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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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FORCE 2020 Well well log and lithofacies dataset for machine learning competition

Authors: Bormann, Peter; Aursand, Peder; Dilib, Fahad; Manral, Surrender; Dischington, Peter;

FORCE 2020 Well well log and lithofacies dataset for machine learning competition

Abstract

This well log dataset from 118 wells in the Norwegian Sea that has been used in the FORCE 2020 machine learning competition with seismic and wells to predict the lithofacies using machine learning models. The well logs have been slightly cleaned up and partially despiked. The lithofacies and lithology interpretation has been hand crafted using skilled geoscientists (Thanks to Explocrowd for excellent work). For citation in addition to the DOI please also refer to the github repository where the documentation and trained models reside https://github.com/bolgebrygg/Force-2020-Machine-Learning-competition The original well log data comes form the Norwegian government and is provided by a NOLD 2.0 license

Keywords

, lithofacies , petrophysical, well logs, North SEA, FORCE, 2020, Machine Learnig competition, GR, NEU, DENS,, well, logs, petrophysics

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
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