1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery
  5. Conference paper

Abstract

Summary

Ensemble data assimilation methods have been successfully applied in several real-life history-matching problems. However, because these methods rely on Gaussian assumptions, their performance is severely degraded when the prior geology is described in terms of complex facies distributions. This work introduces a novel parameterization based on deep learning for history matching facies models with ensemble methods.

The proposed method consists on a parameterization of geological facies by means of a deep belief network (DBN) used as an autoencoder. The process begins with a large set of facies realizations which are used for training the DBN. The trained network has two parts: an encoder and a decoder function. The encoder is used to construct a continuous parameterization of the facies which is iteratively updated to account for observed production data using the method ensemble smoother with multiple data assimilation (ES-MDA). After each iteration of ES-MDA, the decoder is used to reconstruct the facies realizations.

The proposed method is tested in three synthetic history-matching problems with channelized facies constructed with multiple point geostatistics. We compare the results of the DBN parameterization against the standard ES-MDA (with no parameterization) and the recently proposed optimization-based principal component analysis (OPCA). Our results show that all procedures are able to match the observed production data. However, standard ES-MDA failed to generate channel facies with well-defined boundaries. OPCA and DBN parameterizations improved the facies description resulting in the expected bi-modal distributions of log-permeability. This paper reports our initial results on an ongoing investigation with deep learning. Nevertheless, the results presented here indicate a great potential on the use of deep learning technologies in the inverse modeling of petroleum reservoirs.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201802277
2018-09-03
2024-04-28

  • From This Site

    /content/papers/10.3997/2214-4609.201802277
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Agbalaka, C.C. and Oliver, D.S.
    [2008] Application of the EnKF and localization to automatic history matching of facies distribution and production data.Mathematical Geosciences, 40(4), 353–374.
     [Google Scholar]
  2. Armstrong, M., Galli, A., Beucher, H., Loc'h, G.L., Renard, D., Doligez, B., Eschard, R. and Geffroy, F.
    [2011] Plurigaussian simulations in geosciences.Springer-Verlag Berlin Heidelberg, 2nd edn.
     [Google Scholar]
  3. Bengio, Y.
    [2009] Learning jeep architectures for AINow Publishers Inc.
     [Google Scholar]
  4. Caers, J. and Zhang, T.
    [2004] Multiple-point geostatistics: a quantitative vehicle for integrating geologic analogs into multiple reservoir models.AAPG memoir, 80, 383–394.
     [Google Scholar]
  5. Canchumuni, S.A., Emerick, A.A. and Pacheco, M.A.
    [2017] Integration of ensemble data assimilation and deep learning for history matching facies models. In: Proceejings ofthe Offshore Technology Conference, Rio de Janeiro,Brazil, 24–26 October, OTC-28015-MS.
     [Google Scholar]
  6. Chang, Y., Stordal, A.S. and Valestran, R.
    [2015] Facies parameterization and estimation for complex reservoirs - the Brugge field. In: Proceedings of the SPE Bergen One Day Seminar, Bergen, Norway, 22 April, SPE-173872-MS.
     [Google Scholar]
  7. Chen, C., Gao, G., Gelderblom, P. and Jimenez, E.
    [2016] Integration of cumulative-distribution-function mapping with principal-component analysis for the history matching of channelized reservoirs.SPE Reservoir Evaluation & Engineering, 19(2), 278–293.
     [Google Scholar]
  8. 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, 689–703.
     [Google Scholar]
  9. Curtó, J.D., Zarza, I.C., Torre, F.D.L., King, I. and Lyu, M.R.
    [2018] High-resolution deep convolutional generative adversarial networks.arXiv:1711.06491v9 [cs.CV].
     [Google Scholar]
  10. Deng, X., Tian, X., Chen, S. and Harris, C.J.
    [2017] Deep learning based nonlinear principal component analysis for industrial process fault detection. In: Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN).
     [Google Scholar]
  11. Emerick, A.A.
    [2017] Investigation on principal component analysis parameterizations for history matching channelized facies models with ensemble-based data assimilation.Mathematical Geo-sciences, 49(1), 85–120.
     [Google Scholar]
  12. Emerick, A.A. and Reynolds, A.C.
    [2013] Ensemble smoother with multiple data assimilation.Computers & Geosciences, 55, 3–15.
     [Google Scholar]
  13. Furrer, R. and Bengtsson, T.
    [2007] Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants.Journal of Multivariate Analysis, 98(2), 227–255.
     [Google Scholar]
  14. Gaspari, G. and Cohn, S.E.
    [1999] Construction of correlation functions in two and three dimensions.Quarterly Journal of the Royal Meteorological Society, 125(554), 723–757.
     [Google Scholar]
  15. Goodfellow, I., Bengio, Y. and Courville, A.
    [2016] Deep learning.MIT Press.
     [Google Scholar]
  16. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y.
    [2014] Generative adversarial networks.arXiv:1406.2661v1 [stat.ML].
     [Google Scholar]
  17. Hinton, G.E.
    [2002] Training products of experts by minimizing contrastive divergence.Neural Computation, 14(8), 1771–1800.
     [Google Scholar]
  18. Hinton, G.E., Osindero, S. and Teh, Y.W.
    [2006] A fast learning algorithm for deep belief nets.Neural Computation, 18(7), 1527–1554.
     [Google Scholar]
  19. Hinton, G.E. and Salakhutdinov, R.R.
    [2006] Reducing the dimensionality of data with neural networks.Science, 313, 504–507.
     [Google Scholar]
  20. Houtekamer, P.L. and Mitchell, H.L.
    [2001] A sequential ensemble Kalman filter for atmospheric data assimilation.Monthly Weather Review, 129(1), 123–137.
     [Google Scholar]
  21. Jafarpour, B. and Khodabakhshi, M.
    [2011] A probability conditioning method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation.Mathematical Geosciences, 43(2), 133–164.
     [Google Scholar]
  22. Keyvanrad, M.A. and Homayounpour, M.M.
    [2014] A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet).arXiv:1408.3264v7 [cs.CV].
     [Google Scholar]
  23. Le, D.H., Younis, R. and Reynolds, A.C.
    [2015] A history matching procedure for non-Gaussian facies based on ES-MDA. In: Proceedings of the SPE Reservoir Simulation Symposium, Houston, Texas, USA, 23–25 February, SPE-173233-MS.
     [Google Scholar]
  24. Liu, N. and Oliver, D.S.
    [2005] Ensemble Kalman filter for automatic history matching of geologic facies.Journal of Petroleum Science and Engineering, 47(3-4), 147–161.
     [Google Scholar]
  25. Luo, X., Stordal, A.S., Lorentzen, R.J. and Nœvdal, G.
    [2015] Iterative ensemble smoother as an approximate solution to a regularized minimum-average-cost problem: Theory and applications.SPE Journal, 20(5).
     [Google Scholar]
  26. Mariethoz, G. and Caers, J.
    [2014] Multiple-point geostatistics - Stochastic modeling with training images.John Wiley & Sons, Ltd.
     [Google Scholar]
  27. Peters, L., Arts, R., Brouwer, G., Geel, C., Cullick, S., Lorentzen, R.J., Chen, Y., Dunlop, N., Vossepoel, F.C., Xu, R., Sarma, P., Alhuthali, A.H. and Reynolds, A.
    [2010] Results of the Brugge benchmark study for flooding optimisation and history matching.SPE Reservoir Evaluation & Engineering, 13(3), 391–405.
     [Google Scholar]
  28. Radford, A., Metz, L. and Chintala, S.
    [2015] Unsupervised representation learning with deep convolutional generative adversarial networks.arXiv:1511.06434 [cs.LG].
     [Google Scholar]
  29. Sarma, P., Durlofsky, L.J. and Aziz, K.
    [2008] Kernel principal component analysis for efficient differentiable parameterization of multipoint geostatistics.Mathematical Geosciences, 40(1), 3–32.
     [Google Scholar]
  30. Schölkopf, B., Smola, A. and Müller, K.R.
    [1998] Nonlinear component analysis as a kernel eigenvalue problem.Neural Computation, 10(5), 1299–1319.
     [Google Scholar]
  31. Sebacher, B.M., Hanea, R. and Heemink, A.
    [2013] A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF).Computational Geosciences, 17(5), 813–832.
     [Google Scholar]
  32. Sebacher, B.M., Stordal, A.S. and Hanea, R.
    [2015] Bridging multipoint statistics and truncated Gaussian fields for improved estimation of channelized reservoirs with ensemble methods.Computational Geosciences, 19(2), 341–369.
     [Google Scholar]
  33. Stordal, A.S. and Elsheikh, A.H.
    [2015] Iterative ensemble smoothers in the annealed importance sampling framework.Advances in Water Resources, 86, 231–239.
     [Google Scholar]
  34. Strebelle, S.
    [2002] Conditional simulation of complex geological structures using multiple-point statistics.Mathematical Geology, 34(1), 1–21.
     [Google Scholar]
  35. Tavakoli, R., Srinivasan, S. and Wheeler, M.F.
    [2014] Rapid updating of stochastic models by use of an ensemble-filter approach.SPE Journal, 19(3), 500–513.
     [Google Scholar]
  36. Vo, H.X. and Durlofsky, L.J.
    [2014] A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models.Mathematical Geosciences, 46(7), 775–813.
     [Google Scholar]
  37. Zhao, Y., Reynolds, A.C. and Li, G.
    [2008] Generating facies maps by assimilating production data and seismic data with the ensemble Kalman filter. In: Proceejings of the SPE Lmprovej Oil Recovery Symposium, Tulsa, Oklahoma, 20–23 April, SPE-113990-MS.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201802277
Loading
History Matching Channelized Facies Models Using Ensemble Smoother With A Deep Learning Parameterization
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802277
/content/papers/10.3997/2214-4609.201802277
/content/papers/10.3997/2214-4609.201802277
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error