Dividing oil fields into regions with similar characteristic behavior using neural network and fuzzy logic approaches

Masoud Nikravesh*, A. R. Kovscek, T. W. Patzek

*Corresponding author for this work

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

2 Scopus citations

Abstract

Here we present the next generation of 'intelligent' oil field surveillance and prediction software based on neural networks and fuzzy logic. We treat the entire oil field as a coupled, highly nonlinear system of water injectors and oil/water/gas producers. The oil field is divided into regions with similar characteristic behavior using neural network and fuzzy logic. Wells in each region are then modeled with specialized neural networks trained to recognize their particular behavior. The model helps to improve waterflood management, avoid reservoir damage, and increase oil recovery per unit volume of injected water. Finally, the model visualizes the global trajectory of an entire field project and allow engineers to recognize patterns of incipient reservoir damage and poor performance.

Original languageEnglish (US)
Title of host publicationNew Frontiers in Fuzzy Logic and Soft Computing
PublisherIEEE
Pages164-169
Number of pages6
StatePublished - 1996
Externally publishedYes
EventProceedings of the 1996 Biennial Conference of the North American Fuzzy Information Processing Society - NAFIPS - Berkeley, CA, USA
Duration: Jun 19 1996Jun 22 1996

Other

OtherProceedings of the 1996 Biennial Conference of the North American Fuzzy Information Processing Society - NAFIPS
CityBerkeley, CA, USA
Period06/19/9606/22/96

ASJC Scopus subject areas

  • Engineering(all)

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