The 4-D Microgravity Method for Waterflood Surveillance: A Model Study for the Prudhoe Bay Reservoir, Alaska
Hare, J. L.
Ferguson, John F.
Aiken, Carlos L. V.
Brady, J. L.
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Forward and inverse gravity modeling is carried out on a suite of reservoir simulations of a proposed water injection on the Prudhoe Bay reservoir, Alaska. A novel surveillance technique is developed in which surface gravity observations are used to monitor the progress of a gas cap waterflood in the reservoir at 8200-ft depth. The results of the modeling showed that the inversion of time-lapse gravity data is a viable technique for monitoring reservoir gas cap waterfloods. Forward and inverse gravity modeling is carried out on a suite of reservoir simulations of a proposed water injection in the Prudhoe Bay reservoir, Alaska. A novel surveillance technique is developed in which surface gravity observations are used to monitor the progress of a gas cap waterflood in the reservoir at 8200-ft (2500-m) depth. This cost-effective method requires that high-precision gravity surveys be repeated over periods of years. Differences in the gravity field with time reflect changes in the reservoir fluid densities. Preliminary field tests at Prudhoe Bay indicates survey accuracy of 5-10 μGal can be achieved for gravity data using a modified Lacoste and Romberg 'G' type meter or Scintrex CG-3M combined with the NAVSTAR Global Positioning System (GPS). Forward gravity modeling predicts variations in surface measurements of 100 μGal after 5 years of water injection, and 180-250 μGal after 15 years. We use a constrained least-squares method to invert synthetic gravity data for subsurface density distributions. The modeling procedure has been formulated and coded to allow testing of the models for sensitivity to gravity sampling patterns, noise types, and various constraints on model parameters such as density, total mass, and moment of inertia. Horizontal-feature resolution of the waterflood is about 5000 ft (1520 m) for constrained inverse models from synthetic gravity with 5 μGal standard deviation (SD) noise. The inversion method can account for total mass of injected water to within a few percent. Worst-case scenarios result from inversion of gravity data which are contaminated by high levels (greater than 10-15 μGal SD) of spatially correlated noise, in which case the total mass estimate from inverse models may over or underestimate the mass by 10-20%. The results of the modeling indicate that inversion of time-lapse gravity data is a viable technique for the monitoring of reservoir gas cap waterfloods.
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