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• W3212483529 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 methods. 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." @default.
• W3212483529 created "2021-11-22" @default.
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• W3212483529 date "2022-01-01" @default.
• W3212483529 modified "2023-10-10" @default.
• W3212483529 title "Impact of data pre-processing techniques on recurrent neural network performance in context of real-time drilling logs in an automated prediction framework" @default.
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• W3212483529 doi "https://doi.org/10.1016/j.petrol.2021.109760" @default.
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