Basin-scale prediction of S-wave Sonic Logs using Machine Learning techniques from conventional logs

Abstract

S-wave velocity plays a crucial role in various applications but often remains unavailable in vintage wells. To address this practical challenge, we propose a machine learning framework utilizing an enhanced bidirectional long short-term memory algorithm for estimating S-wave sonic logs from conventional logs, including P-wave sonic, gamma ray, total porosity, and bulk density. These input logs are selected based on traditional rock physics models, integrating geological and geophysical relations existing in the data. Our study, encompassing 34 wells across diverse formations in the Delaware Basin, Texas, demonstrates the superiority of machine learning models over traditional methods like Greenberg–Castagna equations, without prior geological and geophysical information. Among these machine learning models, the enhanced bidirectional long short-term memory model with self-attention yields the highest performance, achieving an R-squared value of 0.81. Blind tests on five wells without prior geologic information validate the reliability of our approach. The estimated S-wave velocity values enable the creation of a basin-scale S-wave velocity model through interpolation and extrapolation of these prediction models. Additionally, the bidirectional long short-term memory model excels not only in predicting S-wave velocity but also in estimating S-wave reflectivity for seismic amplitude variation with offset applications in exploration seismology. In conclusion, these S-wave velocity estimates facilitate the prediction of further elastic properties, aiding in the comprehension of petrophysical and geomechanical property variations within the basin and enhancing earthquake hypocentral depth estimation.