--> Imaging CO<sub>2</sub> Sequestration at an Enhanced Oil Recovery Site Using the Depth-to-Surface Resistivity Method

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Imaging CO2 Sequestration at an Enhanced Oil Recovery Site Using the Depth-to-Surface Resistivity Method

Abstract

A crucial component of CO2 enhanced oil recovery is knowing where in the formation the CO2 is resident after injection. Fractures or preferential pathways may misdirect the injected CO2, causing sweep inefficiencies and non-conformance in the reservoir. For this reason, knowledge of CO2 mobility and saturation is key to improving injection efficiency. Mapping the electrical resistivity of the reservoir gives an effective solution to this problem since resistivity is highly sensitive to the fluid properties and saturation within the reservoir. Surface-based resistivity techniques have little sensitivity at reservoir depths, and traditional borehole techniques such as induction logs have limited range beyond the borehole and cannot be implemented after a well is cased. In contrast, the Depth-to-Surface Resistivity method is able to overcome both of these issues by safely energizing the metallic well casing directly. This causes current to flow along the casing, and leak into the formation along the well. By choosing where to energize along the casing, the current profile can be focused on depths of interest, allowing for improved sensitivity to target layers. Electric fields created by the flow of the current within the reservoir are measured at the surface of the earth by an array of capacitively coupled receivers. 3D inversion methods are then used to generate a subsurface electrical resistivity volume from the collected data. Accuracy of the model is enhanced through the inclusion of known information into the inversion workflow (e.g., induction logs, expected faults), and the results are correlated with existing geologic logs and seismic data. We present here an example from a CO2 flood where data was collected using Depth-to-Surface Resistivity. The resulting 3D resistivity model results have shown the ability to identify and image key features associated with CO2 concentrations. Applying this method to the survey resulted in insights about the concentration, areal conformance, and leak points of the flood.