
  • petroleum engineering
  • prediction

Fingerprint

Dive into the research topics of 'Multivariate prediction based on the gamma classifier: A data mining application to petroleum engineering'. Together they form a unique fingerprint.

Cite this