A Review of AI and Machine Learning Techniques in Dew Point Pressure Prediction for Gas Condensate Systems

Volume: 11 | Issue: 01 | Year 2025 | Subscription
International Journal of Chemical Engineering and Processing
Received Date: 01/23/2025
Acceptance Date: 02/03/2025
Published On: 2025-02-13
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By: Zainulabdeen Salah Hasan

Reservoir Engineer, Midland oil Company, Iraq, Baghdad.

Abstract

Condensate gas reservoirs pose considerable management challenges due to their intricate phase behavior and the critical need for accurate predictions of dew point pressure (PDew). A precise determination of PDew is vital for optimizing production strategies, estimating reserves, and planning enhanced oil recovery operations. Traditional methods for PDew determination often involve experimental analyses that can be both costly and time-consuming, highlighting the necessity for alternative predictive models. This review consolidates recent advancements in methodologies for predicting PDew, encompassing empirical correlations, equation-of-state models, and cutting-edge artificial intelligence techniques such as neural networks and machine learning algorithms. Key studies discussed demonstrate that AI can significantly enhance the accuracy of PDew predictions through the application of genetic programming (GP), artificial neural networks (ANNs), and XGBoost across a variety of reservoir conditions. These advanced computational methods hold promising potential for improving reservoir characterization and management practices in gas condensate fields.

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How to cite this article: Zainulabdeen Salah Hasan, A Review of AI and Machine Learning Techniques in Dew Point Pressure Prediction for Gas Condensate Systems. International Journal of Chemical Engineering and Processing. 2025; 11(01): -p.

How to cite this URL: Zainulabdeen Salah Hasan, A Review of AI and Machine Learning Techniques in Dew Point Pressure Prediction for Gas Condensate Systems. International Journal of Chemical Engineering and Processing. 2025; 11(01): -p. Available from:https://journalspub.com/publication/uncategorized/article=15183

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