Rock Property Prediction Ahead of the Drilling Bit Using Dynamic Time Warping and Machine Learning Regression

Identifying the lithology while drilling is a crucial part during geosteering when drilling a new well. Conventional geosteering uses extensive seismic, geological models, borehole images which are not necessarily available in an exploration context. In such challenging context, where only scarce data are available (e.g., Gamma ray (GR) log), we propose a new method for predicting logging responses ahead of the drill bit upstream of geosteering workflow. The method is based on performing machine learning regression and dynamic time warping on available well log data from neighboring wells as well as from the currently drilled well. Combining both technologies allows to reliably predict formation rock properties ahead of the drill bit and therefore enables to guide the geosteering in anticipation of future lithology changes. The prediction can be done in near real-time while drilling because the computational time of only a few minutes is largely inferior to the drilling time for such a distance, which is typically longer than 6h. We successfully applied this method to well log data from Offshore Western Australia and could predict the GR response up to 100m ahead of the drill bit. The proposed workflow is easily transposable to any other well log data.