The objective of this paper is to describe and validate a new approach for acquiring images that provide both qualitative and quantitative information of the formation electrical properties using a high-resolution oil-based mud imager (HROBMI) tool. This new multifrequency imaging tool is able to function at high frequencies (in the MHz range) in oil-based muds.

To allow for the quantitative estimation of formation and mud properties from the HROBMI data, a hybrid machine-learning/inversion approach was implemented. In this hybrid approach, machine-learning models corresponding to different candidate mud properties are trained, and the resulting regression functions are stored. For a given measurement data set, predictions of these different models are used to quickly identify an optimum mud candidate. This information is then fed into an inversion algorithm that provides the accurate quantitative information on the logging environment of the HROBMI. The accuracy of this algorithm has been verified using a test fixture that enables the change of the formation properties in different mud environments.

The measurements from the HROBMI are a function of the formation properties: resistivity and permittivity, frequency, and mud properties. The hybrid algorithm can untangle HROBMI data from multiple frequencies to obtain true formation resistivity images independent of the other parameters that affect the tool measurements. In addition, the algorithm provides formation permittivity images as well as a standoff image. The results have been provided from both the controlled experiments in the test fixture and from field logs.


In the late 1960s, the first borehole imaging tool was introduced, which eliminated the necessity for "transparent mud" through the use of acoustic technology (Zemanek et al., 1968). Though this opened up the world of borehole imaging, the dynamic range of the response was still limited. Dynamic range was improved through the use of multibutton resistivity devices that first appeared in the 1980s. Whilst these evolved to provide ever-clearer images with more buttons providing greater coverage, they remained limited to conductive mud environments. Initial attempts to generate images in oil-based mud (OBM) had some success by measuring the potential difference between two electrodes from an outer set of electrodes, which passed current to the formation; however, the resolution of these tools remained below that of the microresistivity devices used in conductive mud systems.

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