A Data-Space Approach For Well Control Optimization Under Uncertainty

References

1. Aitokhuehi, I. and Durlofsky, L.J.
   [2005] Optimizing the performance of smart wells in complex reservoirs using continuously updated geological models. Journal of Petroleum Science and Engineering, 48(3–4), 254–264.
   [Google Scholar]
2. Audet, C. and Dennis, J.E.Jr
   [2006] Mesh adaptive direct search algorithms for constrained optimization.SIAM Journal on Optimization, 17(1), 188–217.
   [Google Scholar]
3. Brouwer, D.R. and Jansen, J.D.
   [2004] Dynamic optimization of waterflooding with smart wells using optimal control theory. SPEJournal, 9(04), 391–402.
   [Google Scholar]
4. Gao, G., Zafari, M. and Reynolds, A.C.
   [2005] Quantifying uncertainty for the PUNQ-S3 problem in a Bayesian setting with RML and EnKF. Paper SPE-93324-MS, presented at SPE Reservoir Simulation Symposium, The Woodlands, Texas, 31 January-2 Feburary.
   [Google Scholar]
5. Jansen, J.D., Brouwer, D.R. and Douma, S.G.
   [2009] Closed loop reservoir management. Paper SPE-119098-MS, presented at SPE Reservoir Simulation Symposium, The Woodlands, Texas, 2–4 February.
   [Google Scholar]
6. Jansen, J.D., Brouwer, D.R., Naevdal, G. and Van Kruijsdijk, C.
   [2005] Closed-loop reservoir management.First Break, 23(1), 43–48.
   [Google Scholar]
7. Jiang, S.
   [2018] Data-space inversion with variable well controls in the prediction period. Master’s thesis, Stanford University.
   [Google Scholar]
8. Le Digabel, S.
   [2011] Algorithm 909: NOMAD: Nonlinear optimization with the MADS algorithm. ACM Transactions on Mathematical Software, 37(4), 44.
   [Google Scholar]
9. Oliver, D.S. and Chen, Y.
   [2011] Recent progress on reservoir history matching: a review. ComputationalGeosciences, 15(1), 185–221.
   [Google Scholar]
10. Oliver, D.S., Reynolds, A.C. and Liu, N.
    [2008] Inverse Theory ƒor Petroleum Reservoir Characterization and History Matching. Cambridge University Press.
    [Google Scholar]
11. Sarma, P., Durlofsky, L.J., Aziz, K. and Chen, W.H.
    [2006] Efficient real-time reservoir management using adjoint-based optimal control and model updating.Computational Geosciences, 10(1), 3–36.
    [Google Scholar]
12. Satija, A. and Caers, J.
    [2015] Direct forecasting of subsurface flow response from non-linear dynamic data by linear least-squares in canonical functional principal component space.Advances in Water Resources, 77, 69–81.
    [Google Scholar]
13. Satija, A., Scheidt, C., Li, L. and Caers, J.
    [2017] Direct forecasting of reservoir performance using production data without history matching.Computational Geosciences, 21(2), 315–333.
    [Google Scholar]
14. Scheidt, C., Renard, P. and Caers, J.
    [2015] Prediction-focused subsurface modeling: investigating the need for accuracy in flow-based inverse modeling.Mathematical Geosciences, 47(2), 173–191.
    [Google Scholar]
15. Sun, W. and Durlofsky, L.J.
    [2017] A new data-space inversion procedure for efficient uncertainty quantification in subsurface flow problems.Mathematical Geosciences, 49(6), 679–715.
    [Google Scholar]
16. Sun, W., Hui, M.H. and Durlofsky, L.J.
    [2017] Production forecasting and uncertainty quantification for naturally fractured reservoirs using a new data-space inversion procedure.Computational Geo-sciences, 21(5-6), 1443–1458.
    [Google Scholar]
17. Tarantola, A.
    [2005] Inverse Problem Theory and Methods ƒor Model Parameter Estimation.SIAM.
    [Google Scholar]
18. Wang, C., Li, G. and Reynolds, A.C.
    [2009] Production optimization in closed-loop reservoir management. SPE Journal, 14(03), 506–523.
    [Google Scholar]
19. Zhou, Y.
    [2012] Parallel General-purpose Reservoir Simulation with Coupled Reservoir Models and Multisegment Wells. Ph.D. thesis, Stanford University.
    [Google Scholar]