A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference system

A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference system

Density is an important property of natural gas required for the design of gas processing and reservoir simulation. Due to expensive measurement of density, industry tends to predict gas density through an EOS. However, all EOS are associated with uncertainties, especially at high-pressure condition...

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Main Authors: Amir Hossein Saeedi Dehaghani, Mohammad Hasan Badizad
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2017-12-01
Series:Petroleum
Online Access:http://www.sciencedirect.com/science/article/pii/S2405656116301213
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spelling doaj-aa88b5a2c3c74044bfdf56965adff6042021-03-02T10:02:38ZengKeAi Communications Co., Ltd.Petroleum2405-65612017-12-0134447453A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference systemAmir Hossein Saeedi Dehaghani0Mohammad Hasan Badizad1Petroleum Engineering Group, Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran; Corresponding author.Department of Chemical and Petroleum Engineering, Sharif University of Technology, Tehran, IranDensity is an important property of natural gas required for the design of gas processing and reservoir simulation. Due to expensive measurement of density, industry tends to predict gas density through an EOS. However, all EOS are associated with uncertainties, especially at high-pressure conditions. Also, using sophisticated EOS in commercial software renders simulation highly time-consuming. This work aims to evaluate performance of adaptive neuro-fuzzy inference system (ANFIS) as a widely-accepted intelligent model for prediction of P-Ï-T behavior of natural gas. Using experimental data reported in the literature, our inference system was trained with 95 data of natural gas densities in the temperature range of (250â450)K and pressures up to 150 MPa. Additionally, prediction by ANFIS was compared with those of AGA8 and GERG04 which both are leading industrial EOS for calculation of natural gas density. It was observed that ANFIS predicts natural gas density with AARD% of 1.704; and is able to estimate gas density as accurate as sophisticated EOS. The proposed model is applicable for predicting gas density in the range of (250â450) K, (10â150) MPa and also for sweet gases, i.e., containing a low concentration of N2 and CO2. Keywords: Natural gas, Density, Fuzzy inference system, Intelligent modelling, Equation of statehttp://www.sciencedirect.com/science/article/pii/S2405656116301213
collection DOAJ
language English
format Article
sources DOAJ
author Amir Hossein Saeedi Dehaghani
Mohammad Hasan Badizad
spellingShingle Amir Hossein Saeedi Dehaghani
Mohammad Hasan Badizad
A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference system
Petroleum
author_facet Amir Hossein Saeedi Dehaghani
Mohammad Hasan Badizad
author_sort Amir Hossein Saeedi Dehaghani
title A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference system
title_short A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference system
title_full A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference system
title_fullStr A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference system
title_full_unstemmed A soft computing approach for prediction of P- Ï-T behavior of natural gas using adaptive neuro-fuzzy inference system
title_sort soft computing approach for prediction of p- ï-t behavior of natural gas using adaptive neuro-fuzzy inference system
publisher KeAi Communications Co., Ltd.
series Petroleum
issn 2405-6561
publishDate 2017-12-01
description Density is an important property of natural gas required for the design of gas processing and reservoir simulation. Due to expensive measurement of density, industry tends to predict gas density through an EOS. However, all EOS are associated with uncertainties, especially at high-pressure conditions. Also, using sophisticated EOS in commercial software renders simulation highly time-consuming. This work aims to evaluate performance of adaptive neuro-fuzzy inference system (ANFIS) as a widely-accepted intelligent model for prediction of P-Ï-T behavior of natural gas. Using experimental data reported in the literature, our inference system was trained with 95 data of natural gas densities in the temperature range of (250â450)K and pressures up to 150 MPa. Additionally, prediction by ANFIS was compared with those of AGA8 and GERG04 which both are leading industrial EOS for calculation of natural gas density. It was observed that ANFIS predicts natural gas density with AARD% of 1.704; and is able to estimate gas density as accurate as sophisticated EOS. The proposed model is applicable for predicting gas density in the range of (250â450) K, (10â150) MPa and also for sweet gases, i.e., containing a low concentration of N2 and CO2. Keywords: Natural gas, Density, Fuzzy inference system, Intelligent modelling, Equation of state
url http://www.sciencedirect.com/science/article/pii/S2405656116301213
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AT mohammadhasanbadizad asoftcomputingapproachforpredictionofpitbehaviorofnaturalgasusingadaptiveneurofuzzyinferencesystem
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AT mohammadhasanbadizad softcomputingapproachforpredictionofpitbehaviorofnaturalgasusingadaptiveneurofuzzyinferencesystem
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