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Abstract

Summary

Carbonate reservoirs represent strongly complex geological structures whose main feature is that the flow dynamics primarily occurs in fractures. The complexity of the network of fractures as well as their interconnectedness may lead to unexpected flow patterns and uneven sweep efficiency. Determining the fracture distribution and reservoir properties of both matrix and fracture channels is quintessential for accurately tracking the fluid front movement in the reservoir, optimizing sweep efficiency, and maximizing hydrocarbon production.

A feature oriented ensemble-based history matching workflow was introduced previously to enhance the characterization of petroleum reservoirs through the assimilation of time-lapse electromagnetic (EM) data in combination with other available measurements. Compared with seismic measurements, which provide effective information related to reservoir structure, deep EM measurements in the interwell volumes are more sensitive to distinguish between hydrocarbon fluids and water. The developed workflow calibrates model variables of interest utilizing the information of formation resistivity that is usually made available through geophysical inversion of raw EM data. Archie’s law is typically used to build a relation between formation porosity, fluid properties (e.g., water saturation and salt concentration) and formation resistivity. Instead of integrating directly the inverted EM resistivity data, which is usually of high dimensions and noisy in amplitude, the boundary or contour information extracted from the EM resistivity field is utilized through an image oriented distance parameterization combined with an iterative ensemble smoother.

We are showcasing this framework on a realistic carbonate reservoir box model with a complex fracture channel network. Time-lapsed cross-well EM data was assimilated to update fracture and matrix reservoir properties, ensuring that the heterogeneity in the properties is maintained. The framework exhibited strong performance in the history matching of the complex carbonate reservoir structure. In comparison with conventional ensemble-based history matching techniques, this innovative developed approach led to significantly more accurate sweep efficiency maps, while maintaining the heterogeneity in the parameters between the fractures and the matrix. Finally, uncertainty in the saturation maps could be significantly reduced with the assistance of deep EM reservoir tomography.

Carbonate reservoirs represent highly complex geological structures and are characterized by flow dynamics dominated by natural fractures. The complexity of the network of fractures as well as their interconnectedness may lead to unexpected flow patterns and uneven sweep efficiency. Determining the fracture distribution and reservoir properties of both matrix and fracture channels is quintessential for accurately tracking the fluid front movement in the reservoir, optimizing sweep efficiency, and maximizing hydrocarbon production.

A feature-oriented ensemble-based history matching workflow was introduced previously to enhance the characterization of petroleum reservoirs through the assimilation of time-lapse electromagnetic (EM) data in combination with other available measurements. Compared with seismic measurements, which provide effective information related to reservoir structure, deep EM measurements in the interwell volumes are more sensitive to distinguish between hydrocarbon fluids and water due to the difference in electrical conductivity. The developed workflow calibrates model variables of interest utilizing the information of formation resistivity that is usually inferred through geophysical inversion of raw EM data. Archie’s law is typically used to describe the relation between formation porosity, fluid properties (e.g., water saturation and salt concentration) and formation resistivity. Instead of integrating directly the inverted EM resistivity data, which is usually of high dimensions and noisy in amplitude, the boundary or contour information extracted from the EM resistivity field is utilized through an image-oriented distance parameterization combined with an iterative ensemble smoother.

We are showcasing this framework using a realistic carbonate reservoir box model with a complex fracture channel network. We history matched time-lapsed crosswell EM data to update fracture and matrix reservoir properties, by preserving the heterogeneity in the properties. The framework exhibited strong performance in the history matching of the complex carbonate reservoir structure. The developed innovative approach led to significantly more accurate sweep efficiency maps, while maintaining the heterogeneity in the fractures and the matrix parameters. Uncertainties in the saturation maps were also significantly reduced with the history matching of deep EM reservoir tomography data.

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/content/papers/10.3997/2214-4609.202035033
2020-09-14
2024-04-19

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References

  1. Aanonsen, S. I., Nævdal, G., Oliver, D. S., Reynolds, A. C., and Vallès, B.
    [2009]. The Ensemble Kalman Filter in Reservoir Engineering – a Review. SPE Journal, 14(03), 393–412. https://doi.org/10.2118/117274-PA
     [Google Scholar]
  2. Abadpour, A., Adejare, M., Chugunova, T., Mathieu, H., and Haller, N.
    [2018]. Integrated geo-modeling and ensemble history matching of complex fractured carbonate and deep offshore turbidite fields, generation of several geologically coherent solutions using ensemble methods. Abu Dhabi International Petroleum Exhibition & Conference.
     [Google Scholar]
  3. Al-Ali, Z. H. Al-Buali, M., AlRuwaili, S., Ma, S. M., Marsala, A. F., Hulme, C., and Wilt, M.
    [2009]. Looking deep into the reservoir. Oilfield Review, 21(2), 38–47.
     [Google Scholar]
  4. Archie, G. E.
    [1942]. The electrical resistivity log as an aid in determining some reservoir characteristics. Transactions of the AIME, 146(01), 54–62. https://doi.org/10.2118/942054-G
     [Google Scholar]
  5. Chen, Y.
    [2014]. History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother. (May), 244–256.
     [Google Scholar]
  6. Chen, Y., and Oliver, D. S.
    [2013]. Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Computational Geosciences, 17(4), 689–703. https://doi.org/10.1007/s10596-013-9351-5
     [Google Scholar]
  7. [2017]. Localization and regularization for iterative ensemble smoothers. Computational Geosciences, 21(1), 13–30. https://doi.org/10.1007/s10596-016-9599-7
     [Google Scholar]
  8. Dovera, L., and Della Rossa, E.
    [2011]. Multimodal ensemble Kalman filtering using Gaussian mixture models. Computational Geosciences, 15(2), 307–323. https://doi.org/10.1007/s10596-010-9205-3
     [Google Scholar]
  9. Dresser, A.
    [1982]. Well logging and interpretation techniques. The course for home study. Dresser Atlas.
     [Google Scholar]
  10. Emerick, A. A., and Reynolds, A. C.
    [2013]. Ensemble smoother with multiple data assimilation. Computers and Geosciences, 55, 3–15. https://doi.org/10.1016/j.cageo.2012.03.011
     [Google Scholar]
  11. Hamada, G. M.
    [2010]. Analysis of archie’s parameters determination techniques. Petroleum Science and Technology, 28(1), 79–92. https://doi.org/10.1080/10916460802706463
     [Google Scholar]
  12. Hoteit, I., Luo, X., and Pham, D.-T.
    [2012]. Particle Kalman filtering: a nonlinear Bayesian framework for ensemble Kalman filters. Monthly Weather Review, 140(2), 528–542. https://doi.org/10.1175/2011MWR3640.1
     [Google Scholar]
  13. Jung, S., Lee, K., Park, C., and Choe, J.
    [2018]. Ensemble-based data assimilation in reservoir characterization: A review. Energies. https://doi.org/10.3390/en11020445
     [Google Scholar]
  14. Katterbauer, K., Hoteit, I., and Sun, S.
    [2016]. Synergizing Crosswell Seismic and Electromagnetic Techniques for Enhancing Reservoir Characterization. SPE Journal, 21(03), 0909–0927. https://doi.org/10.2118/174559-PA
     [Google Scholar]
  15. Leeuwenburgh, O., and Arts, R.
    [2014]. Distance parameterization for efficient seismic history matching with the ensemble Kalman Filter. Computational Geosciences, 18(3–4), 535–548. https://doi.org/10.1007/s10596-014-9434-y
     [Google Scholar]
  16. Liang, L., Abubakar, A., and Habashy, T. M.
    [2016]. Reservoir property mapping and monitoring from joint inversion of time-lapse seismic, electromagnetic, and production data. Geophysics, 81(5), 73–84. https://doi.org/10.1190/geo2015-0620.1
     [Google Scholar]
  17. Liu, B., Ait-El-Fquih, B., and Hoteit, I.
    [2016]. Efficient Kernel-Based Ensemble Gaussian Mixture Filtering. Monthly Weather Review, 144(2), 781–800. https://doi.org/10.1175/MWR-D-14-00292.1
     [Google Scholar]
  18. Lorentzen, R. J., Flornes, K. M., and Nævdal, G.
    [2012]. History matching channelized reservoirs using the ensemble kalman filter. SPE Journal, 17(1), 137–151. https://doi.org/10.2118/143188-PA
     [Google Scholar]
  19. Luo, X., Bhakta, T., Jakobsen, M., and Nævdal, G.
    [2017]. An ensemble 4D-seismic history-matching framework with sparse representation based on wavelet multiresolution analysis. SPE Journal. https://doi.org/10.2118/180025-PA
     [Google Scholar]
  20. Luo, X., Stordal, A. S., Lorentzen, R. J., and Nævdal, G.
    [2015]. Iterative Ensemble Smoother as an Approximate Solution to a Regularized Minimum-Average-Cost Problem : Theory and Applications. (January), 1–21. https://doi.org/10.1086/309714
     [Google Scholar]
  21. Marsala, A. F., Lyngra, S., Ma, S., and Alsaif, S.
    [2017]. Workflow to integrate geophysical deep em and reservoir simulation for interwell saturation mapping. 79th EAGE Conference and Exhibition 2017 - Workshops. https://doi.org/10.3997/2214-4609.201701781
     [Google Scholar]
  22. Marsala, A. F., Ruwaili, S., Ma, S. M., Ali, Z., Buali, M., Crary, S., and Wilt, M.
    [2008]. Crosswell electromagnetic tomography: From resistivity mapping to interwell fluid distribution. International Petroleum Technology Conference, IPTC 2008, 2, 1078–1083. https://doi.org/10.2523/iptc-12229-ms
     [Google Scholar]
  23. Maucec, M., Zhang, S., Camargo, O. M., and Olukoko, O.
    [2020]. New approach to history matching of simulation models with discrete fracture networks. International Petroleum Technology Conference 2020, IPTC 2020. https://doi.org/10.2523/iptc-19962-ms
     [Google Scholar]
  24. Oliver, D. S., and Alfonzo, M.
    [2018]. Calibration of imperfect models to biased observations. Computational Geosciences, 22(1), 145–161. https://doi.org/10.1007/s10596-017-9678-4
     [Google Scholar]
  25. Oliver, D. S., and Chen, Y.
    [2011]. Recent progress on reservoir history matching: A review. Computational Geosciences, 15(1), 185–221. https://doi.org/10.1007/s10596-010-9194-2
     [Google Scholar]
  26. Strebelle, S.
    [2012]. Multiple-Point Geostatistics : from Theory to Practice. 1–9.
     [Google Scholar]
  27. Wilt, M. J., Alumbaugh, D. L., Morrison, H. F., Becker, A., Lee, K. H., and Deszcz-Pan, M.
    [1995]. Crosswell electromagnetic tomography: System design considerations and field results. Geophysics, 60(3), 871–885. https://doi.org/10.1190/1.1443823
     [Google Scholar]
  28. Zhang, Y., and Leeuwenburgh, O.
    [2016]. Ensemble-based seismic history matching with distance parameterization for complex grids. ECMOR XV - 15th European Conference on the Mathematics of Oil Recovery. https://doi.org/10.3997/2214-4609.201601815
     [Google Scholar]
  29. Zhang, Yanfen, and Oliver, D. S.
    [2010]. Improving the ensemble estimate of the Kalman gain by bootstrap sampling. Mathematical Geosciences, 42(3), 327–345. https://doi.org/10.1007/s11004-010-9267-8
     [Google Scholar]
  30. Zhang, Yanhui, and Hoteit, I.
    [2020]. Efficient Joint 4D Seismic and Electromagnetic History Matching Using an Ensemble-Based Approach. International Petroleum Technology Conference, p. 20. https://doi.org/10.2523/IPTC-19856-Abstract
     [Google Scholar]
  31. Zhang, Yanhui, and Leeuwenburgh, O.
    [2017]. Image-oriented distance parameterization for ensemble-based seismic history matching. 713–731. https://doi.org/10.1007/s10596-017-9652-1
     [Google Scholar]
  32. Zhang, Yanhui, Oliver, D. S., Chen, Y., and Skaug, H. J.
    [2014]. Data assimilation by use of the iterative ensemble smoother for 2D facies models. SPE Journal, 20(01), 169–185. https://doi.org/10.2118/170248-PA
     [Google Scholar]
  33. Zhang, Yanhui, Vossepoel, F. C., and Hoteit, I.
    [2020]. Efficient Assimilation of Crosswell Electromagnetic Data Using an Ensemble-Based History-Matching Framework. SPE Journal, 25(01), 119–138. https://doi.org/10.2118/193808-PA
     [Google Scholar]
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History Matching of Time-Lapse Deep Electromagnetic Tomography with A Feature Oriented Ensemble-Based Approach
Conference Proceedings 2020, 1 (2020); https://doi.org/10.3997/2214-4609.202035033
/content/papers/10.3997/2214-4609.202035033
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