Saline Aquifer Characterization for Geological Carbon Sequestration Using EnKF
- Jonghyeon Jeon (Seoul National University) | Junhee Kang (Seoul National University) | Namhoon Kim (Seoul National University) | Yi-Kyun Kwon (Kongju National University) | Jonggeun Choe (Seoul National University)
- Document ID
- International Society of Offshore and Polar Engineers
- The 28th International Ocean and Polar Engineering Conference, 10-15 June, Sapporo, Japan
- Publication Date
- Document Type
- Conference Paper
- 2018. International Society of Offshore and Polar Engineers
- ensemble Kalman filter(EnKF), history matching, geological carbon sequestration, aquifer characterization, brine extraction
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- 29 since 2007
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Geological carbon sequestration (GCS) is one of methods that can alleviate excessive amount of CO2 in the atmosphere. However, when CO2 is injected into subsurface, it can induce unwanted results such as CO2 leakage, surface deformation, and induced seismicity. Therefore, it is important to characterize properties of GCS field properly and to monitor the CO2 plume migration of injected gas. In this paper, an ensemble Kalman filter is used to characterize two types of synthetic 2D fields, high and low permeability aquifers. In order to get additional observation data for better characterization result, brine extraction is adopted. As a result, the proposed method reduces uncertainty of field performances, and predicts reliably CO2 plume migration.
Geological carbon sequestration (GCS) in deep saline aquifers can reduce excessive CO2 in the atmosphere. However, when the gas is injected into subsurface, the leakage of the injected gas, surface deformation, and induced seismicity can happen. Therefore, monitoring the CO2 behavior is important to prevent these side effects.
To predict CO2 plume migration correctly, it is necessary to character-ize the properties of GCS field. Field characterization is implemented by integrating all available data, including static and dynamic data. Static data refers to spatial data, which do not change with time, such as core measurements and well logs, whereas dynamic data refer to information that may change over time, such as bottomhole pressure (BHP).
An initial field model is typically generated from static data by geostatistical schemes (Choe, 2013), and it is subsequently updated using dynamic data through history matching based on inverse algorithms. Because of its non-linearity and non-uniqueness of history matching problems, many optimization methods have been developed to estimate the distribution of field parameters such as permeability and porosity. Gradient-based optimization methods have fast convergence but can be trapped to a local minimum. On the other hand, non-gradient-based optimization methods give a global minimum but a lot of computational cost is required.
|File Size||6 MB||Number of Pages||6|