Detection of Layer Boundaries From Array Induction Tool Responses Using Neural Networks

Summary Delineation of bed boundaries is one of the important aspects of petrophysical interpretation. Proper location of bed boundaries plays a key role in log data inversion, especially in resistivity inversion, where the subsurface formation parameters are commonly parameterized into layers of varying thickness and resistivity. Traditionally, the location of bed boundaries is chosen based on the inflection points, maximum change in slope, etc. These algorithms work well for focussed responses and reasonably well for unfocussed responses, but the algorithms fail in presence of noise and in thin-bed regions. In order to overcome these difficulties, we propose an algorithm using Artificial Neural Networks (ANN) for detection of bed boundaries. We demonstrate the applicability of the algorithm on array induction data for a synthetic Oklahoma formation model and a field data from the Gulf of Mexico. The results obtained from the proposed scheme are compared with existing bed boundary