Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination

Aniefiok Lawrence Ukpong; Ise Ise Ekpoudom; Eddie Achie Akpan

doi:doi:10.11648/j.ogce.20170503.11

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Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination

Aniefiok Lawrence Ukpong, Ise Ise Ekpoudom, Eddie Achie Akpan

Published in International Journal of Oil, Gas and Coal Engineering (Volume 5, Issue 3)

Received: 21 February 2017 Accepted: 6 March 2017 Published: 22 April 2017

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Abstract

The presence of water in natural gas stream is a recurring problem that the oil and gas industry have been dealing with over the years. Failure to remove water vapor from natural gas stream leads to the formation of hydrates and corrosion of critical facilities. Determination of natural gas dew point which is the temperature at which water vapor condenses out of natural gas is a tricky endeavor, hence there is need to explore modern and more effective ways of determining the dew point. In this research, data driven modeling technique is utilized to generate an expression for the Dew Point of a Natural Gas stream exiting a Molecular Sieve Dehydrator Bed. After data quality analysis, various model structures were utilized for modeling. The Autoregressive Moving Average with exogenous inputs model proved its suitability for predicting the plant output with a highest level of accuracy.

Published in	International Journal of Oil, Gas and Coal Engineering (Volume 5, Issue 3)
DOI	10.11648/j.ogce.20170503.11
Page(s)	27-33
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Data-Driven Modeling, Dehydrator, Natural Gas, Dew Point

References

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[2]	Løkken, T. V., Bersås, A., Christensen, K. O., Nygaard, C. F., and Solbraa, E." Water Content of High Pressure Natural Gas: Data, Prediction and Experience from Field", International Gas Union Research Conference, Oslo Norway, 2008, pp. 12-15.
[3]	Gandhidasan, P., Al-Farayedhi, A. A. and Al-Mubarak A. A. "Dehydration of Natural Gas Using Solid Desiccants", Energy, Vol. 26, No. 9, 2001, pp. 855-868.
[4]	Mohr, S. H. and Evans, G. M. "Long Term Forecasting of Natural Gas Production", Energy Policy, Vol. 39, No. 9, 2011, pp. 5550-5560.
[5]	Solomatine, D., See, L. M., and Abrahart, R. J. “Data-driven Modelling: Concepts, Approaches and Experiences”, Practical Hydroinformatics, Vol. 34, No. 5, 2008, pp. 17-30.
[6]	Janes, K. A., and Yaffe, M. B. “Data-driven Modelling of Signal-transduction Networks”, Nature Reviews Molecular Cell Biology, Vol. 7, No. 11, 2006, pp. 820-828.
[7]	Friedel, M. J. “A Data-driven Aapproach for Modeling Post-fire Debris-flow Volumes and their Uncertainty”, Environmental Modelling and Software, Vol. 26, No.12, 2011, pp. 1583-1598.
[8]	Barbour, S. L. and Krahn, J. “Numerical Modelling: Prediction or Process?”, Geotechnical News, Vol. 22, No.4, 2004, pp. 44-52.
[9]	Mathworks(2005). FuzzyLogicToolbox.http://www.mathworks.com/products/fuzzylogic/. Retrieved on the 20th of June 2014. pp. 1 – 22.
[10]	Kidnay, A. J. and Parrish, W. R.,. Fundamentals of natural gas processing, CRC Press, Paperback, Florida, 2006.
[11]	Lin, H., Thompson, S. M., Serbanescu-Martin, A., Wijmans, J. G., Amo, K. D., Lokhandwala, K. A. and Merkel, T. C. "Dehydration of Natural Gas Using Membranes. Part I: Composite Membranes", Journal of Membrane Science, Vol. 413, No. 19, 2012, pp. 70-81.
[12]	Ljung, L. System identification: Theory for the User, Prentice Hall PTR, Paperback, Upper Saddle River, 1999.

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APA Style

Aniefiok Lawrence Ukpong, Ise Ise Ekpoudom, Eddie Achie Akpan. (2017). Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination. International Journal of Oil, Gas and Coal Engineering, 5(3), 27-33. https://doi.org/10.11648/j.ogce.20170503.11

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ACS Style

Aniefiok Lawrence Ukpong; Ise Ise Ekpoudom; Eddie Achie Akpan. Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination. Int. J. Oil Gas Coal Eng. 2017, 5(3), 27-33. doi: 10.11648/j.ogce.20170503.11

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AMA Style

Aniefiok Lawrence Ukpong, Ise Ise Ekpoudom, Eddie Achie Akpan. Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination. Int J Oil Gas Coal Eng. 2017;5(3):27-33. doi: 10.11648/j.ogce.20170503.11

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@article{10.11648/j.ogce.20170503.11,
  author = {Aniefiok Lawrence Ukpong and Ise Ise Ekpoudom and Eddie Achie Akpan},
  title = {Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination},
  journal = {International Journal of Oil, Gas and Coal Engineering},
  volume = {5},
  number = {3},
  pages = {27-33},
  doi = {10.11648/j.ogce.20170503.11},
  url = {https://doi.org/10.11648/j.ogce.20170503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ogce.20170503.11},
  abstract = {The presence of water in natural gas stream is a recurring problem that the oil and gas industry have been dealing with over the years. Failure to remove water vapor from natural gas stream leads to the formation of hydrates and corrosion of critical facilities. Determination of natural gas dew point which is the temperature at which water vapor condenses out of natural gas is a tricky endeavor, hence there is need to explore modern and more effective ways of determining the dew point. In this research, data driven modeling technique is utilized to generate an expression for the Dew Point of a Natural Gas stream exiting a Molecular Sieve Dehydrator Bed. After data quality analysis, various model structures were utilized for modeling. The Autoregressive Moving Average with exogenous inputs model proved its suitability for predicting the plant output with a highest level of accuracy.},
 year = {2017}
}

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TY  - JOUR
T1  - Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination
AU  - Aniefiok Lawrence Ukpong
AU  - Ise Ise Ekpoudom
AU  - Eddie Achie Akpan
Y1  - 2017/04/22
PY  - 2017
N1  - https://doi.org/10.11648/j.ogce.20170503.11
DO  - 10.11648/j.ogce.20170503.11
T2  - International Journal of Oil, Gas and Coal Engineering
JF  - International Journal of Oil, Gas and Coal Engineering
JO  - International Journal of Oil, Gas and Coal Engineering
SP  - 27
EP  - 33
PB  - Science Publishing Group
SN  - 2376-7677
UR  - https://doi.org/10.11648/j.ogce.20170503.11
AB  - The presence of water in natural gas stream is a recurring problem that the oil and gas industry have been dealing with over the years. Failure to remove water vapor from natural gas stream leads to the formation of hydrates and corrosion of critical facilities. Determination of natural gas dew point which is the temperature at which water vapor condenses out of natural gas is a tricky endeavor, hence there is need to explore modern and more effective ways of determining the dew point. In this research, data driven modeling technique is utilized to generate an expression for the Dew Point of a Natural Gas stream exiting a Molecular Sieve Dehydrator Bed. After data quality analysis, various model structures were utilized for modeling. The Autoregressive Moving Average with exogenous inputs model proved its suitability for predicting the plant output with a highest level of accuracy.
VL  - 5
IS  - 3
ER  -

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Author Information

Aniefiok Lawrence Ukpong

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
Ise Ise Ekpoudom

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
Eddie Achie Akpan

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria

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Plain Text BibTeX RIS

APA Style

Aniefiok Lawrence Ukpong, Ise Ise Ekpoudom, Eddie Achie Akpan. (2017). Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination. International Journal of Oil, Gas and Coal Engineering, 5(3), 27-33. https://doi.org/10.11648/j.ogce.20170503.11

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ACS Style

Aniefiok Lawrence Ukpong; Ise Ise Ekpoudom; Eddie Achie Akpan. Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination. Int. J. Oil Gas Coal Eng. 2017, 5(3), 27-33. doi: 10.11648/j.ogce.20170503.11

Copy | Download

AMA Style

Aniefiok Lawrence Ukpong, Ise Ise Ekpoudom, Eddie Achie Akpan. Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination. Int J Oil Gas Coal Eng. 2017;5(3):27-33. doi: 10.11648/j.ogce.20170503.11

Copy | Download

@article{10.11648/j.ogce.20170503.11,
  author = {Aniefiok Lawrence Ukpong and Ise Ise Ekpoudom and Eddie Achie Akpan},
  title = {Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination},
  journal = {International Journal of Oil, Gas and Coal Engineering},
  volume = {5},
  number = {3},
  pages = {27-33},
  doi = {10.11648/j.ogce.20170503.11},
  url = {https://doi.org/10.11648/j.ogce.20170503.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ogce.20170503.11},
  abstract = {The presence of water in natural gas stream is a recurring problem that the oil and gas industry have been dealing with over the years. Failure to remove water vapor from natural gas stream leads to the formation of hydrates and corrosion of critical facilities. Determination of natural gas dew point which is the temperature at which water vapor condenses out of natural gas is a tricky endeavor, hence there is need to explore modern and more effective ways of determining the dew point. In this research, data driven modeling technique is utilized to generate an expression for the Dew Point of a Natural Gas stream exiting a Molecular Sieve Dehydrator Bed. After data quality analysis, various model structures were utilized for modeling. The Autoregressive Moving Average with exogenous inputs model proved its suitability for predicting the plant output with a highest level of accuracy.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Data-Driven Modelling of Natural Gas Dehydrators for Dew Point Determination
AU  - Aniefiok Lawrence Ukpong
AU  - Ise Ise Ekpoudom
AU  - Eddie Achie Akpan
Y1  - 2017/04/22
PY  - 2017
N1  - https://doi.org/10.11648/j.ogce.20170503.11
DO  - 10.11648/j.ogce.20170503.11
T2  - International Journal of Oil, Gas and Coal Engineering
JF  - International Journal of Oil, Gas and Coal Engineering
JO  - International Journal of Oil, Gas and Coal Engineering
SP  - 27
EP  - 33
PB  - Science Publishing Group
SN  - 2376-7677
UR  - https://doi.org/10.11648/j.ogce.20170503.11
AB  - The presence of water in natural gas stream is a recurring problem that the oil and gas industry have been dealing with over the years. Failure to remove water vapor from natural gas stream leads to the formation of hydrates and corrosion of critical facilities. Determination of natural gas dew point which is the temperature at which water vapor condenses out of natural gas is a tricky endeavor, hence there is need to explore modern and more effective ways of determining the dew point. In this research, data driven modeling technique is utilized to generate an expression for the Dew Point of a Natural Gas stream exiting a Molecular Sieve Dehydrator Bed. After data quality analysis, various model structures were utilized for modeling. The Autoregressive Moving Average with exogenous inputs model proved its suitability for predicting the plant output with a highest level of accuracy.
VL  - 5
IS  - 3
ER  -

Copy | Download