Forecasting gas density using artificial intelligence

Abouzar Choubineh, Elias Khalafi, Riyaz Kharrat, Alireza Bahreini, Amir Hossein Hosseini

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)


Proper calculations of gas engineering require precise determination of gas properties and its associated variations with pressure and temperature. These properties can be determined by conducting experimental tests on gathered fluid samples from the bottom of the wellbore or at the surface as well as using equations of state and empirical correlations. This work is concentrated to develop a robust and quick model based on artificial network trained with teaching learning based optimization (ANN-TLBO) using 693 data sets at a wide range of pressure and temperature for gas density prediction. Comparing gas density from the predictive method and experimental results describe that the proposed ANN-TLBO model is of reliable accuracy for determining gas density. Sensitivity analysis also showed the extreme effect of temperature and pressure on gas density.
Original languageEnglish
Pages (from-to)903-909
Number of pages7
JournalPetroleum science and technology
Issue number9
Publication statusPublished - 10 Aug 2017
Externally publishedYes

Bibliographical note

Publisher Copyright: © 2017 Taylor & Francis Group, LLC.


  • Artificial neural network
  • comparing
  • gas density
  • sensitivity analysis
  • teaching learning based optimization

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