An Accurate Reservoir's Bubble Point Pressure Correlation

Bubble point pressure (Pb) is essential for determining petroleum production, simulation, and reservoir characterization calculations. The Pbcan be measured from the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consu...

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Main Authors: Alakbari, F.S., Mohyaldinn, M.E., Ayoub, M.A., Muhsan, A.S., Hussein, I.A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.33137 /
Published: American Chemical Society 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128511621&doi=10.1021%2facsomega.2c00651&partnerID=40&md5=461d5835344695dcb814bc61f039ae1b
http://eprints.utp.edu.my/33137/
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spelling utp-eprints.331372022-07-06T07:56:29Z An Accurate Reservoir's Bubble Point Pressure Correlation Alakbari, F.S. Mohyaldinn, M.E. Ayoub, M.A. Muhsan, A.S. Hussein, I.A. Bubble point pressure (Pb) is essential for determining petroleum production, simulation, and reservoir characterization calculations. The Pbcan be measured from the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. Therefore, some studies used alternative methods, namely, empirical correlations and machine learning techniques, to obtain the Pb. However, the previously published methods have restrictions like accuracy, and some use specific data to build their models. In addition, most of the previously published models have not shown the proper relationships between the features and targets to indicate the correct physical behavior. Therefore, this study develops an accurate and robust correlation to obtain the Pbapplying the Group Method of Data Handling (GMDH). The GMDH combines neural networks and statistical methods that generate relationships among the feature and target parameters. A total of 760 global datasets were used to develop the GMDH model. The GMDH model is verified using trend analysis and indicates that the GMDH model follows all input parameters' exact physical behavior. In addition, different statistical analyses were conducted to investigate the GMDH and the published models' robustness. The GMDH model follows the correct trend for four input parameters (gas solubility, gas specific gravity, oil specific gravity, and reservoir temperature). The GMDH correlation has the lowest average percent relative error, root mean square error, and standard deviation of 8.51, 12.70, and 0.09, respectively, and the highest correlation coefficient of 0.9883 compared to published models. The different statistical analyses indicated that the GMDH is the first rank model to accurately and robustly predict the Pb © 2022 American Chemical Society. All rights reserved. American Chemical Society 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128511621&doi=10.1021%2facsomega.2c00651&partnerID=40&md5=461d5835344695dcb814bc61f039ae1b Alakbari, F.S. and Mohyaldinn, M.E. and Ayoub, M.A. and Muhsan, A.S. and Hussein, I.A. (2022) An Accurate Reservoir's Bubble Point Pressure Correlation. ACS Omega, 7 (15). pp. 13196-13209. http://eprints.utp.edu.my/33137/
institution Universiti Teknologi Petronas
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description Bubble point pressure (Pb) is essential for determining petroleum production, simulation, and reservoir characterization calculations. The Pbcan be measured from the pressure-volume-temperature (PVT) experiments. Nonetheless, the PVT measurements have limitations, such as being costly and time-consuming. Therefore, some studies used alternative methods, namely, empirical correlations and machine learning techniques, to obtain the Pb. However, the previously published methods have restrictions like accuracy, and some use specific data to build their models. In addition, most of the previously published models have not shown the proper relationships between the features and targets to indicate the correct physical behavior. Therefore, this study develops an accurate and robust correlation to obtain the Pbapplying the Group Method of Data Handling (GMDH). The GMDH combines neural networks and statistical methods that generate relationships among the feature and target parameters. A total of 760 global datasets were used to develop the GMDH model. The GMDH model is verified using trend analysis and indicates that the GMDH model follows all input parameters' exact physical behavior. In addition, different statistical analyses were conducted to investigate the GMDH and the published models' robustness. The GMDH model follows the correct trend for four input parameters (gas solubility, gas specific gravity, oil specific gravity, and reservoir temperature). The GMDH correlation has the lowest average percent relative error, root mean square error, and standard deviation of 8.51, 12.70, and 0.09, respectively, and the highest correlation coefficient of 0.9883 compared to published models. The different statistical analyses indicated that the GMDH is the first rank model to accurately and robustly predict the Pb © 2022 American Chemical Society. All rights reserved.
format Article
author Alakbari, F.S.
Mohyaldinn, M.E.
Ayoub, M.A.
Muhsan, A.S.
Hussein, I.A.
spellingShingle Alakbari, F.S.
Mohyaldinn, M.E.
Ayoub, M.A.
Muhsan, A.S.
Hussein, I.A.
An Accurate Reservoir's Bubble Point Pressure Correlation
author_sort Alakbari, F.S.
title An Accurate Reservoir's Bubble Point Pressure Correlation
title_short An Accurate Reservoir's Bubble Point Pressure Correlation
title_full An Accurate Reservoir's Bubble Point Pressure Correlation
title_fullStr An Accurate Reservoir's Bubble Point Pressure Correlation
title_full_unstemmed An Accurate Reservoir's Bubble Point Pressure Correlation
title_sort accurate reservoir's bubble point pressure correlation
publisher American Chemical Society
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128511621&doi=10.1021%2facsomega.2c00651&partnerID=40&md5=461d5835344695dcb814bc61f039ae1b
http://eprints.utp.edu.my/33137/
_version_ 1741197808401121280
score 11.62408

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