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Abstract

Summary

Probabilistic approaches for optimization objectives need a large ensemble size to consider uncertainties, which is often computationally expensive. Our proposed method includes two scenario reduction (SR) techniques applied to geostatistical realizations and reservoir simulation models to handle geological and dynamic uncertainties. The goal is to select a subset of simulation models to be used in an efficient robust optimization (RO).

The proposed workflow is summarized in the following sections.

  1. Generate total geostatistical (TG) realizations representing grid properties using Latin Hypercube (LH) sampling;
  2. Select representative geostatistical (RG) realizations from the TG realizations using an integrated statistical technique named Distance-based Clustering with Simple Matching Coefficient (DCSMC). This section is the first stage of SR;
  3. Integrate other uncertainties with the RG scenarios to generate total simulation (TS) models using Discrete Latin Hypercube with Geostatistical models (DLHG);
  4. Apply data assimilation process to reduce uncertainty and generate total history-matched simulation (THS) models using a filtering indicator named Normalized Quadratic Deviation with Signal (NQDS);
  5. Select representative history-matched simulation (RHS) models from the THS models set using a tool based on a metaheuristic optimization algorithm named RMFinder. This section is the second stage of SR;
  6. Perform an RO to maximize NPV as the objective function using the selected RHS models;

The novel SR workflow selects the representative scenarios (RG realizations and RHS models) during two steps: (1) RG selection based on static features before the simulation process and, (2) RHS selection based on simulation-based (dynamic) features after the simulation process. The workflow is applied to a fractured synthetic reservoir model named the UNISIM-II-D flow unit-based.

To check the computational-time and efficiency of the methodology, we compare two candidate production strategies based on (1) five RHS models obtained from the two-stage SR process considering DCSMC and RMFinder techniques (workflow A), and (2) five RHS models obtained from one-stage SR process using the RMFinder method (workflow B). In workflow A, the SR process is performed gradually during two steps while in workflow B, the SR process is applied all at once in one step.

The results show that the distribution of simulation outcomes after RO for the representative scenarios and the total scenarios in workflow A are more similar than workflow B. In addition, the robust production strategy obtained from workflow A is preferred to workflow B because it presents higher chances of high NPV value and lower chances of low NPV value.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202035105
2020-09-14
2024-04-19

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References

  1. Almeida, F.L.R., Davolio, A., and Schiozer, D.J.
    (2014). A New Approach to Perform a Probabilistic and Multi-Objective History Matching. SPE Annual Technical Conference and Exhibition held in, Amsterdam, The Netherlands. SPE-170623-MS. SPE-66399-MS.
     [Google Scholar]
  2. Amirian, E., Leung, J.Y., Zanon, S., and Dzurman, P.
    (2015). Integrated Cluster Analysis and Artificial Neural Network Modeling for Steam-assisted Gravity Drainage Performance Prediction in Heterogeneous Reservoirs. Expert Systems with Applications, 42 (2), 723–740. https://doi.org/10.1016/j.eswa.2014.08.034.
     [Google Scholar]
  3. Avansi, G.D. and Schiozer, D.J.
    (2015). A New Approach to History Matching using Reservoir Characterization and Reservoir Simulation Integrated Studies. Offshore Technology Conference held in Houston, Texas, USA, 04-07 May. OTC-26038-MS.
     [Google Scholar]
  4. Badru, O. and Kabir, C.S.
    (2003). Well Placement Optimization in Field Development. SPE Annual Technical Conference and Exhibition held in Denver, Colorado. https://doi.org/10.2118/84191-MS.
     [Google Scholar]
  5. Bertolini, A.C., Maschio, C., and Schiozer, D.J.
    (2015). A Methodology to Evaluate and Reduce Reservoir Uncertainties using Multivariate Distribution, Journal of Petroleum Science and Engineering, 128, 1–14. https://doi.org/10.1016/j.petrol.2015.02.003.
     [Google Scholar]
  6. Botechia, V. E., Gasper, A.T., and Schiozer, D. J.
    , (2013). Use of Well Indicators in the Production Strategy Optimization Production Process. EAGE Annual Conference & Exhibition incorporating SPE Europec, 10-13 June, London, UK. SPE-164874-MS.
     [Google Scholar]
  7. Caers, J., and Park, K.
    (2008). A Distance-based Representation of Reservoir Uncertainty: The Metric EnKF. 11th European Conference on the Mathematics of Oil Recovery (ECMOR XI). EAGE. Bergen, Norway, 8 - 11 September.
     [Google Scholar]
  8. Chang, Y., Bouzarkouna, Z., Devegowda, D.
    , (2015). Multi-objective Optimization for Rapid and Robust Optimal Oilfield Development under Geological Uncertainty. Computational Geosciences, 19(2015), 933–950. https://doi.org/10.1007/s10596-015-9507-6.
     [Google Scholar]
  9. Chen, C., Gao, G., Ramirez, B.A., Vink, J.C., and Girardi, A.M.
    (2016). Assisted History Matching of Channelized Models Using Pluri-Principal Component Analysis. SPE Journal, 21 (5), 1793–1812. https://doi.org/10.2118/173192-PA.
     [Google Scholar]
  10. Ferreira, C.J., Davolio, A., and Schiozer, D.J.
    (2017). Evaluation of the Discrete Latin Hypercube with Geostatistical Realizations Sampling for History Matching Under Uncertainties for the Norne Benchmark Case. Offshore Technology Conference Brasil, 24–26 October, Rio de Janeiro, Brazil. OTC-28073-MS.
     [Google Scholar]
  11. Formentin, H. N., Almeida, F. L., Avansi, G. D., Maschio, C., Schiozer, D. J., Caiado, C., Vernon, I., Goldstein, M.
    (2019). Gaining More Understanding about Reservoir Behavior through Assimilation of Breakthrough Time and Productivity Deviation in the History Matching Process. Journal of Petroleum Science and Engineering, 173, 1080–1096. https://doi.org/10.1016/j.petrol.2018.10.045.
     [Google Scholar]
  12. Haghighat Sefat, M., Elsheikh, A. H., Muradov, K. M., and Davies, D. R.
    (2016). Reservoir Uncertainty Tolerant, Proactive Control of Intelligent Wells. Computational Geosciences, 20(3), 655–676. https://doi.org/10.1007/s10596-015-9513-8.
     [Google Scholar]
  13. Hutahaean, J., Demyanov, V., and Christie, M.
    (2019). Reservoir Development Optimization under Uncertainty for Infill Well Placement in Brownfield Redevelopment. Journal of Petroleum Science and Engineering, 175(2018), 444–464. https://doi.org/10.1016/j.petrol.2018.12.043.
     [Google Scholar]
  14. Insuasty, E., Van den Hof, P.M., Weiland, S., and Jansen, J.D.
    (2017). Flow-based Dissimilarity Measures for Reservoir Models: A Spatial-Temporal Tensor approach. Computational Geosciences, 21, 645–663. https://doi.org/10.1007/s10596-017-9641-4.
     [Google Scholar]
  15. Janiga, D., Czarnota, R., Stopa, J., and Wojnarowski, P.
    (2019). Self-adapt Reservoir Clusterization Method to Enhance Robustness of Well Placement Optimization. Journal of Petroleum Science and Engineering, 173 (2018), 37–52. https://doi.org/10.1016/j.petrol.2018.10.005.
     [Google Scholar]
  16. Jin, J., Lim, J., Lee, H., and Choe, J.
    (2011). Metric Space Mapping of Oil Sands Reservoirs Using Streamline Simulation. Geosystem Engineering, 14 (3), 109–113. https://doi.org/10.1080/12269328.2011.10541338.
     [Google Scholar]
  17. Kang, B., Kim, S., Jung, H., Choe, J., and Lee, K.
    (2019). Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review. Energies, 12(10), 1859. https://doi.org/10.3390/en12101859.
     [Google Scholar]
  18. Lee, K., Jeong, H., Jung, S. and Choe, J.
    (2013) Characterization of Channelized Reservoir Using Ensemble Kalman Filter with Clustered Covariance. Energy Exploration & Exploitation, 31(1), 17–29. https://doi.org/10.1260/0144-5987.31.1.17.
     [Google Scholar]
  19. Lee, K., Jo, G., and Choe, J.
    (2011). Improvement of Ensemble Kalman Filter for Improper Initial Ensembles. Geosystem Engineering, 14(2), 79–84. https://doi.org/10.1080/12269328.2011.10541334.
     [Google Scholar]
  20. Lee, K., Jung, S., and Choe, J.
    (2016). Ensemble Smoother with Clustered Covariance for 3D Channelized Reservoirs with Geological Uncertainty. Journal of Petroleum Science and Engineering, 145(2016), 423–435. https://doi.org/10.1016/j.petrol.2016.05.029.
     [Google Scholar]
  21. Lee, K., Jung, S., Lee, T., and Choe, J.
    (2017). Use of Clustered Covariance and Selective Measurement Data in Ensemble Smoother for Three-dimensional Reservoir Characterization. Journal of Energy Resources Technology-ASME, 139(2), 022905. https://doi.org/10.1115/1.4034443.
     [Google Scholar]
  22. Mahjour, S. K., Correia, M. G., de Souza dos Santos, A. A., and Schiozer, D. J.
    (2020a). Using an Integrated Multidimensional Scaling and Clustering Method to Reduce the Number of Scenarios Based on Flow-Unit Models under Geological Uncertainties. ASME. Journal of Energy Resources Technology. 142(6), 063005. https://doi.org/10.1115/1.4045736.
     [Google Scholar]
  23. Mahjour, S. K., Santos, A. A. S., Correia, M. G., and Schiozer, D. J.
    (2020b). Developing a Workflow to Select Representative Reservoir Models Combining Distance Based Clustering and Data Assimilation for Decision Making Process. Journal of Petroleum Science and Engineering. 190(2020), 107078. https://doi.org/10.1016/j.petrol.2020.107078.
     [Google Scholar]
  24. Mahjour, S.K., Correia, M.G., Santos, A.A.S., and Schiozer, D.J.
    (2019). Developing a Workflow to Represent Fractured Carbonate Reservoirs for Simulation Models Under Uncertainties Based on Flow Unit Concept. Oil & Gas Science and Technology - Rev. IFP Energies nouvelles74, 15. https://doi.org/10.2516/ogst/2018096.
     [Google Scholar]
  25. Maschio, C. and Schiozer, D.J.
    (2016). Probabilistic History Matching Using Discrete Latin Hypercube Sampling and Nonparametric Density Estimation. Journal of Petroleum Science and Engineering, 147, 98–115, 2016. https://doi.org/10.1016/j.petrol.2016.05.011.
     [Google Scholar]
  26. Meira, L.A., Coelho, G.P., Silva, C.G., Abreu, J.A.L., Santos, A.A.S., and Schiozer, D.J.
    (2019). Improving Representativeness in a Scenario Reduction Process to Aid Decision Making in Petroleum Fields. Journal of Petroleum Science and Engineering, In Press, Journal Pre-proof. https://doi.org/10.1016/j.petrol.2019.106398.
     [Google Scholar]
  27. Morosov, A. L., and Schiozer, D. J.
    (2017). Field-Development Process Revealing Uncertainty-Assessment Pitfalls. SPE Reservoir Evaluation & Engineering, 20(03), 765–778. https://doi.org/10.2118/180094-PA.
     [Google Scholar]
  28. Rahim, S. and Li, Z.
    (2015). Well Placement Optimization with Geological Uncertainty Reduction. 9th International Symposium on Advanced Control of Chemical Processes, The International Federation of Automatic Control held in Whistler, British Columbia, Canada, 7-10 June, Whistler, British Columbia, Canada.
     [Google Scholar]
  29. Santos, S. M. G., Gaspar, A. T. F. S., and Schiozer, D. J.
    (2018a). Managing Reservoir Uncertainty in Petroleum Field Development: Defining a Flexible Production Strategy from a Set of Rigid Candidate Strategies. Journal of Petroleum Science and Engineering, 171 (2018), 516–528. https://doi.org/10.1016/j.petrol.2018.07.048.
     [Google Scholar]
  30. Santos, S. M. G., Gasper, A. T. F. S., and Schiozer, D. J.
    (2018b). Comparison of Risk Analysis Methodologies in a Geostatistical Context: Monte Carlo with Joint Proxy Models and Discretized Latin Hypercube. International Journal for Uncertainty Quantification, 8(1), 23–41. DOI: 10.1615/Int.J.UncertaintyQuantification.2018019782.
     https://doi.org/10.1615/Int.J.UncertaintyQuantification.2018019782 [Google Scholar]
  31. Sarma, P., Chen, W.H., Xie, J.
    (2013). Selecting Representative Models from a Large Set of Models. SPE Reservoir Simulation Symposium held in The Woodlands, Texas, USA, 18-20 February. SPE-163671-MS.
     [Google Scholar]
  32. Scheidt, C., and Caers, J.
    (2009). Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods — Application to a West Africa Deepwater Turbidite Reservoir. Society of Petroleum Engineers, Volume 14, Issue 04. https://doi.org/10.2118/118740-PA.
     [Google Scholar]
  33. Schiozer, D.J., Avansi, G.D., and Santos, A.A.S.
    (2016). Risk Quantification Combining Geostatistical Realizations and Discretized Latin Hypercube. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(2), 575–587. https://doi.org/10.1007/s40430-016-0576-9.
     [Google Scholar]
  34. Schiozer, D.J., Santos, A.A.S., and Drumond, P.S.
    (2015). Integrated Model Based Decision Analysis in Twelve Steps Applied to Petroleum Fields Development and Management. EUROPEC 2015, 1-4 June, Madrid, Spain. SPE-174370-MS.
     [Google Scholar]
  35. Schiozer, D.J., Santos, A.A.S., Santos, S.M.G., and Filho, J.C.H.
    (2019). Model-Based Decision Analysis Applied to Petroleum Field Development and Management. Oil & Gas Science and Technology - Rev. IFP Energies nouvelles74, 46. https://doi.org/10.2516/ogst/2019019.
     [Google Scholar]
  36. Shirangi, M. G., and Durlofsky, L. J.
    (2016). A General Method to Select Representative Models for Decision Making and Optimization under Uncertainty. Computers & Geosciences. 96, 109–123. https://doi.org/10.1016/j.cageo.2016.08.002.
     [Google Scholar]
  37. Suzuki, S., and Caers, J.
    (2008). A Distance-based Prior Model Parameterization for Constraining Solutions of Spatial Inverse Problems. Mathematical Geosciences, 40(4), 445–469. https://doi.org/10.1007/s11004-008-9154-8.
     [Google Scholar]
  38. van Essen, G., Zandvliet, M., van den Hof, P., Bosgra, O., and JansenJ.-D.
    (2009). Robust Waterflooding Optimization of Multiple Geological Scenarios, SPE Journal. 14(1), 24–27. https://doi.org/10.2118/102913-PA.
     [Google Scholar]
  39. von Hohendorff FilhoJ.C., MaschioC., and SchiozerD.J.
    (2016) Production Strategy Optimization Based on Iterative Discrete Latin Hypercube. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 38(8), 2473–2480. https://doi.org/10.1007/s40430-016-0511-0.
     [Google Scholar]
  40. Yang, C., CardC., Nghiem, L., and Fedutenko, E.
    (2011). Robust Optimization of SAGD Operations under Geological Uncertainties. SPE Reservoir Simulation Symposium held in The Woodlands, Texas, 21–23 February. https://doi.org/10.2118/141676-MS.
     [Google Scholar]
  41. Yasari, E., Pishvaie, M.R., Khorasheh, F., and Salahshoor, K.
    (2013). Application of Multi-criterion Robust Optimization in Water-flooding of Oil Reservoir. Petroleum Science and Engineering, 109(2013), 1–11. https://doi.org/10.1016/j.petrol.2013.07.008.
     [Google Scholar]
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Two-Stage Scenario Reduction Process for An Efficient Robust Optimization
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202035105
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