Data for: A methodology for building a data-enclosing tunnel for automated online-feedback in simulator training
Published: 4 November 2019| Version 1 | DOI: 10.17632/bpczvpr5np.1
Contributors:
Laura Marcano, Anis Yazidi, Davide Manca, Tiina M KomulainenDescription
The data was generated randomly, based on different combinations of possible actions in the dynamic simulator K-Spice from Kongsberg Digital. In case study 1, the aim was to increase +10 % of the oil production with respect to the initial condition value. In case study 2, the aim was to decrease -10 % of the gas production with respect to the initial condition value. The data show examples of possible correct and incorrect paths that a trainee could follow trying to solve the scenarios.
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Categories
Engineering, Data Mining, Data Science, Training