Inversion-Based Workflow for Quantitative Interpretation of the New-Generation Oil-Based-Mud Resistivity Imager

摘要

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.