A data processing algorithm proposed for identiﬁcation of breakout zones in tight formations: A case study in Barnett gas shale
Due to low permeability of tight gas shale, production in commercial quantities requires effective hydraulic fracturing and horizontal drilling technologies. Therefore, understanding rock properties and earth’s stresses is an important step toward reservoir evaluation and ultimately development of these kinds of resources. Furthermore, successful production from such a complex formation is heavily dependent on selection of appropriate completion technology which requires having sufﬁcient knowledge of borehole shape or say enlarged zones. Borehole enlargements or speciﬁcally breakouts provide valuable information for evaluation of in-situ stresses and veriﬁcation of geomechanical models. Customarily used methods to identify breakouts, i.e., caliper and image logs, suffer from several limitations. In addition, good quality image logs are not usually available in shaly formations due to requirement of using oil-based mud. This led to the need for developing a new technique to identify borehole enlargement zones using petrophysical logs which are often acquired in majority of the wells. This study proposes a new multi-variable approach to identify borehole enlargement zones in tight gas shale using some petrophysical logs, mud weight and overburden stress data. This approach employs number of data processing techniques including Bayesian classiﬁcation, wavelet decomposition and data fusion to determine borehole intervals with maximumlikelihood of enlargement. This paper explains the methodology and presents its results in four study wells in Barnett gas shale. The study conﬁrms the applicability and the generalization capability of the approach in shaly formations with a signiﬁcant accuracy.