Characterizing partially sealing faults - An artificial neural network approach

G. Aydinoglu, M. Bhat, T. Ertekin

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Well testing is an effective tool for characterizing hydrocarbon reservoirs. Although the forward solution of the well testing theory is well advanced, the corresponding inverse-solution protocols are not well established, especially for complex domains. A new inverse-solution method uses artificial neural network (ANN) technology to analyze pressure transient data from an anisotropic faulted reservoir. The primary goal of this work was to test the capability of the ANN methodology as an engineering tool in pressure-transient analysis.

Original languageEnglish (US)
Pages (from-to)68-69
Number of pages2
JournalJPT, Journal of Petroleum Technology
Volume55
Issue number2
DOIs
StatePublished - Feb 2003

All Science Journal Classification (ASJC) codes

  • Fuel Technology
  • Industrial relations
  • Energy Engineering and Power Technology
  • Strategy and Management

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