Functional Network softsensor for formation porosity and water saturation in oil wells

Ahmed Adeniran*, Moustafa Elshafei, Gharib Hamada

*Corresponding author for this work

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

12 Scopus citations

Abstract

Formation porosity and water saturation play important role in evaluating potential oil reservoirs and for drafting development plans for new oil fields. This paper presents a novel method for estimating these two important parameters directly from conventional well measurements. The recently proposed Functional Networks technique is applied for rapid and accurate prediction of these parameters, using six and five basic well log measurements as data for estimating porosity and water saturation respectively. Functional network is a generalization of the conventional Feed Forward Neural Networks, which overcome many of the drawbacks of the conventional neural network techniques. The proposed functional network was trained using data gathered from two wells in the Middle East region. Results obtained from this case study using the proposed intelligent technique have shown to be fast and accurate.

Original languageEnglish
Title of host publication2009 IEEE Intrumentation and Measurement Technology Conference, I2MTC 2009
PublisherIEEE Computer Society
Pages1138-1143
Number of pages6
ISBN (Print)9781424433537
DOIs
StatePublished - 2009

Publication series

Name2009 IEEE Intrumentation and Measurement Technology Conference, I2MTC 2009

Keywords

  • Component
  • Functional Networks
  • Neural Networks
  • Porosity
  • Water saturation
  • Well log

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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