Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns

This paper proposes a collection of novel deliverability prediction models for underground natural gas storage (UNGS) in salt caverns based on machine learning algorithms. Considering that the natural gas supply chain is characterized by imbalances between demand and supply on a timely basis, effective and fast models for predicting the deliverability of UNGS would not only be a valuable tool to various stakeholders but also, of great benefit in competitive natural gas marketplace. In this paper, a first step in applying machine learning algorithms to predict the deliverability of UNGS in salt caverns is proposed. To achieve this, the capability of three machine learning algorithms namely, artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF) are examined. The predictive capabilities of these methods were investigated using different monthly field storage data samples for different years with varied data samples of 36 active UNGS in salt caverns in the United States. Experimental results reveal that the machine learning models developed in this study can serve as suitable tools for predicting the deliverability of UNGS in salt caverns with different performances. Overall result shows that RF model exhibits better prediction performance with varied data partitions over ANN and SVM models . (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes the application of machine learning algorithms in predicting the deliverability of underground natural gas storage in salt caverns, and examines the capabilities of artificial neural network, support vector machine, and random forest algorithms. Experimental results show that the random forest model outperforms other models in predicting deliverability with different data partitions.