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- W1594899229 abstract "Crosswell seismic and electromagnetic data sets taken before and during CO2 flooding of an oil reservoir are inverted to produce crosswell images of the change in compressional velocity, shear velocity and electrical conductivity during a CO2 injection pilot study. A rock properties model is developed using measured log porosity, fluid saturations, pressure, temperature, bulk density, sonic velocity and electrical conductivity. The parameters of the rock properties model are found by an L1-norm simplex minimization of predicted and observed compressional velocity and density. A separate minimization using Archie's law provides parameters for modeling the relations between water saturation, porosity and the electrical conductivity. The rock properties model is used to generate relationships between changes in geophysical parameters and changes in reservoir parameters. The electrical conductivity changes are directly mapped to changes in water saturation. The estimated changes in water saturation are used with the observed changes in shear wave velocity to predict changes in reservoir pressure. The estimation of the spatial extent and amount of CO2 relies on first removing the effects of the water saturation and pressure changes from the observed compressional velocity changes, producing a residual compressional velocity change. The residual compressional velocity change is then interpreted in termsmore » of increases in the CO2 /oil ratio. Resulting images of CO2/oil ratio show CO2 rich zones that are well correlated with the location of injection perforations with the size of these zones also correlating to the amount of injected CO2. The images produced by this process are better correlated to the location and amount of injected CO2 than are any of the individual images of change in geophysical parameters.« less" @default.
- W1594899229 created "2016-06-24" @default.
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- W1594899229 date "2002-08-28" @default.
- W1594899229 modified "2023-10-04" @default.
- W1594899229 title "CO2 gas/oil ratio prediction in a multi-component reservoir bycombined seismic and electromagnetic imaging" @default.
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