Statistical feature engineering using discrete wavelet transform: a tool for improving the prediction of compressional and shear sonic travel time logs

Abstract

This paper aims to predict compressional and shear sonic travel time logs using conventional logs that are easier and less expensive to acquire during the development cycle. The study uses a gradient boosting algorithm and incorporates a novel method of using statistical feature engineering with wavelet transform to improve prediction accuracy across thin beds in hydrocarbon reservoirs. The approach uses a discrete wavelet transform over the neutron log, a prominent feature, to identify thin layers and enhance prediction accuracy. The detailed coefficients are analyzed using Daubechies and Haar wavelets to reconstruct newly transformed data with an enhanced thin-layer signal. The Haar wavelet with three levels is the most optimum wavelet and decomposition levels. The algorithm for the reconstructed log shows an increased thin layer resolution, with an accuracy improvement of 6.8% for the prediction. The proposed approach significantly contributes to geologists, geophysicists, and reservoir technologists for reservoir characterization and safe drilling operations.