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- W4309558145 abstract "One major element in geological CO2 storage is the ability to track the CO2 migration and its related pressure buildup in the reservoir. Lack of information, especially for saline storage, requires extensive fluid flow simulations to understand risk and design mitigation plans. The ultimate goal is to run these simulations fast and accurately for visualization and demonstration to various stakeholders. We are evaluating the performance of several deep learning algorithms that use numerical simulations to train and predict the system behavior of an offshore Gulf of Mexico reservoir. The simulation responses of interest were the pressure and saturation and the water production rate. We used the global root-mean-squared error (RMSE) and forecast time as quantitative performance metrics. Comparison of different neural networks shows that pressure and saturation can be predicted with an RMSE of less than 2 psi and 0.05, respectively. In addition, models offer 50–2,000X speedup." @default.
- W4309558145 created "2022-11-28" @default.
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- W4309558145 date "2022-11-22" @default.
- W4309558145 modified "2023-10-17" @default.
- W4309558145 title "Reservoir Modeling Using Fast Predictive Machine Learning Algorithms for Geological Carbon Storage" @default.
- W4309558145 doi "https://doi.org/10.1201/9781003207009-17" @default.
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