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Simulation of CO2 Storage using a Parameterization Method for Essential Trapping Physics: FluidFlower Benchmark Study

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An efficient compositional framework is developed for simulation of CO2 storage in saline aquifers during a full-cycle injection, migration and post-migration processes. Essential trapping mechanisms, including structural, dissolution, and residual trapping, which operate at different time scales are accurately captured in the presented unified framework. In particular, a parameterization method is proposed to efficiently describe the relevant physical processes. The proposed framework is validated by comparing the dynamics of gravity-induced convective transport with that reported in the literature. Results show good agreement for both the characteristics of descending fingers and the associated dissolution rate. The developed simulator is then applied to study the FluidFlower benchmark model. An experimental setup with heterogeneous geological layers is discretized into a two-dimensional computational domain where numerical simulation is performed. Impacts of hysteresis and the diffusion of CO2 in liquid phase on the migration and trapping of CO2 plume are investigated. Inclusion of the hysteresis effect does not affect plume migration in this benchmark model, whereas diffusion plays an important role in promoting convective mixing. This work casts a promising approach to predict the migration of the CO2 plume, and to assess the amount of trapping from different mechanisms for long-term CO2 storage.

Yuhang Wang, Ziliang Zhang, Cornelis Vuik, Hadi Hajibeygi• 2023

Related benchmarks

TaskDatasetResultRank
Multivariate ForecastingETTh1
MSE0.258
645
Multivariate Time-series ForecastingETTm1
MSE0.338
433
Multivariate ForecastingETTh2
MSE0.252
341
Multivariate Time-series ForecastingETTm2
MSE0.187
334
Multivariate Time-series ForecastingWeather
MSE0.172
276
Multivariate Time-series ForecastingTraffic
MSE0.593
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Multivariate Time-series ForecastingExchange
MAE0.234
165
Multivariate Time-series ForecastingElectricity
MSE0.168
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Multivariate time series predictionPeMS03
MSE0.085
111
Multivariate Time-series ForecastingPeMS04
MSE0.129
74
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