Application of pattern recognition techniques to hydrogeological modeling of mature oilfields

Leonid Sheremetov, Ana Cosultchi, Ildar Batyrshin, Jorge Velasco-Hernandez

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

Abstract

Several pattern recognition techniques are applied for hydrogeological modeling of mature oilfields. Principle component analysis and clustering have become an integral part of microarray data analysis and interpretation. The algorithmic basis of clustering - the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set - is well established. This paper discusses the motivations for and applications of these techniques to integrate water production data with other physicochemical information in order to classify the aquifers of an oilfield. Further, two time series pattern recognition techniques for basic water cut signatures are discussed and integrated within the methodology for water breakthrough mechanism identification.

Original languageEnglish
Title of host publicationPattern Recognition - Third Mexican Conference, MCPR 2011, Proceedings
Pages85-94
Number of pages10
DOIs
StatePublished - 2011
Externally publishedYes
Event3rd Mexican Conference on Pattern Recognition, MCPR 2011 - Cancun, Mexico
Duration: 29 Jun 20112 Jul 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6718 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd Mexican Conference on Pattern Recognition, MCPR 2011
Country/TerritoryMexico
CityCancun
Period29/06/112/07/11

Keywords

  • Principal component analysis
  • clustering
  • oilfield
  • time series pattern

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