![]() More specific and advanced concepts of applying ML in modern reservoir simulation models are described and justified, particularly with respect to history matching and proxy models. ![]() The simulation of gas reservoirs (dry gas, wet gas, and retrograde gas-condensate) is introduced along with its fundamental concepts and governing equations. The aspiration ML technics should be capable of providing some improvements in terms of both accuracy and speed. Given the capabilities of Machine Learning (ML) and their general acceptance in recent decades, this chapter considers the application of these techniques to gas reservoir simulations. Although simple simulations often provide acceptable approximations, there is a continued desire to develop more sophisticated simulation strategies and techniques. If not, it may be highly misleading and cause substantial losses, poor estimation of ultimate recovery factor, and wasted effort. ![]() Natural gas reservoir simulation, as a physics-based numerical method, needs to be carried out with a high level of precision.
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