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Joint impedance and facies inversion of time-lapse seismic data for improving monitoring of CO2 incidentally stored from CO2 EOR
Highlights Rock physics constrained seismic inversion to track injected CO2 over time. Rock physics modeling for better CO2 injection monitoring with time-lapse seismic. Reservoir litho-fluid facies probabilities to forecast CO2 saturation changes. Quantitative 4D seismic analysis verified by reservoir modeling.
Abstract Time-lapse seismic monitoring is an effective and proven technology for mapping the distribution of CO2 in a subsurface reservoir. When injected CO2 displaces other reservoir fluids, porous-medium properties are changed and thus the seismic impedance changes, causing time-lapse seismic amplitude differences in the injection zones. The analysis and interpretation of images created from these amplitude differences can provide information about reservoir architecture and the CO2 migration within the reservoir. Incorporating seismic inversion and rock physics into the interpretation of time-lapse seismic data can considerably improve the modeling and monitoring to detect and assess the location of CO2 over time. The joint inversion method presented in this paper has an integral representation of the geology in the inversion algorithm using elastic facies, which provides information about the spatial distribution of the geologic heterogeneities controlling the movement of fluids in the reservoir. The method was successfully applied to time-lapse seismic data from a mature oil field undergoing CO2 enhanced oil recovery. The estimated seismic acoustic impedances and facies reflect the characteristics of individual geologic facies and fluid conditions of the reservoir subject to CO2 injection. The probabilities estimated by the joint impedance and facies inversion for the reservoir's litho-fluid facies can be used for forecasting CO2 saturation and pressure changes within the target reservoir.
Joint impedance and facies inversion of time-lapse seismic data for improving monitoring of CO2 incidentally stored from CO2 EOR
Highlights Rock physics constrained seismic inversion to track injected CO2 over time. Rock physics modeling for better CO2 injection monitoring with time-lapse seismic. Reservoir litho-fluid facies probabilities to forecast CO2 saturation changes. Quantitative 4D seismic analysis verified by reservoir modeling.
Abstract Time-lapse seismic monitoring is an effective and proven technology for mapping the distribution of CO2 in a subsurface reservoir. When injected CO2 displaces other reservoir fluids, porous-medium properties are changed and thus the seismic impedance changes, causing time-lapse seismic amplitude differences in the injection zones. The analysis and interpretation of images created from these amplitude differences can provide information about reservoir architecture and the CO2 migration within the reservoir. Incorporating seismic inversion and rock physics into the interpretation of time-lapse seismic data can considerably improve the modeling and monitoring to detect and assess the location of CO2 over time. The joint inversion method presented in this paper has an integral representation of the geology in the inversion algorithm using elastic facies, which provides information about the spatial distribution of the geologic heterogeneities controlling the movement of fluids in the reservoir. The method was successfully applied to time-lapse seismic data from a mature oil field undergoing CO2 enhanced oil recovery. The estimated seismic acoustic impedances and facies reflect the characteristics of individual geologic facies and fluid conditions of the reservoir subject to CO2 injection. The probabilities estimated by the joint impedance and facies inversion for the reservoir's litho-fluid facies can be used for forecasting CO2 saturation and pressure changes within the target reservoir.
Joint impedance and facies inversion of time-lapse seismic data for improving monitoring of CO2 incidentally stored from CO2 EOR
Barajas-Olalde, César (author) / Mur, Alan (author) / Adams, Donald C. (author) / Jin, Lu (author) / He, Jun (author) / Hamling, John A. (author) / Gorecki, Charles D. (author)
2021-10-17
Article (Journal)
Electronic Resource
English
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