Bayesian Algorithm Opens Way to Wellbore Stability

Abstract Breakouts provide valuable information with respect to evaluation of maximum horizontal stress magnitude and also verification of the geomechanical model built for a field. Caliper and image logs are routinely used to identify borehole enlargement. However, these methods are limited in their applications in many instances. In addition, good quality image logs are not usually available in old fields. This led to the need for development of a new approach to identify borehole breakouts. 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 breakouts 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 statistical classifiers, and wavelet de-noising to determine borehole intervals with maximum likelihood of enlargement. The results showed that analyzing de-noised petrophysical logs with Bayesian classifier enables identification of breakouts with a significant accuracy. This paper explains the methodology and presents the 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 reasonable accuracy. Introduction The integrity of the wellbore plays an important role in petroleum operations including drilling, completion and production. Wellbore failure occurs principally through changes in the original stress state due to drilling the rock that concentrates stresses around a wellbore. If the new stresses exceed the rock strength, breakout will form around the borehole. Borehole enlargement may lead to difficulties removing cuttings and if severe can result in borehole collapse. The same factors contribute to the risk of solid production during the producing life of the well. To complete a successful wellbore stability analysis or sanding study, building a reliable geomechanical model is a basic requirement. Breakouts are valuable information to calibrate and verify a geomechanical model. In addition, they provide final borehole shape which is a critical factor in completion and production optimization. This study aims in identifying borehole breakout zones in carbonates from common petrophysical logs using data processing techniques such as wavelet decomposition, de-noising and statistical classifiers as an alternative for caliper and image logs which are associated with many limitations. Limitations of available methods Breakouts were first documented using 4-arm caliper data as zones that have consistent orientations in which one caliper pair indicates a borehole size that is greater than the bit size while the other caliper arm pair is in gauge (Wiprup 2001; Moos et al. 2007). Besides the caliper, several other downhole devices have been used for identifying borehole breakouts; including borehole optical televiewer (Gazaniol 1994) and both acoustic and electrical borehole imaging devices (Aoki et al. 1994; Van Oort et al. 1995; Tan et al. 1998). While caliper data are most often used in regional and field studies because they are widely available, acoustic imaging tools are considered the best devices for identifying breakouts and distinguishing them from other types of borehole elongations. Figure 1 illustrates a comparison between electrical and acoustic image logs' quality in breakout identification in an identical interval. As can be seen, the acoustic log displays very well-defined breakouts, whilst, electrical image logs do not show any visible breakout.