Abstract
Recurrent neural networks(RNN),which are able to capture temporal natures of a signal,are becoming more common in machine learning applied to petroleum engineering,particularly drilling.With this technology come requirements and caveats related to the input data that play a significant role on resultant models.This paper explores how data pre-processing and attribute selection techniques affect the RNN models'performance.Re-sampling and down-sampling methods are compared;imputation strategies,a problem generally omitted in published research,are explored and a method to select either last observation carried forward or linear interpolation is introduced and explored in terms of model accuracy.Case studies are performed on real-time drilling logs from the open Volve dataset published by Equinor.For a realistic evaluation,a semi-automated process is proposed for data preparation and model training and evaluation which employs a continuous learning approach for machine learning model updating,where the training dataset is being built continuously while the well is being made.This allows for accurate benchmarking of data pre-processing mediods.Included is a previously developed and updated branched custom neural network architecture that includes both recurrent elements as well as row-wise regression elements.Source code for the implementation is published on GitHub.
NSTL