Development of new gas viscosity correlations

Kadhem S. Al-Nasser*, Muhammad A. Al-Marhoun

*Corresponding author for this work

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

7 Scopus citations

Abstract

Most of the existing correlations for estimating gas viscosity were developed in mid 60's and 70's of the last century. Limited number of data was used to develop them and their accuracies are questionable. Predicting accurate gas viscosity is extremely important in the oil and gas industry as it has a major impact on reservoir recovery, fluid flow, deliverability, and well storage. In this study, a new correlation has been introduced. This correlation is simpler, features higher accuracy, and uses fewer coefficients compared with the existing correlations. Its application covers a wider range of gas specific gravity without jeopardizing the accuracy of the correlation. Another model was built using Artificial Neural Networks, ANN in order to compare its results with those obtained from the new correlation. The existing correlations were studied and analyzed using the same, large set of measured data used for this study. Most of these correlations suffered from high errors and thus were optimized using the linear and non-linear regressions. New set of coefficients for these correlations are recalculated for which the accuracy has significantly improved. In spite of such an improvement, the new correlation and new ANN model outperform the existing correlations.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE Production and Operations Symposium 2012
PublisherSociety of Petroleum Engineers (SPE)
Pages119-137
Number of pages19
ISBN (Print)9781622761272
DOIs
StatePublished - 2012

Publication series

NameSPE Production and Operations Symposium, Proceedings
Volume1

ASJC Scopus subject areas

  • General Engineering

Fingerprint

Dive into the research topics of 'Development of new gas viscosity correlations'. Together they form a unique fingerprint.

Cite this