Forecasting gas density using artificial intelligence
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in: Petroleum science and technology, Jahrgang 35.2017, Nr. 9, 10.08.2017, S. 903-909.
Publikationen: Beitrag in Fachzeitschrift › Artikel › Forschung › (peer-reviewed)
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TY - JOUR
T1 - Forecasting gas density using artificial intelligence
AU - Choubineh, Abouzar
AU - Khalafi, Elias
AU - Kharrat, Riyaz
AU - Bahreini, Alireza
AU - Hosseini, Amir Hossein
N1 - Publisher Copyright: © 2017 Taylor & Francis Group, LLC.
PY - 2017/8/10
Y1 - 2017/8/10
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - comparing
KW - gas density
KW - sensitivity analysis
KW - teaching learning based optimization
UR - http://www.scopus.com/inward/record.url?scp=85028618834&partnerID=8YFLogxK
U2 - 10.1080/10916466.2017.1303712
DO - 10.1080/10916466.2017.1303712
M3 - Article
AN - SCOPUS:85028618834
VL - 35.2017
SP - 903
EP - 909
JO - Petroleum science and technology
JF - Petroleum science and technology
SN - 1091-6466
IS - 9
ER -
