Graphical Network Based Reservoir Modelling to Quickly Use Data and Physics to Explore the Subsurface

References

1. Davis, J. V. et al.
   , 2007. Information-theoretic metric learning. pp. 209 – 2016.
   [Google Scholar]
2. Emerick, A. A. & Reynolds, A. C.
   , 2013. Ensemble smoother with multiple data assimilation. Computers and Geosciences, Volume 55, pp. 3–13.
   [Google Scholar]
3. Gilks, W., Richardson, S. & Spiegelhalter, D.
   , 1996. Markov Chain Monte Carlo in Practice.
   [Google Scholar]
4. Hotelling, H.
   , 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology.
   [Google Scholar]
5. Kahneman, D.
   , 2011. Thinking fast and slow.
   [Google Scholar]
6. Krige, D. G.
   , 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. J. of the Chem., Metal. and Mining Soc. of South Africa,52(6), pp. 119–139.
   [Google Scholar]
7. Marti, K. & Kall, P.
   , 1994. Stochastic Programming. Springer.
   [Google Scholar]
8. Mohus, E.
   , 2018. Over Budget, Over Time, and Reduced Revenue, Over and Over Again - An Analysis of the Norwegian Petroleum Industry’s Inability to Forecast Production. UiS.
   [Google Scholar]
9. Robert, C. P. & Casella, G.
   , 2004. Monte Carlo Statistical Methods. 2nd ed. Springer.
   [Google Scholar]
10. Sætrom, J.
    , 2019. The Seven Wastes in Reservoir Modelling Projects (and how to overcome them).
    [Google Scholar]
11. Silver, N.
    , 2012. The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t.
    [Google Scholar]