Automated detection of planar geologic features in borehole images

ABSTRACTThe interpretation of borehole images begins with the detection and classification of features—a time-consuming manual process subject to variations between interpreters. In seeking to automate the detection part for the most frequently picked features (which in circumferential images from clastic rock environments are sinusoids corresponding to planar or subplanar bedding surfaces and fractures), it is not necessary to pick all instances, but it is necessary to pick sufficient representative instances to satisfy the interpretation objective, accounting for a broad range of apparent dips, and allowing for the likelihood of fractures crossing bedding surfaces. A key challenge in this context is the minimization of false picks, as manual corrections would potentially negate the principal benefit of automation. A fast nonsubjective method is described for the detection of prominent discontinuities and the calculation of associated dip angles. It combines a gradient based approach for edge detection w...

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