The new high-definition oil-based mud (OBM) imager is a pad-based microelectrical imager operating at high frequency to establish capacitive contact with the formation in wellbores filled with nonconductive mud. From multiple modes of operation, formation resistivity-like images are generated using an efficient composite data-processing scheme that approximates formation resistivity either by filtering or applying a correction to minimize the contribution of the OBM to the measured signal. Data from the different modes are ?blended? based on estimated formation parameters to generate an optimized image. This approach requires some knowledge of mud electrical properties.

In addition to the composite processing scheme, we also developed a model-based parametric inversion for quantitative interpretation. The Gauss-Newton algorithm matches the measurements to an accurate computationally efficient approximate forward model built by multidimensional fitting of the data generated using a finite-element simulation. The workflow overcomes the underdetermined inversion problem and calibration limitations of the measurements. The inversion allows flexible model definition and parameterization, including refinement of the calibration, and can process intervals of logging data and measurements from multiple buttons simultaneously. The workflow stabilizes the inversion and improves the consistency of the processed results. To overcome the underdetermined nature of the problem and speed up the inversion, we use a sequence of inversion runs to first iteratively estimate the mud properties for a small depth section of the log; this estimate is then used to invert for the button standoff and the formation resistivity and permittivity for longer data sections.