Machine learning to improve natural gas reservoir simulations

Abstract Natural gas reservoir simulation, as a physics-based numerical method, needs to be carried out with a high level of precision. If not, it may be highly misleading and cause substantial losses, poor estimation of ultimate recovery factor, and wasted effort. Although simple simulations often provide acceptable approximations, there is a continued desire to develop more sophisticated simulation strategies and techniques. 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. The aspiration ML technics should be capable of providing some improvements in terms of both accuracy and speed. The simulation of gas reservoirs (dry gas, wet gas, and retrograde gas-condensate) is introduced along with its fundamental concepts and governing equations. 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. Reservoir simulation assisted by machine learning is becoming increasingly applied to assess suitably of reservoirs for carbon capture and sequestration associated with enhanced gas recovery. Such applications, and the ability to improve reservoir performance via production efficiency, make ML-assisted reservoir simulation a valuable approach for improving the sustainability of natural gas reservoirs. The concepts are reinforced using a case study applying two ML models providing dew point pressure predictions for gas condensate reservoirs. Banner headline Reservoir simulation methods applied to gas reservoirs are reviewed and the key influencing variables identified. Machine Learning (ML) methods can be applied in various ways to improve the performance of gas reservoir simulations, especially in respect to history matching and proxy modeling. Additionally, ML can assist the CO2 sequestration and enhanced gas recovery, well placement optimization, production optimization, estimation of gas production, dew point prediction in gas condensate reservoirs, and pressure and rate transient analysis.