The relatively new method of using wavelets in well log analysis is a powerful tool for defining multiple superimposed scales of lithic trends and contacts. Interpreting depositional processes associated with different scales of vertical variation within well log responses allows prediction of the lateral extent of sands and the distribution of internal flow barriers important for development of oil field recovery strategies. Wavelet analysis of grain-size variations in a 2.1 km thick fluvial section including the fluvial Chinji and Nagri Formations, northern Pakistan, revealed three major wavelengths. Reliability of the wavelength values was tested and confirmed by multiple sectioning of the dataset. These dominant wavelengths are interpreted to reflect vertical variations within individual channels, the stacking of channel belts within overbank successions due to river avulsion, and larger-scale channel stacking patterns within this foreland basin that may reflect allocyclic influences. Wavelet analysis allows quantification of the scales of periodic vertical variations that may not be strictly cyclic in nature. Comparison of total wavelet energies over all scales for each depth to the grain size and sand percentages yielded good correlations with sand proportion curves. Although changes in the wavelet energy profile were much more distinct with respect to grain size, lithic boundaries' locations were not detected based solely on the total of the wavelet energies. The data were also analyzed using Fourier transforms. Although Fourier transforms of the data yielded the smallest scale cyclicities, the higher-order cyclicities were not defined. This comparison demonstrates the power of wavelet analysis in defining types of repetitive, but not strictly cyclic, variations that are commonly observed in the sedimentary record. Assessments of Milankovitch cyclicities were performed for the Chinji and the Nagri Formations using statistical and analytical analysis methods. A clear match between Milankovitch frequency ratios and vertical lithic variations was not observed, and thus distinct climatic control on cyclic lithological trends was not demonstrated. Analysis using wavelets to determine wavelet coefficients helps quantify characteristic scales of vertical variations, cyclicities, zone thicknesses, and locations of abrupt lithic boundaries. Wavelet analysis provides methods that could be used to help automate well log analysis.