With the arrival of Machine Learning (ML) techniques as effective alternatives to many legacy modeling steps, classical static and dynamic reservoir modeling workflows need re-adjustment. In particular, we will focus on Big Loop (BL) approaches to reservoir modeling, where subsurface disciplines create an integrated representation of the subsurface, calibrated to static and dynamic information, for reliable field development and reservoir management decision making. The commonality is

Finally, we show some of the specific ingredients of an evergreen ML-driven Big Loop workflow.


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  1. Alpak, F.O., Barton, M.D., van der Vlugt, F.F., Pirmez, C., Prather, B.E. and Tennant, S.H.
    [2010] Simplified Modeling of Turbidite Channel Reservoirs. SPE Journal, 15(2), 480–494.
    [Google Scholar]
  2. Alpak, F.O., Gray, F., Saxena, N., Dietderich, J., Hofmann, R. and Berg, S.
    [2018] A distributed paral-lel multiple-relaxation-time lattice Boltzmann method on general-purpose graphics processing units for the rapid and scalable computation of absolute permeability from high-resolution 3D micro-CT images. Computational Geosciences, published online.
    [Google Scholar]
  3. Alpak, F.O., Jennings, J.W., Gelderblom, P., Chen, C., Gao, G. and Du, K.
    [2017] A Direct Over-parameterize and Optimize Method for Stratigraphically Consistent Assisted History Matching of Object-Based Geomodels: Algorithm and Field Application. SPE Journal, 22(4), 1280–1295.
    [Google Scholar]
  4. Alpak, F.O., van Kats, F. and Hohl, D.
    [2009] Stochastic History Matching of a Deepwater Turbidite Reservoir. Proceedings of the 2009 Society of Petroleum Engineers (SPE) Reservoir Simulation Sym-posium, The Woodlands, TX.
    [Google Scholar]
  5. Alpak, F.O., Vink, J.C., Gao, G. and Mo, W.
    [2013] Techniques for effective simulation, optimization, and uncertainty quantification of the in-situ upgrading process. Journal of Unconventional Oil and Gas Resources, 3–4, 1 – 14.
    [Google Scholar]
  6. Araya-Polo, M., Alpak, F.O., Saxena, N. and Hunter, S.
    [2018a] Rapid Computation of Permeability from Micro-CT images in GPGPUs. In: European Conference on the Mathematics of Oil Recovery (ECMOR) XVI. EAGE.
    [Google Scholar]
  7. Araya-Polo, M., Dahlke, T., Frogner, C., Zhang, C., Poggio, T. and Hohl, D.
    [2017] Automated fault detection without seismic processing. The Leading Edge, 36(3), 208–214.
    [Google Scholar]
  8. Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T.
    [2018b] Deep-learning tomography. The Leading Edge, 37(1), 58–66.
    [Google Scholar]
  9. Bao, A., Gildin, E. and Zalavadia, H.
    [2018] Development Of Proxy Models For Reservoir Simulation By Sparsity Promoting Methods And Machine Learning Techniques. In: ECMOR XVI-16th European Conference on the Mathematics of Oil Recovery. EAGE.
    [Google Scholar]
  10. Centilmen, A., Ertekin, T.
    and Grader, A.S. [1999] Applications of Neural Networks in Multiwell Field Development.
    [Google Scholar]
  11. Chauhan, S., Rühaak, W., Anbergen, H., Kabdenov, A., Freise, M., Wille, T. and Sass, I.
    [2016] Phase Segmentation of X-Ray Computer Tomography Rock Images using Machine Learning Techniques: an Accuracy and Performance Study, 1–20.
    [Google Scholar]
  12. Frank, F., Liu, C., Alpak, F.O. and Riviere, B.
    [2018] A finite volume / discontinuous Galerkin method for the advective Cahn–Hilliard equation with degenerate mobility on porous domains stemming from micro-CT imaging. Computational Geosciences, 22(2), 543–563.
    [Google Scholar]
  13. Guitton, A., Wang, H. and Trainor-Guitton, W.
    [2017] Statistical imaging of faults in 3D seismic vol-umes using a machine learning approach. In: SEG Technical Program Expanded Abstracts2017. 2045–2049.
    [Google Scholar]
  14. Guyaguler, B., Horne, R.N., Rogers, L. and Rosenzweig, J.J.
    [2002] Optimization of Well Placement in a Gulf of Mexico Waterflooding Project. SPE Reservoir Evaluation and Engineering, 5(3), 229–236.
    [Google Scholar]
  15. Huang, L., Dong, X. and Clee, T.E.
    [2017] A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge, 36(3), 249–256.
    [Google Scholar]
  16. Jin, L., Alpak, F.O., van den Hoek, P., Pirmez, C., Fehintola, T., Tendo, F. and Olaniyan, E.
    [2012a] A Comparison of Stochastic Data-Integration Algorithms for the Joint History Matching of Production and Time-Lapse-Seismic Data. SPE Reservoir Evaluation and Engineering, 15(4), 498–512.
    [Google Scholar]
  17. Jin, L., Weber, D., Van den Hoek, P., Alpak, F.O. and Pirmez, C.
    [2012b] 4D seismic history matching using information from the flooded zone. First Break, 30, 55–60.
    [Google Scholar]
  18. Kaleta, M., Van Essen, G., Doren, J., Bennett, R., van Beest, B., Van den Hoek, P., Forsyth Brint, J. and Jonathan Woodhead, T.
    [2012] Coupled Static / Dynamic Modeling For Improved Uncertainty Handling. Proceedings of the 2012 EAGE Annual Conference and Exhibition, Copenhagen, Denmark.
    [Google Scholar]
  19. Lin, Y., Guthrie, G., Coblentz, D., Wang, S. and Thiagarajan, J.
    [2017] Towards real-time geologic feature detection from seismic measurements using a randomized machine-learning algorithm. In: SEG Technical Program Expanded Abstracts2017. 2143–2148.
    [Google Scholar]
  20. Waldeland, A.U. and Solberg, A.S.
    [2017] Salt Classification Using Deep Learning. In: 79th EAGE Conference and Exhibition.
    [Google Scholar]

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