A data-driven reservoir simulation for natural gas reservoirs

Abstract

Physics-based reservoir simulation is the backbone of many decision-making processes in the oil and gas industry. However, due to being computationally demanding, simulating a model multiple times in iterative studies, such as history matching and production optimisation, is extremely time intensive. This downside results in it being practically impossible to update the model every time a set of new data are available. One of the popular solutions for this problem is creating a proxy model of the desired reservoir. However, the consequence of this approach is that such a proxy model can only represent one corresponding reservoir, and, for every new reservoir, a new proxy model must be rebuilt. Additionally, when the overall runtime is considered, it can be observed that, in some cases, iteratively running a numerical reservoir simulation may be quicker than the process of building, validating and using a proxy model. To overcome this obstacle, in this study, we used deep learning to create a data-driven simulator, deep net simulator (DNS), that enables us to simulate a wide range of reservoirs. Unlike the conventional proxy approach, we collected the training data from multiple reservoirs with completely different configurations and settings. We compared the precision and reliability of DNS with a commercial simulator for 600 generated case studies, consisting of 500,000,000 data points. DNS successfully predicts 45%, 70% and 90% of the cases with a mean absolute percentage error of less than 5%, 10% and 15%, respectively. Due to the indirect dependency of DNS on the initial and boundary conditions, DNS acts incredibly fast when compared with physics-based simulators. Our results showed that DNS is, on average, 9.25E+7 times faster than a commercial simulator.