Energy Reports (Nov 2022)
Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals
Abstract
This study presents a method to develop a series of unique deliverability smart models for underground natural gas storage (UNGS) in different types of target formations. The natural gas supply loop is defined by periodic mismatches between demand and supply. Efficient and faster approaches for forecasting UNGS deliverability may not only assist stakeholders but also the competitive natural gas industry. Due to this fact, this article suggests a series of robust deliverability estimation models for 387 UNGS sites in depleted fields, aquifers, and salt domes based on rigorous machine learning (ML) techniques. To this end, the potential of three ML algorithms, including Gaussian Process Regression (GPR), Least Squares Support Vector Machine (LSSVM), and Extra Tree (ET), is employed. To assess and compare the proposed models, statistical parameters including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) were employed. Accordingly, in the case of depleted fields, the GPR, LSSVM, and ET paradigms show overall R2, RMSE, and MAE of 0.999999998, 4.75E−06, 0.00021, and 0.00021. For salt domes, the GPR, LSSVM, and ET models indicate overall R2, RMSE, and MAE of 0.987, 0.0046, and 0.11. Finally, for aquifers, the GPR, LSSVM, and ET algorithms represent overall R2, RMSE, and MAE of 0.999999997, 7.1094E−06, and 0.0002102. The prediction performance reveals that the GPR model is superior to the LSSVM and ET models. This study found that the proposed intelligent models could be utilized as a template for fast estimating the deliverability of UNGS in depleted fields, aquifers, and salt domes with high accuracy. In the end, the outcomes of this study contribute to a deeper understanding of the critical role of machine learning in resolving the difficulty of forecasting the UNSG on cleaner production and sustainable development strategies.
