Waterflood monitoring using borehole self-potential data

Abstract

A cross-hole imaging technique is developed to follow the evolution of an oil-water encroachment front over time during the water flooding of an oil reservoir. I simulate borehole electric potentials associated with a two-phase flow between two wells in a heterogeneous reservoir. The flow of the water phase generates a source (streaming) current density, which is responsible for the occurrence of an electrical field that can be measured remotely. I develop a least-squares static inversion algorithm, a time-lapse inversion algorithm and a differential inversion algorithm to invert a set of snapshots of the electrical potential distribution in a borehole in order to localize and monitor the position and shape of the oil-water encroachment front. I demonstrate through a synthetic case study that the magnitude of the electric signal can reach up to hundreds of millivolts, and that the position and the shape of the oil-water encroachment front can be successfully recovered while it is still tens to hundreds of meters away from the well where the sensors are located. Such information could be part of an intelligent feedback system between borehole monitoring and flow-control that optimizes injection and production in a producing reservoir.