Numerous subsurface factors, including geology and fluid properties, can affect the connectivity of storage spaces in depleted reservoirs; hence, flow simulations become more complicated, predicting their deliverability remains challenging. This paper applies Machine Learning (ML) techniques to predict underground natural gas (UNGS) reservoirs. First, three baseline models were developed based ...