Numerical studies are presented to evaluate the sensitivity of surface and surface to borehole Controlled Source Electromagnetic (CSEM) methods, to monitor the evolution of a steam plume injection in EOR for the Ratqa heavy oil reservoir, in North Kuwait.

A surface CSEM dataset collected over a pilot area in 2011 was used to determine a baseline 3D model of electrical resistivity at reservoir depth. To this end, the data underwent constrained 3D inversion, incorporating a-priori information from resistivity well logs and seismic horizons and focusing the inversion within the reservoir. The resulting 3D model provided high resolution of the lateral and vertical distribution of electrical resistivity within the reservoir, which was further verified by comparison with direct interpolation of well log data available over the entire area of the survey. Perturbations of the reservoir resistivity were then introduced by arbitrarily lowering the resistivity in a thin disk defined around a test wellbore, such as to represent a steam plume homogeneously expanding away from the borehole. Datasets were then numerically simulated to determine the expected response of EM measurements performed by surface deployed receivers and excited by sources deployed both on the surface and in the borehole, within the reservoir section. It was found that surface CSEM data could be used to recover the resistivity anomaly produced by the steam injection, provided that the injected steam generates a plume with a radius > 60. Smaller plumes produce a surface response which was close to the measurement's noise level expected in a 4D time-lapse survey. A STB-EM configuration was found to yield a strong sensitivity of the response even for radius of the plume < 20m.

At the outset the analysis presented here shows that resolution of surface based deployments is low, but they can be used to determine large scale variations within the reservoir. Novel and recently developed STB-EM technologies can yield the resolution required of the length scales variations expected in EOR processes.

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