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Volume 14
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10.3390/pr14182956
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Article

Experimental Measurement and Artificial Neural Network Prediction of Dew Point Pressure for Ultra-Deep Condensate Gas

by
Yu Zhang
1,2,
Ao Li
3,*,
Ke Zhang
1,2,4,
Yaoze Cheng
1,2,
Jiahao Gao
1,2 and
Zhenlong Song
1,2,4
1
State Key Laboratory of Enhanced Oil and Gas Recovery, Beijing 100083, China
2
Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, China
3
Natural Gas Research Institute, Shaanxi Yanchang Petroleum (Group) Co., Ltd., Xi′an 710075, China
4
Institute of Porous Flow & Fluid Mechanics, Chinese Academy of Sciences, Langfang 065007, China
*
Author to whom correspondence should be addressed.
Processes 2026, 14(18), 2956; https://doi.org/10.3390/pr14182956
Submission received: 11 August 2026 / Revised: 14 September 2026 / Accepted: 15 September 2026 / Published: 17 September 2026
(This article belongs to the Section AI-Enabled Process Engineering)
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Abstract

The development of oil and gas resources in global petroliferous basins has extended from shallow to deep reservoirs. Dew point pressure (Pd) is a vital parameter for fluid characterization and field development. Accurately and quickly obtaining Pd is crucial for the development of ultra-deep condensate gas reservoirs. The objective of this work is to predict the Pd of condensate gas by an artificial neural network (ANN) model. Ten ultra-deep condensate gas samples were analyzed using an experimental method and the Pd at reservoir temperature was obtained. A total of 113 datasets including 103 collected datasets and 10 measured datasets were adopted for ANN model training and testing. The results show that the average absolute percent relative error (AAPRE) of the developed ANN model between the measured and predicted values on the test set was 4.9589%. The predicted accuracy between the ANN model and widely used equations of state was compared. The results of statistical and graphical analysis show that the ANN model achieves the minimum prediction error. This ANN model can provide the necessary guidance for predicting the Pd for the development of different kinds of reservoirs.
Keywords: ultra-deep reservoir; condensate gas; dew point pressure; phase behavior; artificial neural network ultra-deep reservoir; condensate gas; dew point pressure; phase behavior; artificial neural network

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MDPI and ACS Style

Zhang, Y.; Li, A.; Zhang, K.; Cheng, Y.; Gao, J.; Song, Z. Experimental Measurement and Artificial Neural Network Prediction of Dew Point Pressure for Ultra-Deep Condensate Gas. Processes 2026, 14, 2956. https://doi.org/10.3390/pr14182956

AMA Style

Zhang Y, Li A, Zhang K, Cheng Y, Gao J, Song Z. Experimental Measurement and Artificial Neural Network Prediction of Dew Point Pressure for Ultra-Deep Condensate Gas. Processes. 2026; 14(18):2956. https://doi.org/10.3390/pr14182956

Chicago/Turabian Style

Zhang, Yu, Ao Li, Ke Zhang, Yaoze Cheng, Jiahao Gao, and Zhenlong Song. 2026. "Experimental Measurement and Artificial Neural Network Prediction of Dew Point Pressure for Ultra-Deep Condensate Gas" Processes 14, no. 18: 2956. https://doi.org/10.3390/pr14182956

APA Style

Zhang, Y., Li, A., Zhang, K., Cheng, Y., Gao, J., & Song, Z. (2026). Experimental Measurement and Artificial Neural Network Prediction of Dew Point Pressure for Ultra-Deep Condensate Gas. Processes, 14(18), 2956. https://doi.org/10.3390/pr14182956

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