Wavelet analysis is an image analysis technique that can extract local information at multiple scales. Because of this capability, wavelet analysis can be used to identify dominant scales in statistically heterogeneous and anisotropic random fields. We develop and test a wavelet analysis method for identifying dominant scales and orientations in permeability fields and for identifying boundaries between regions with different dominant orientations. We evaluate three different wavelets (fully anisotropic Morlet wavelet, Mexican hat wavelet, and Cauchy wavelet) and show that the Morlet wavelet is the most effective of these three wavelets in identifying dominant orientations. We also investigate the use of several different quantitative wavelet measures in identifying dominant scales and orientations in permeability fields. The technique is demonstrated using both a synthetic data set with known characteristics and a laboratory-collected permeability data set from Massillon sandstone.