The high-definition oil-based-mud (OBM) imager is a pad-based microelectrical wireline tool designed to operate in wellbores filled with nonconductive mud. To complement standard composite data processing and provide quantitative interpretation, we developed a model-based parametric inversion using the Gauss-Newton algorithm that matches the measurements to an accurate and efficient forward model built by multidimensional fitting of simulated data. The inversion-based workflow allows flexible selection of model parameters to be inverted and can process logging data from multiple depths and buttons simultaneously, stabilizing the inversion, overcoming the underdetermined problem and measurement calibration limitations.

Besides producing accurate formation resistivity, the inversion improves image quality in highly resistive and fractured formations, improves consistency among the pads, and helps eliminate "blending" artifacts. The inversion also generates a tool-standoff image detailing the borehole shape and a dielectric-permittivity image, which can be valuable for standalone formation evaluation or joint interpretation with array dielectric measurements.

The inversion algorithm is applied to field data acquired in different conditions, to illustrate its potential of inversion-based workflow to further enhance interpretation of the new OBM imager. The field data examples include various complex cases with blending artifacts, large standoff, and resistive formations. The processed resistivities compare well with standard array-induction responses, and so do the computed dielectric permittivities with those derived from an array-dielectric tool. The standoff image helps characterize fractures, faults, and other natural or drilling-induced events on the borehole surface.


The application of the microelectrical imager (Luthi, 2001) has been limited to the conductive water-based fluids (WBM), conditions favorable for use of the low-frequency galvanic measurement physics. The WBM imager is able to produce high-definition images of 0.2 in. resolution and 80% circumferential coverage in an 8-in. borehole, using 192 buttons distributed over 8 pads. Conventional interpretation of the microelectrical images covers determination of structure, identification of thin beds, classification of heterogeneities, facies classification, identification of the depositional environments, fracture analysis, and use in constraining the reservoir model (Hansen and Fett, 2000; Slatt and Davis, 2010).

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