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Fully coupled inversion on a multi-physical reservoir model – Part II: The Ketzin CO2 storage reservoir

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Abstract Reliable monitoring of CO2 storage reservoirs requires a combination of different observation methods. However, history matching is typically limited to CO2 pressure data alone. This paper presents a multi-physical… Click to show full abstract

Abstract Reliable monitoring of CO2 storage reservoirs requires a combination of different observation methods. However, history matching is typically limited to CO2 pressure data alone. This paper presents a multi-physical inversion of hydraulic pressure, CO2 pressure, CO2 arrival time and geoelectrical crosshole observations of the Ketzin pilot site for CO2 storage, Germany. Multi-physical inversion has rarely been reported for CO2 storage reservoirs. In contrast to previous studies, there is no need for pre-inversion of geophysical datasets as these are now directly included in a fully coupled manner. The deteriorating impact of structural noise is effectively mitigated by preconditioning of the observation data. A double regularisation scheme provides stability for insensitive parameters and reduces the number of required model runs during inversion. The model shows fast and stable convergence and the results provide a good fit to the multi-physical observation dataset. It has certain predictive power as the known migration direction of the CO2 plume is captured. These results clarify two long discussed issues of the Ketzin CO2 storage reservoir: (1) The pre-existing hypothesis of an existing hydraulic barrier became unsubstantial as the data series suggesting weak hydraulic communication are identified as erroneous. (2) Salt precipitation around the injection well doubles the injection overpressure compared to salt free conditions, which is equivalent to a well skin of 10. The presented framework allows to integrate various types of observations into a single multi-physical model leading to an increased confidence in the spatial permeability distribution and, in perspective, to improved predictive assessments of CO2 storage reservoirs.

Keywords: storage; inversion; reservoir; model; multi physical; co2 storage

Journal Title: International Journal of Greenhouse Gas Control
Year Published: 2018

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