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Abstract

Summary

With the rapid increase in computing power over the past several decades, automatic or semi-automatic approaches to history matching (HM) have become viable replacements for the traditional manual HM approach. HM approaches now include robust and efficient numerical algorithms with the ability to account for geological and petrophysical uncertainties. Downhole rate and pressure data are commonly collected for the purpose of uncertainty reduction through the HM process. Although the cost required to collect such data, and conduct the HM, is significant, few companies conduct an a priori analysis of the information value from the data.

Although some studies have demonstrated the post-hoc value of HM data, few have demonstrated its a priori value; i.e., the assessment required to determine whether it is worthwhile investing in gathering the data and conducting the HM. In this paper, we illustrate and discuss an a priori analysis on information valuation, known as the Value-of-Information (VOI) analysis. The VOI from HM is assessed for future production data with the goal of informing the decision-maker of the potential value of investing in downhole measuring devices and HM procedures. We present the scientific basis for VOI analysis followed by an example of its implementation for an improved-oil-recovery (IOR) case. In the example, we use our proposed workflow of assessing VOI from HM to calculate the VOI from different types of production data and compare their values to distinguish between constructive and wasteful information gathering.

The contributions of this paper are three-fold. Firstly, we provide a consistent definition of VOI from production data and HM, and discuss the details of the calculations. Secondly, we propose a workflow of assessing VOI from HM. Thirdly, we present an IOR example using our proposed workflow involving the use of Ensemble Kalman Filter (EnKF) combined with Robust Optimization (RO) to calculate the VOI. Finally, we identify and discuss the possible causes for the limited use of VOI methods in HM contexts and suggest ways to increase the use of this powerful analysis tool.

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/content/papers/10.3997/2214-4609.201700327
2017-04-24
2020-09-28
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References

  1. Aanonsen, S. I., Nævdal, G., Oliver, D. S., and Reynolds, A. C.
    [2009] The Ensemble Kalman Filter in Reservoir Engineering—a Review. SPE Journal, 14(3), 393–412. SPE-117274-PA. http://dx.doi.org/10.2118/117274-PA.
    [Google Scholar]
  2. Bratvold, R. B., and Begg, S.
    [2010] Making Good Decisions, first edition. Society of Petroleum Engineers, Texas, USA.
    [Google Scholar]
  3. Bratvold, R. B., Bickel, J. E., and Lohne, H. P.
    [2009] Value of Information in the Oil and Gas Industry: Past, Present, and Future. SPE Reservoir Evaluation & Engineering, 12(4), 630–638. SPE-110378-PA. http://dx.doi.org/10.2118/110378-PA.
    [Google Scholar]
  4. Burgers, G., Jan van Leeuwen, P., and Evensen, G.
    [1998] Analysis Scheme in the Ensemble Kalman Filter. Monthly Weather Review, 126(6), 1719–1724. http://dx.doi.org/10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2.
    [Google Scholar]
  5. Barros, E. G. D., Jansen, J. D., and Van den Hof, P. M. J.
    [2015a] Value of Information in Parameter Identification and Optimization of Hydrocarbon Reservoirs. IFAC-PapersOnLine, 48(6), 229–235. http://dx.doi.org/10.1016/j.ifacol.2015.08.036.
    [Google Scholar]
  6. Barros, E. G. D., Leeuwenburgh, O., Van den Hof, P. M. J., and Jansen, J. D.
    [2015b] Value of Multiple Production Measurements and Water Front Tracking in Closed-Loop Reservoir Management. SPE Reservoir Characterisation and Simulation Conference and Exhibition, 14–16 September, Abu Dhabi, UAE. SPE-175608-MS. http://dx.doi.org/10.2118/175608-MS.
    [Google Scholar]
  7. Barros, E. G. D., Van den Hof, P. M. J., and Jansen, J. D.
    [2016] Value of Information in Closed-Loop Reservoir Management. Computational Geosciences, 20(3), 737–749. http://dx.doi.org/10.1007/s10596-015-9509-4.
    [Google Scholar]
  8. Clemen, R. T.
    [1991] Making Hard Decisions: An Introduction to Decision Analysis. PWS-Kent, Boston, USA.
    [Google Scholar]
  9. Chen, Y., Oliver, D. S., and Zhang, D.
    [2009] Efficient Ensemble-Based Closed-Loop Production Optimization. SPE Journal, 14(4), 634–645. SPE-112873-PA. http://dx.doi.org/10.2118/112873-PA.
    [Google Scholar]
  10. Evensen, G.
    [1994] Sequential Data Assimilation with a Nonlinear Quasi-Geostrophic Model Using Monte Carlo Methods to Forecast Error Statistics. Journal of Geophysical Research: Oceans, 99(C5), 10143–10162. http://dx.doi.org/10.1029/94JC00572.
    [Google Scholar]
  11. [2009] Data Assimilation: The Ensemble Kalman Filter, second edition. Springer Science & Business Media, Heidelberg, Germany.
    [Google Scholar]
  12. Eidsvik, J., Mukerji, T., and Bhattacharjya, D.
    [2015] Value of Information in the Earth Sciences: Integrating Spatial Modeling and Decision Analysis, first edition. Cambridge University Press, Cambridge, United Kingdom.
    [Google Scholar]
  13. Eppel, T., and von Winterfeldt, D.
    [2008] Value-of-Information Analysis for Nuclear Waste Storage Tanks. Decision Analysis, 5(3), 157–167. http://dx.doi.org/10.1287/deca.1080.0121.
    [Google Scholar]
  14. Fonseca, R., Stordal, A., Leeuwenburgh, O., Van den Hof, P. M. J. and Jansen, J. D.
    [2014] Robust ensemble-based multi-objective optimization. ECMOR XIV-14th European conference on the mathematics of oil recovery, 8–11 September 2014, Catania, Sicily, Italy.
    [Google Scholar]
  15. Grayson, C. J.Jr.
    [1960]. Decisions under Uncertainty: Drilling Decisions by Oil and Gas Operators. Harvard University Press, Boston, USA.
    [Google Scholar]
  16. Howard, R. A.
    [1966] Information Value Theory. IEEE Transactions on Systems Science and Cybernetics, 2(1), 22–26. http://dx.doi.org/10.1109/TSSC.1966.300074.
    [Google Scholar]
  17. Howard, R. A., and Abbas, A. E.
    [2016] Foundations of Decision Analysis, global edition. Pearson Education Limited, Harlow, England.
    [Google Scholar]
  18. Hong, A. J., Bratvold, R. B., Thomas, P., and Hanea, R. G.
    [2017] Value-of-Information for Model Parameter Updating through History Matching (under review).
    [Google Scholar]
  19. Kalman, R. E.
    [1960]. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35–45. http://dx.doi.org/10.1115/1.3662552.
    [Google Scholar]
  20. Krymskaya, M. V., Hanea, R. G., Jansen, J. D., and Heemink, A. W.
    [2010] Observation Sensitivity in Computer-Assisted History Matching. 72nd EAGE Conference and Exhibition incorporating SPE EUROPEC, 14–17 June 2010, Barcelona, Spain. http://dx.doi.org/10.3997/2214-4609.201400961.
    [Google Scholar]
  21. Le, D. H., and Reynolds, A. C.
    [2014] Optimal Choice of a Surveillance Operation Using Information Theory. Computational Geosciences, 18(3–4), 505–518. http://dx.doi.org/10.1007/s10596-014-9401-7.
    [Google Scholar]
  22. Meinhold, R. J., and Singpurwalla, N. D.
    [1983] Understanding the Kalman Filter. The American Statistician, 37(2), 123–127.
    [Google Scholar]
  23. Raiffa, H., and Schlaifer, R.
    [1961] Applied Statistical Decision Theory. Division of Research, Graduate School of Business Administration, Harvard University (Reprint), Boston, USA.
    [Google Scholar]
  24. Schlaifer, R.
    [1959] Probability and Statistics for Business Decisions. McGraw-Hill, New York, USA.
    [Google Scholar]
  25. Samson, D., Wirth, A., and Rickard, J.
    [1989]. The value of information from multiple sources of uncertainty in decision analysis. European Journal of Operational Research, 39(3), 254–260. https://doi.org/10.1016/0377-2217(89)90163-x.
    [Google Scholar]
  26. Willis, H. H., Moore, M.
    [2013] Improving the Value of Analysis for Biosurveillance. Decision Analysis, 11(1), 63–81. http://dx.doi.org/10.1287/deca.2013.0283.
    [Google Scholar]
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