Towards More Accurate and Explainable Supervised Learning-Based Prediction of Deliverability for Underground Natural Gas Storage

Ali, Aliyuda and Aliyuda, Kachalla and Elmitwally, Nouh and Bello, Abdulwahab Muhammad (2022) Towards More Accurate and Explainable Supervised Learning-Based Prediction of Deliverability for Underground Natural Gas Storage. Applied Energy, 327. ISSN 0306-2619

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Numerous subsurface factors, including geology and fluid properties, can affect the connectivity of the storage spaces in depleted reservoirs; hence, fluid flow simulations become more complicated, and predicting their deliverability remains challenging. This paper applies Machine Learning (ML) techniques to predict the deliverability of underground natural gas storage (UNGS) in depleted reservoirs. First, three baseline models were developed based on Support Vector Regression (SVR), Artificial Neural Network (ANN), and Random Forest (RF) algorithms. To improve the accuracy of the RF model as the best-performing baseline model, a unified framework, referred to as SARF, was developed. SARF combines the capabilities of Sparse Autoencoder (SA) and that of Random Forest (RF). To achieve this, the internal representations of the SA, which constitute extracted features of the input variables, are used in RF to develop the proposed SARF framework. The predictive capabilities of the baseline models and the proposed SARF model were validated using 3744 real-world storage data samples of 52 active storage reservoirs in the United States. The experimental result of this study shows that the proposed SARF model achieved an average 5.7% increase in accuracy on four separate data partitions over the baseline RF model. Furthermore, a set of eXplainable Artificial Intelligence (XAI) methods were developed to provide an intuitive explanation of which factors influence the deliverability of reservoir storage. The visualizations developed using the XAI method provide an easy-to-understand interpretation of how the SARF model predicted the deliverability values for separate reservoirs.

Item Type: Article
Identification Number:
2 October 2022Accepted
14 October 2022Published Online
Uncontrolled Keywords: Artificial neural network, Data-driven modeling, Interpretable machine learning. Natural gas industry, Random forests, Support vector regression
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Aliyuda Ali
Date Deposited: 13 Oct 2022 10:47
Last Modified: 21 Oct 2022 12:28

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