Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency

Ahmad Muhammad Salisu; Ibrahim Ayuba; Abdulrahman Abdulrasheed; Abubakar Usman

doi:doi:10.11648/j.pse.20250902.12

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Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency

Ahmad Muhammad Salisu^*

, Ibrahim Ayuba

, Abdulrahman Abdulrasheed

, Abubakar Usman

Published in Petroleum Science and Engineering (Volume 9, Issue 2)

Received: 21 June 2025 Accepted: 4 July 2025 Published: 30 July 2025

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Abstract

Liquid loading in gas condensate wells drastically lowers gas production and increases operating expenses if unmanaged. The traditional empirical model often has difficulty representing the complex behaviours of multiphase flow and typically rely solely on historical data. In contrast, this study introduces a novel machine learning approach using a non-linear regression that integrates both historical and live data to predict liquid loading events in gas condensate wells with greater precision and adaptability. The newly developed machine learning Algorithm exhibited a very significant performance achieving an RMSE of 1.1293Mscf/d, MSE of 1.561 and R2 of 0.9978. The results surpass other machine learning approaches including the hybrid model with an RMSE of 2.8639 and R2 of 0.9978 and the Feed forward neural network, which have the value of R2 of 0.9833 respectively. The model’s streamlined architecture requires moderate data volume and low computational power making it suitable for real time monitoring and seamless integration into digital oil field systems which improves usability. Also, its accuracy relies on high-quality data input, highlighting the importance of a strong sensor network. With lower computing power requirements and the ability to adjust to different field conditions, this makes it a practical, scalable tool and a cost-effective solution that improves decision-making in oil and gas field operations through insight based on data. This dual data driven approach offers a practical advancement over existing models, significantly contributing to the optimization of hydrocarbon recovery.

Published in	Petroleum Science and Engineering (Volume 9, Issue 2)
DOI	10.11648/j.pse.20250902.12
Page(s)	55-66
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), 2025. Published by Science Publishing Group

Keywords

Machine Learning, Gas-well, Liquid-loading, Liquid Prediction, Gas-condensate, Efficiency

References

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APA Style

Salisu, A. M., Ayuba, I., Abdulrasheed, A., Usman, A. (2025). Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency. Petroleum Science and Engineering, 9(2), 55-66. https://doi.org/10.11648/j.pse.20250902.12

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ACS Style

Salisu, A. M.; Ayuba, I.; Abdulrasheed, A.; Usman, A. Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency. Pet. Sci. Eng. 2025, 9(2), 55-66. doi: 10.11648/j.pse.20250902.12

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AMA Style

Salisu AM, Ayuba I, Abdulrasheed A, Usman A. Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency. Pet Sci Eng. 2025;9(2):55-66. doi: 10.11648/j.pse.20250902.12

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@article{10.11648/j.pse.20250902.12,
  author = {Ahmad Muhammad Salisu and Ibrahim Ayuba and Abdulrahman Abdulrasheed and Abubakar Usman},
  title = {Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency
},
  journal = {Petroleum Science and Engineering},
  volume = {9},
  number = {2},
  pages = {55-66},
  doi = {10.11648/j.pse.20250902.12},
  url = {https://doi.org/10.11648/j.pse.20250902.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20250902.12},
  abstract = {Liquid loading in gas condensate wells drastically lowers gas production and increases operating expenses if unmanaged. The traditional empirical model often has difficulty representing the complex behaviours of multiphase flow and typically rely solely on historical data. In contrast, this study introduces a novel machine learning approach using a non-linear regression that integrates both historical and live data to predict liquid loading events in gas condensate wells with greater precision and adaptability. The newly developed machine learning Algorithm exhibited a very significant performance achieving an RMSE of 1.1293Mscf/d, MSE of 1.561 and R2 of 0.9978. The results surpass other machine learning approaches including the hybrid model with an RMSE of 2.8639 and R2 of 0.9978 and the Feed forward neural network, which have the value of R2 of 0.9833 respectively. The model’s streamlined architecture requires moderate data volume and low computational power making it suitable for real time monitoring and seamless integration into digital oil field systems which improves usability. Also, its accuracy relies on high-quality data input, highlighting the importance of a strong sensor network. With lower computing power requirements and the ability to adjust to different field conditions, this makes it a practical, scalable tool and a cost-effective solution that improves decision-making in oil and gas field operations through insight based on data. This dual data driven approach offers a practical advancement over existing models, significantly contributing to the optimization of hydrocarbon recovery.},
 year = {2025}
}

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TY  - JOUR
T1  - Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency

AU  - Ahmad Muhammad Salisu
AU  - Ibrahim Ayuba
AU  - Abdulrahman Abdulrasheed
AU  - Abubakar Usman
Y1  - 2025/07/30
PY  - 2025
N1  - https://doi.org/10.11648/j.pse.20250902.12
DO  - 10.11648/j.pse.20250902.12
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 55
EP  - 66
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20250902.12
AB  - Liquid loading in gas condensate wells drastically lowers gas production and increases operating expenses if unmanaged. The traditional empirical model often has difficulty representing the complex behaviours of multiphase flow and typically rely solely on historical data. In contrast, this study introduces a novel machine learning approach using a non-linear regression that integrates both historical and live data to predict liquid loading events in gas condensate wells with greater precision and adaptability. The newly developed machine learning Algorithm exhibited a very significant performance achieving an RMSE of 1.1293Mscf/d, MSE of 1.561 and R2 of 0.9978. The results surpass other machine learning approaches including the hybrid model with an RMSE of 2.8639 and R2 of 0.9978 and the Feed forward neural network, which have the value of R2 of 0.9833 respectively. The model’s streamlined architecture requires moderate data volume and low computational power making it suitable for real time monitoring and seamless integration into digital oil field systems which improves usability. Also, its accuracy relies on high-quality data input, highlighting the importance of a strong sensor network. With lower computing power requirements and the ability to adjust to different field conditions, this makes it a practical, scalable tool and a cost-effective solution that improves decision-making in oil and gas field operations through insight based on data. This dual data driven approach offers a practical advancement over existing models, significantly contributing to the optimization of hydrocarbon recovery.
VL  - 9
IS  - 2
ER  -

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Author Information

Ahmad Muhammad Salisu

Department of Petroleum Engineering, Faculty of Engineering and Engineering Technology, Abubakar Tafawa Balewa University, Bauchi, Nigeria

Contact Email

http://orcid.org/0009-0007-4699-7968
Ibrahim Ayuba

Department of Petroleum Engineering, Faculty of Engineering and Engineering Technology, Abubakar Tafawa Balewa University, Bauchi, Nigeria

http://orcid.org/0000-0001-6887-6808
Abdulrahman Abdulrasheed

Department of Petroleum Engineering, Faculty of Engineering and Engineering Technology, Abubakar Tafawa Balewa University, Bauchi, Nigeria

http://orcid.org/0000-0002-9662-8980
Abubakar Usman

Department of Petroleum Engineering, Faculty of Engineering and Engineering Technology, Abubakar Tafawa Balewa University, Bauchi, Nigeria

http://orcid.org/0009-0003-4827-3614

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APA Style

Salisu, A. M., Ayuba, I., Abdulrasheed, A., Usman, A. (2025). Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency. Petroleum Science and Engineering, 9(2), 55-66. https://doi.org/10.11648/j.pse.20250902.12

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ACS Style

Salisu, A. M.; Ayuba, I.; Abdulrasheed, A.; Usman, A. Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency. Pet. Sci. Eng. 2025, 9(2), 55-66. doi: 10.11648/j.pse.20250902.12

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AMA Style

Salisu AM, Ayuba I, Abdulrasheed A, Usman A. Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency. Pet Sci Eng. 2025;9(2):55-66. doi: 10.11648/j.pse.20250902.12

Copy | Download

@article{10.11648/j.pse.20250902.12,
  author = {Ahmad Muhammad Salisu and Ibrahim Ayuba and Abdulrahman Abdulrasheed and Abubakar Usman},
  title = {Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency
},
  journal = {Petroleum Science and Engineering},
  volume = {9},
  number = {2},
  pages = {55-66},
  doi = {10.11648/j.pse.20250902.12},
  url = {https://doi.org/10.11648/j.pse.20250902.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pse.20250902.12},
  abstract = {Liquid loading in gas condensate wells drastically lowers gas production and increases operating expenses if unmanaged. The traditional empirical model often has difficulty representing the complex behaviours of multiphase flow and typically rely solely on historical data. In contrast, this study introduces a novel machine learning approach using a non-linear regression that integrates both historical and live data to predict liquid loading events in gas condensate wells with greater precision and adaptability. The newly developed machine learning Algorithm exhibited a very significant performance achieving an RMSE of 1.1293Mscf/d, MSE of 1.561 and R2 of 0.9978. The results surpass other machine learning approaches including the hybrid model with an RMSE of 2.8639 and R2 of 0.9978 and the Feed forward neural network, which have the value of R2 of 0.9833 respectively. The model’s streamlined architecture requires moderate data volume and low computational power making it suitable for real time monitoring and seamless integration into digital oil field systems which improves usability. Also, its accuracy relies on high-quality data input, highlighting the importance of a strong sensor network. With lower computing power requirements and the ability to adjust to different field conditions, this makes it a practical, scalable tool and a cost-effective solution that improves decision-making in oil and gas field operations through insight based on data. This dual data driven approach offers a practical advancement over existing models, significantly contributing to the optimization of hydrocarbon recovery.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Predicting Liquid Loading in Gas Condensate Wells Using Machine Learning to Enhance Production Efficiency

AU  - Ahmad Muhammad Salisu
AU  - Ibrahim Ayuba
AU  - Abdulrahman Abdulrasheed
AU  - Abubakar Usman
Y1  - 2025/07/30
PY  - 2025
N1  - https://doi.org/10.11648/j.pse.20250902.12
DO  - 10.11648/j.pse.20250902.12
T2  - Petroleum Science and Engineering
JF  - Petroleum Science and Engineering
JO  - Petroleum Science and Engineering
SP  - 55
EP  - 66
PB  - Science Publishing Group
SN  - 2640-4516
UR  - https://doi.org/10.11648/j.pse.20250902.12
AB  - Liquid loading in gas condensate wells drastically lowers gas production and increases operating expenses if unmanaged. The traditional empirical model often has difficulty representing the complex behaviours of multiphase flow and typically rely solely on historical data. In contrast, this study introduces a novel machine learning approach using a non-linear regression that integrates both historical and live data to predict liquid loading events in gas condensate wells with greater precision and adaptability. The newly developed machine learning Algorithm exhibited a very significant performance achieving an RMSE of 1.1293Mscf/d, MSE of 1.561 and R2 of 0.9978. The results surpass other machine learning approaches including the hybrid model with an RMSE of 2.8639 and R2 of 0.9978 and the Feed forward neural network, which have the value of R2 of 0.9833 respectively. The model’s streamlined architecture requires moderate data volume and low computational power making it suitable for real time monitoring and seamless integration into digital oil field systems which improves usability. Also, its accuracy relies on high-quality data input, highlighting the importance of a strong sensor network. With lower computing power requirements and the ability to adjust to different field conditions, this makes it a practical, scalable tool and a cost-effective solution that improves decision-making in oil and gas field operations through insight based on data. This dual data driven approach offers a practical advancement over existing models, significantly contributing to the optimization of hydrocarbon recovery.
VL  - 9
IS  - 2
ER  -

Copy | Download