Characterizing partially sealing faults - An artificial neural network approach

G. Aydinoglu, M. Bhat, T. Ertekin

Research output: Contribution to journalArticle

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
StatePublished - Jan 1 2003

Fingerprint

Well testing
Neural networks
Transient analysis
Hydrocarbons
Fault
Artificial neural network
Methodology
Theory testing
Testing

All Science Journal Classification (ASJC) codes

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

Cite this

Aydinoglu, G. ; Bhat, M. ; Ertekin, T. / Characterizing partially sealing faults - An artificial neural network approach. In: JPT, Journal of Petroleum Technology. 2003 ; Vol. 55, No. 2. pp. 68-69.
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Characterizing partially sealing faults - An artificial neural network approach. / Aydinoglu, G.; Bhat, M.; Ertekin, T.

In: JPT, Journal of Petroleum Technology, Vol. 55, No. 2, 01.01.2003, p. 68-69.

Research output: Contribution to journalArticle

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