Workflow for adding 4D seismic data in history matching
Keywords:
The National IOR Centre of Norway, ensemble-based history matching, 4D seismic data, reservoir engineeringSynopsis
In this document we present a workflow for ensemble-based 4D seismic history matching. Ensemble-based history matching has become standard for production data, but 4D seismic data poses a number of additional challenges. One issue is that the amount of data is considerably larger, but another, probably more complicating factor is that for utilizing the seismic data, either the seismic data must be inverted to properties that is included in the reservoir simulation model, or a seismic response must be modeled, given the current estimate of the reservoir properties. This leads to a number of choices on how to utilize the information of the 4D seismic data. We will discuss this, as well as point to approaches for handling large amounts of data in ensemble-based history matching. The developed approach has been applied on the Norne field and is currently being evaluated at the Ekosk field.
This document is primarily addressed to reservoir engineers and researchers that are working on history matching 4D seismic data, but it might also be of interest to those working with 4D seismic data from a geophysical perspective. After all, 4D seismic history matching should be viewed as an interdisciplinary subject. Although, our focus has been on ensemble-based
history matching, some of the choices that have to be made in utilizing 4D seismic data is independent of the actual method used for history matching.
References
[1] S. I. Aanonsen, G. Nævdal, D. S. Oliver, A. C. Reynolds, and B. Valles. The ensemble Kalman filter in reservoir engineering - a review. SPE Journal, 14(3):393-412, September 2009.
https://doi.org/10.2118/117274-PA
[2] Q. Boersma, W. Athmer, M. Haege, M. Etchebes, J. Haukås, and G. Bertotti. Natural fault and fracture network characterization for the southern Ekofisk field: A case study integrating seismic attribute analysis with image log interpretation. Journal of Structural Geology, 141(104197), 2020.
https://doi.org/10.1016/j.jsg.2020.104197
[3] Y. Chen and D. S. Oliver. Ensemble randomized maximum likelihood method as an iterative ensemble smoother. Mathematical Geosciences, 44(1):1{26, 2012. ISSN 1874-8961.
https://doi.org/10.1007/s11004-011-9376-z
[4] Y. Chen and D. S. Oliver. Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Computational Geosciences, 17(4):689-703, 2013. ISSN 1420-0597. doi: 10.1007/s10596-013-9351-5.
https://doi.org/10.1007/s10596-013-9351-5
[5] D. V. da Nobrega, F. S. de Moraes, and A. A. Emerick. Data assimilation of a legacy 4D seismic in a brown field. Journal of Geophysics and Engineering, 15(6):2585-2601, DEC 2018. ISSN 1742-2132.
https://doi.org/10.1088/1742-2140/aadd68
[6] A. A. Emerick. Analysis of the performance of ensemble-based assimilation of production and seismic data. Journal of Petroleum Science and Engineering, 139:219-239, 2016. ISSN 0920-4105. 029.
https://doi.org/10.1016/j.petrol.2016.01.029
[7] A. A. Emerick and A. C. Reynolds. Ensemble smoother with multiple data assimilation. Computers & Geosciences, 55:3-15, 2013.
https://doi.org/10.1016/j.cageo.2012.03.011
[8] A. A. Emerick and A. C. Reynolds. History-matching production and seismic data in a real field case using the ensemble smoother with multiple data assimilation. In 2013 SPE Reservoir Simulation Symposium, The Woodlands, TX, USA, February 18 - 20 2013. Society of Petroleum Engineers. SPE 163675-MS.
https://doi.org/10.2118/163675-MS
[9] G. Evensen, P. N. Raanes, A. S. Stordal, and J. Hove. Efficient implementation of an iterative ensemble smoother for data assimilation and reservoir history matching. Frontiers in Applied Mathematics and Statistics, 5:1{47, 2019. ISSN 2297-4687.
https://doi.org/10.3389/fams.2019.00047
[10] O. Leeuwenburgh, S. Meekes, V. Vandeweijer, and J. Brouwer. Stochastic history matching to time-lapse seismic of a CO2-EOR project sector model. International Journal of Greenhouse Gas Control, 54(2):441{453, Nov 2016. ISSN 1750-5836.
https://doi.org/10.1016/j.ijggc.2016.05.027
[11] M. Liu and D. Grana. Time-lapse seismic history matching with iterative ensemble smoother and deep convolutional autoencoder. Geophysics, 85:1-63, 10 2019.
https://doi.org/10.1190/geo2019-0019.1
[12] R. J. Lorentzen, X. Luo, T. Bhakta, and R. Valestrand. History matching the full Norne Field model using seismic and production data. SPE Journal, 24(4):1452-1467, 2019.
https://doi.org/10.2118/194205-PA
[13] R. J. Lorentzen, T. Bhakta, D. Grana, X. Luo, R. Valestrand, and G. Nævdal. Simultaneous assimilation of production and seismic data: application to the Norne field. Computational Geosciences, 24(2):907-920, 2020.
https://doi.org/10.1007/s10596-019-09900-0
[14] X. Luo and T. Bhakta. Estimating observation error covariance matrix of seismic data from a perspective of image denoising. Computational Geosciences, 21:205-222, 2017.
https://doi.org/10.1007/s10596-016-9605-0
[15] X. Luo and T. Bhakta. Automatic and adaptive localization for ensemble-based history matching. Journal of Petroleum Science and Engineering, 184:106559, 2020.
https://doi.org/10.1016/j.petrol.2019.106559
[16] X. Luo, T. Bhakta, M. Jakobsen, and G. Nævdal. An ensemble 4D-seismic historymatching framework with sparse representation based on wavelet multiresolution analysis. SPE Journal, 22:985 - 1010, 2017.
https://doi.org/10.2118/180025-PA
[17] X. Luo, T. Bhakta, M. Jakobsen, and G. Nævdal. Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering. Plos One, July 27 2018. doi: 10.1371/journal.pone.0198586. URL
https://doi.org/10.1371/journal.pone.0198586
[18] X. Luo, T. Bhakta, and G. Nævdal. Correlation-based adaptive localization with applications to ensemble-based 4D seismic history matching. SPE Journal, 23:396 - 427, 2018.
https://doi.org/10.2118/185936-PA
[19] X. Luo, R. J. Lorentzen, R. Valestrand, and G. Evensen. Correlation-based adaptive localization for ensemble-based history matching: Applied to the Norne field case study. SPE Reservoir Evaluation & Engineering, 22:1084 - 1109, 2019.
https://doi.org/10.2118/191305-PA
[20] X. Luo, R. J. Lorentzen, and T. Bhakta. Accounting for model errors of rock physics models in 4D seismic history matching problems: A perspective of machine learning. Journal of Petroleum Science and Engineering, 196:107961, 2021.
https://doi.org/10.1016/j.petrol.2020.107961
[21] X. Luo, A. S. Stordal, R. J. Lorentzen, and G. Nævdal. Iterative ensemble smoother as an approximate solution to a regularized minimum-average-cost problem: Theory and applications. SPE Journal, 20(5):962-982, October 2015. ISSN 1086-055X.
https://doi.org/10.2118/176023-PA
[22] NTNU. Norne benchmark case 2 - full field. http://www.ipt.ntnu.no/~norne/wiki1/doku.php?id=english:nornebenchmarkcase2. Accessed 27-09-2021.
[23] E. Oen Lie, T. Bhakta, and I. Sandø. Seismic modeling using dual-reservoir and geophysical models. In First International Meeting for Applied Geoscience & Energy, pages 2510-2514. Society of Exploration Geophysicists, 2021.
https://doi.org/10.1190/segam2021-3583399.1
[24] D. S. Oliver and Y. Chen. Recent progress on reservoir history matching: a review. Computational Geosciences, 15(1):185-221, 2011.
https://doi.org/10.1007/s10596-010-9194-2
[25] D. S. Oliver, K. Fossum, T. Bhakta, I. Sandø, G. Nævdal, and R. J. Lorentzen. 4D seismic history matching. Journal of Petroleum Science and Engineering, 207(109119), 2021.
https://doi.org/10.1016/j.petrol.2021.109119
[26] P. N. Raanes, A. S. Stordal, and G. Evensen. Revising the stochastic iterative ensemble smoother. Nonlinear Processes in Geophysics, 26(3):325-338, 2019.
https://doi.org/10.5194/npg-26-325-2019
[27] Rystad Energy. Effekter av forskningsrådets målrettede aktiviteter innen petroleum. https://www.regjeringen.no/contentassets/94e8a9c502944ef9b1d9c38b5ed7541e/effekter-av-marettede-aktiviteter-innen-petroleum_hoveddokument-1.pdf, December 20 2019. Accessed 02-11-2021.
[28] Rystad Energy Appendix. Effekter av forskningsrådets målrettede aktiviteter innen petroleum. Underlagsmateriale (appendiks). https://www.forskningsradet.no/contentassets/66c1b5cc03054f0e9fe39194cdfbdb60/20200109_effekter-av-marettede-aktiviteter-innen-petroleum_underlag.pdf, January 9 2020. Appendix to [27]. Accessed 02-11-2021.
[29] J.-A. Skjervheim and G. Evensen. An ensemble smoother for assisted history matching. In SPE Reservoir simulations symposium, The Woodlands, Texas, February 21-23 2011. SPE141929-MS.
https://doi.org/10.2118/141929-MS
[30] R. Soares, X. Luo, G. Evensen, and T. Bhakta. 4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method. Journal of Petroleum Science and Engineering, 195:107763, 2020. ISSN 0920-4105.
https://doi.org/10.1016/j.petrol.2020.107763
[31] R. V. Soares, X. Luo, G. Evensen, and T. Bhakta. Handling big models and big data sets in history-matching problems through an adaptive local analysis scheme. SPE Journal, 26(02):973-992, 2021.
https://doi.org/10.2118/204221-PA
[32] A. Tarantola. Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM, 2005.
https://doi.org/10.1137/1.9780898717921
[33] Z. Yin, T. Feng, and C. MacBeth. Fast assimilation of frequently acquired 4D seismic data for reservoir history matching. Computers & Geosciences, 128:30-40, JUL 2019. ISSN 0098-3004.
https://doi.org/10.1016/j.cageo.2019.04.001
https://doi.org/10.2118/117274-PA
[2] Q. Boersma, W. Athmer, M. Haege, M. Etchebes, J. Haukås, and G. Bertotti. Natural fault and fracture network characterization for the southern Ekofisk field: A case study integrating seismic attribute analysis with image log interpretation. Journal of Structural Geology, 141(104197), 2020.
https://doi.org/10.1016/j.jsg.2020.104197
[3] Y. Chen and D. S. Oliver. Ensemble randomized maximum likelihood method as an iterative ensemble smoother. Mathematical Geosciences, 44(1):1{26, 2012. ISSN 1874-8961.
https://doi.org/10.1007/s11004-011-9376-z
[4] Y. Chen and D. S. Oliver. Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Computational Geosciences, 17(4):689-703, 2013. ISSN 1420-0597. doi: 10.1007/s10596-013-9351-5.
https://doi.org/10.1007/s10596-013-9351-5
[5] D. V. da Nobrega, F. S. de Moraes, and A. A. Emerick. Data assimilation of a legacy 4D seismic in a brown field. Journal of Geophysics and Engineering, 15(6):2585-2601, DEC 2018. ISSN 1742-2132.
https://doi.org/10.1088/1742-2140/aadd68
[6] A. A. Emerick. Analysis of the performance of ensemble-based assimilation of production and seismic data. Journal of Petroleum Science and Engineering, 139:219-239, 2016. ISSN 0920-4105. 029.
https://doi.org/10.1016/j.petrol.2016.01.029
[7] A. A. Emerick and A. C. Reynolds. Ensemble smoother with multiple data assimilation. Computers & Geosciences, 55:3-15, 2013.
https://doi.org/10.1016/j.cageo.2012.03.011
[8] A. A. Emerick and A. C. Reynolds. History-matching production and seismic data in a real field case using the ensemble smoother with multiple data assimilation. In 2013 SPE Reservoir Simulation Symposium, The Woodlands, TX, USA, February 18 - 20 2013. Society of Petroleum Engineers. SPE 163675-MS.
https://doi.org/10.2118/163675-MS
[9] G. Evensen, P. N. Raanes, A. S. Stordal, and J. Hove. Efficient implementation of an iterative ensemble smoother for data assimilation and reservoir history matching. Frontiers in Applied Mathematics and Statistics, 5:1{47, 2019. ISSN 2297-4687.
https://doi.org/10.3389/fams.2019.00047
[10] O. Leeuwenburgh, S. Meekes, V. Vandeweijer, and J. Brouwer. Stochastic history matching to time-lapse seismic of a CO2-EOR project sector model. International Journal of Greenhouse Gas Control, 54(2):441{453, Nov 2016. ISSN 1750-5836.
https://doi.org/10.1016/j.ijggc.2016.05.027
[11] M. Liu and D. Grana. Time-lapse seismic history matching with iterative ensemble smoother and deep convolutional autoencoder. Geophysics, 85:1-63, 10 2019.
https://doi.org/10.1190/geo2019-0019.1
[12] R. J. Lorentzen, X. Luo, T. Bhakta, and R. Valestrand. History matching the full Norne Field model using seismic and production data. SPE Journal, 24(4):1452-1467, 2019.
https://doi.org/10.2118/194205-PA
[13] R. J. Lorentzen, T. Bhakta, D. Grana, X. Luo, R. Valestrand, and G. Nævdal. Simultaneous assimilation of production and seismic data: application to the Norne field. Computational Geosciences, 24(2):907-920, 2020.
https://doi.org/10.1007/s10596-019-09900-0
[14] X. Luo and T. Bhakta. Estimating observation error covariance matrix of seismic data from a perspective of image denoising. Computational Geosciences, 21:205-222, 2017.
https://doi.org/10.1007/s10596-016-9605-0
[15] X. Luo and T. Bhakta. Automatic and adaptive localization for ensemble-based history matching. Journal of Petroleum Science and Engineering, 184:106559, 2020.
https://doi.org/10.1016/j.petrol.2019.106559
[16] X. Luo, T. Bhakta, M. Jakobsen, and G. Nævdal. An ensemble 4D-seismic historymatching framework with sparse representation based on wavelet multiresolution analysis. SPE Journal, 22:985 - 1010, 2017.
https://doi.org/10.2118/180025-PA
[17] X. Luo, T. Bhakta, M. Jakobsen, and G. Nævdal. Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering. Plos One, July 27 2018. doi: 10.1371/journal.pone.0198586. URL
https://doi.org/10.1371/journal.pone.0198586
[18] X. Luo, T. Bhakta, and G. Nævdal. Correlation-based adaptive localization with applications to ensemble-based 4D seismic history matching. SPE Journal, 23:396 - 427, 2018.
https://doi.org/10.2118/185936-PA
[19] X. Luo, R. J. Lorentzen, R. Valestrand, and G. Evensen. Correlation-based adaptive localization for ensemble-based history matching: Applied to the Norne field case study. SPE Reservoir Evaluation & Engineering, 22:1084 - 1109, 2019.
https://doi.org/10.2118/191305-PA
[20] X. Luo, R. J. Lorentzen, and T. Bhakta. Accounting for model errors of rock physics models in 4D seismic history matching problems: A perspective of machine learning. Journal of Petroleum Science and Engineering, 196:107961, 2021.
https://doi.org/10.1016/j.petrol.2020.107961
[21] X. Luo, A. S. Stordal, R. J. Lorentzen, and G. Nævdal. Iterative ensemble smoother as an approximate solution to a regularized minimum-average-cost problem: Theory and applications. SPE Journal, 20(5):962-982, October 2015. ISSN 1086-055X.
https://doi.org/10.2118/176023-PA
[22] NTNU. Norne benchmark case 2 - full field. http://www.ipt.ntnu.no/~norne/wiki1/doku.php?id=english:nornebenchmarkcase2. Accessed 27-09-2021.
[23] E. Oen Lie, T. Bhakta, and I. Sandø. Seismic modeling using dual-reservoir and geophysical models. In First International Meeting for Applied Geoscience & Energy, pages 2510-2514. Society of Exploration Geophysicists, 2021.
https://doi.org/10.1190/segam2021-3583399.1
[24] D. S. Oliver and Y. Chen. Recent progress on reservoir history matching: a review. Computational Geosciences, 15(1):185-221, 2011.
https://doi.org/10.1007/s10596-010-9194-2
[25] D. S. Oliver, K. Fossum, T. Bhakta, I. Sandø, G. Nævdal, and R. J. Lorentzen. 4D seismic history matching. Journal of Petroleum Science and Engineering, 207(109119), 2021.
https://doi.org/10.1016/j.petrol.2021.109119
[26] P. N. Raanes, A. S. Stordal, and G. Evensen. Revising the stochastic iterative ensemble smoother. Nonlinear Processes in Geophysics, 26(3):325-338, 2019.
https://doi.org/10.5194/npg-26-325-2019
[27] Rystad Energy. Effekter av forskningsrådets målrettede aktiviteter innen petroleum. https://www.regjeringen.no/contentassets/94e8a9c502944ef9b1d9c38b5ed7541e/effekter-av-marettede-aktiviteter-innen-petroleum_hoveddokument-1.pdf, December 20 2019. Accessed 02-11-2021.
[28] Rystad Energy Appendix. Effekter av forskningsrådets målrettede aktiviteter innen petroleum. Underlagsmateriale (appendiks). https://www.forskningsradet.no/contentassets/66c1b5cc03054f0e9fe39194cdfbdb60/20200109_effekter-av-marettede-aktiviteter-innen-petroleum_underlag.pdf, January 9 2020. Appendix to [27]. Accessed 02-11-2021.
[29] J.-A. Skjervheim and G. Evensen. An ensemble smoother for assisted history matching. In SPE Reservoir simulations symposium, The Woodlands, Texas, February 21-23 2011. SPE141929-MS.
https://doi.org/10.2118/141929-MS
[30] R. Soares, X. Luo, G. Evensen, and T. Bhakta. 4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method. Journal of Petroleum Science and Engineering, 195:107763, 2020. ISSN 0920-4105.
https://doi.org/10.1016/j.petrol.2020.107763
[31] R. V. Soares, X. Luo, G. Evensen, and T. Bhakta. Handling big models and big data sets in history-matching problems through an adaptive local analysis scheme. SPE Journal, 26(02):973-992, 2021.
https://doi.org/10.2118/204221-PA
[32] A. Tarantola. Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM, 2005.
https://doi.org/10.1137/1.9780898717921
[33] Z. Yin, T. Feng, and C. MacBeth. Fast assimilation of frequently acquired 4D seismic data for reservoir history matching. Computers & Geosciences, 128:30-40, JUL 2019. ISSN 0098-3004.
https://doi.org/10.1016/j.cageo.2019.04.001
Downloads
Published
June 28, 2022
Series
Online ISSN
2387-6662
Categories
Copyright (c) 2021 The authors
