A combined Bayesian–wavelet–data fusion approach for borehole enlargement identification in carbonates

Petrophysical logs are usually acquired in most of the drilled wells and some of them have good correlations with mechanical properties of the rock. In this paper, a new multi-variable workflow is proposed in order to identify the location of borehole enlargements along the wellbore in correlation with some of the petrophysical logs acquired using wireline or logging while drilling tools in addition to mud weight and in-situ vertical stress data. This approach employs number of data processing techniques including Bayesian classification, wavelet de-noising, and data fusion to determine borehole intervals with maximum likelihood of enlargement. The application of the proposed method is to identify enlargement zones and does not provide information about stresses orientations and magnitudes. This paper explains the methodology and presents its results in five study wells in a carbonate field. The study confirms the applicability and the generalization capability of the method in carbonate formations with a significant accuracy.