Decoupling and predicting natural gas deviation factor using machine learning methods

• Published in: Scientific reports Vol. 14; no. 1; pp. 21640 - 17
• Main Authors: Geng, Shaoyang, Zhai, Shuo, Ye, Jianwen, Gao, Yajie, Luo, Hao, Li, Chengyong, Liu, Xianshan, Liu, Shudong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/898; 639/4077/4082/4090; Algorithms; Correlation coefficient; Decomposition; Deviation factor; High temperature; Humanities and Social Sciences; Learning algorithms; Long short-term memory; Machine learning; multidisciplinary; Natural gas; Natural gas reserves; Neural networks; Oil and gas industry; Pipelines; Reservoir management; Science; Science (multidisciplinary); Support vector machines; Z-factor; Decomposition; Z-factor; Natural gas; Machine learning; Deviation factor
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-72499-5

Accurately predicting the deviation factor (Z-factor) of natural gas is crucial for the estimation of natural gas reserves, evaluation of gas reservoir recovery, and assessment of natural gas transport in pipelines. Traditional machine learning algorithms, such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory Neural Networks (BiLSTM), often lack accuracy and robustness in various situations due to their inability to generalize across different gas components and temperature-pressure conditions. To address this limitation, we propose a novel and efficient machine learning framework for predicting natural gas Z-factor. Our approach first utilizes a signal decomposition algorithm like Variational Mode Decomposition (VMD), Empirical Fourier Decomposition (EFD) and Ensemble Empirical Mode Decomposition (EEMD) to decouple the Z-factor into multiple components. Subsequently, traditional machine learning algorithms is employed to predict each decomposed Z-factor component, where combination of SVM and VMD achieved the best performance. Decoupling the Z-factors firstly and then predicting the decoupled components can significantly improve prediction accuracy of all traditional machine learning algorithms. We thoroughly evaluate the impact of the decoupling method and the number of decomposed components on the model’s performance. Compared to traditional machine learning models without decomposition, our framework achieves an average correlation coefficient exceeding 0.99 and an average mean absolute percentage error below 0.83% on 10 datasets with different natural gas components, high temperatures, and pressures. These results indicate that hybrid model effectively learns the patterns of Z-factor variations and can be applied to the prediction of natural gas Z-factors under various conditions. This study significantly advances methodologies for predicting natural gas properties, offering a unified and robust solution for precise estimations, thereby benefiting the natural gas industry in resource estimation and reservoir management.