Research Papers: Petroleum Engineering

Regeneration of Initial Ensembles With Facies Analysis for Efficient History Matching

[+] Author and Article Information
Byeongcheol Kang

Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: qudcjf@snu.ac.kr

Jonggeun Choe

Department of Energy Resources Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received November 17, 2016; final manuscript received March 24, 2017; published online April 17, 2017. Assoc. Editor: Egidio Marotta.

J. Energy Resour. Technol 139(4), 042903 (Apr 17, 2017) (11 pages) Paper No: JERT-16-1463; doi: 10.1115/1.4036382 History: Received November 17, 2016; Revised March 24, 2017

Reservoir characterization is needed for estimating reservoir properties and forecasting production rates in a reliable manner. However, it is challenging to figure out reservoir properties of interest due to limited information. Therefore, well-designed reservoir models, which reflect characteristics of a true field, should be selected and fine-tuned. We propose a novel scheme of generating initial reservoir models by using static data and production history data available. We select representative reservoir models by projecting reservoir models onto a two-dimensional (2D) plane using principal component analysis (PCA) and calculating errors of production rates against observed data. These selected models, which will have similar geological properties with the reference, are used to regenerate models by perturbing along the boundary of the different facies. These regenerated models have all the different facies distributions but share principal characteristics based on the selected models. We compare cases using 400 ensemble members, 100 models with unbiased uniform sampling, and 100 regenerated models by the proposed method. We analyze two synthetic reservoirs with different permeability distributions: one is a typical heterogeneous reservoir and the other is a channel reservoir with a bimodal permeability distribution. Compared to the cases using all the 400 models with ensemble Kalman filter (EnKF), the simulation time is dramatically reduced to 4.7%, while the prediction quality on oil and water productions is improved. Even in the more complex reservoir case, the proposed method shows great improvements with reduced uncertainties against the other cases.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.


Lee, H. , Jin, J. , Shin, H. , and Choe, J. , 2015, “ Efficient Prediction of SAGD Productions Using Static Factor Clustering,” ASME J. Energy Resour. Technol., 137(3), p. 032907. [CrossRef]
Gao, G. , Zafari, M. , and Reynolds, A. C. , 2006, “ Quantifying Uncertainty for the PUNQ-S3 Problem in a Bayesian Setting With RML and EnKF,” SPE J., 11(4), pp. 506–515. [CrossRef]
Skjervheim, J.-A. , Evensen, G. , Aanonsen, S. I. , Ruud, B. O. , and Johansen, T. A. , 2007, “ Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter,” SPE J., 12(3), pp. 282–292. [CrossRef]
Kim, S. , Jung, H. , Lee, K. , and Choe, J. , 2017, “ Initial Ensemble Design Scheme for Effective Characterization of Three-Dimensional Channel Gas Reservoirs With an Aquifer,” ASME J. Energy Resour. Technol., 139(2), p. 022911. [CrossRef]
Gu, Y. , and Oliver, D. S. , 2005, “ The Ensemble Kalman Filter for Continuous Updating of Reservoir Simulation Models,” ASME J. Energy Resour. Technol., 128(1), pp. 79–87. [CrossRef]
Nævdal, G. , Mannseth, T. , and Vefring, E. H. , 2002, “ Near-Well Reservoir Monitoring Through Ensemble Kalman Filter,” SPE/DOE 13th Symposium of Improved Oil Recovery, Tulsa, OK, Apr. 13–17, SPE Paper No. 75235.
Gu, Y. , and Oliver, D. S. , 2005, “ History Matching of the PUNQ-S3 Reservoir Model Using the Ensemble Kalman Filter,” SPE J., 10(2), pp. 217–224. [CrossRef]
Chang, Y. , Stordal, A. S. , and Valestrand, R. , 2016, “ Integrated Work Flow of Preserving Facies Realism in History Matching: Application to the Brugge Field,” SPE J., 21(4), pp. 1413–1424. [CrossRef]
Lee, K. , Jung, S. P. , Lee, T. , and Choe, J. , 2016, “ Use of Clustered Covariance and Selective Measurement Data in Ensemble Smoother for Three-Dimensional Reservoir Characterization,” ASME J. Energy Resour. Technol., 139(2), p. 022905. [CrossRef]
Kang, B. , Yang, H. , Lee, K. , and Choe, J. , 2017, “ Ensemble Kalman Filter With Principal Component Analysis Assisted Sampling for Channelized Reservoir Characterization,” ASME J. Energy Resour. Technol., 139(3), p. 032907. [CrossRef]
Kang, B. , and Choe, J. , 2017, “ Initial Model Selection for Efficient History Matching of Channel Reservoirs Using Ensemble Smoother,” J. Pet. Sci. Eng., 152, pp. 294–308. [CrossRef]
Jafarpour, B. , and McLaughlin, D. B. , 2009, “ Estimating Channelized-Reservoir Permeabilities With the Ensemble Kalman Filter: The Importance of Ensemble Design,” SPE J., 14(2), pp. 374–388. [CrossRef]
Shin, Y. , Jeong, H. , and Choe, J. , 2010, “ Reservoir Characterization Using an EnKF and a Non-Parametric Approach for Highly Non-Gaussian Permeability Fields,” Energy Sources, Part A, 32(16), pp. 1569–1578. [CrossRef]
Dovera, L. , and Della Rossa, E. , 2011, “ Multimodal Ensemble Kalman Filtering Using Gaussian Mixture Models,” Comput. Geosci., 15(2), pp. 307–323. [CrossRef]
Liao, Q. , and Zhang, D. , 2015, “ Data Assimilation for Strongly Nonlinear Problems by Transformed Ensemble Kalman Filter,” SPE J., 20(1), pp. 202–221. [CrossRef]
Evensen, G. , 2004, “ Sampling Strategies and Square Root Analysis Schemes for the EnKF,” Ocean Dyn., 54(6), pp. 539–560. [CrossRef]
Aanonsen, S. I. , Nævdal, G. , Oliver, D. S. , Reynolds, A. C. , and Valle ̀s, B. , 2009, “ The Ensemble Kalman Filter in Reservoir Engineering—A Review,” SPE J., 14(3), pp. 393–412. [CrossRef]
Sætrom, J. , Hove, J. , Skjervheim, J. A. , and Vabø, J. G. , 2012, “ Improved Uncertainty Quantification in the Ensemble Kalman Filter Using Statistical Model-Selection Techniques,” SPE J., 17(1), pp. 152–162. [CrossRef]
Patel, R. G. , Trivedi, J. , Rahim, S. , and Li, Z. , 2015, “ Initial Sampling of Ensemble for Steam-Assisted-Gravity-Drainage-Reservoir History Matching,” SPE J., 54(6), pp. 424–441.
Kang, B. , Lee, K. , and Choe, J. , 2016, “ Improvement of Ensemble Smoother With SVD-Assisted Sampling Scheme,” J. Pet. Sci. Eng., 141, pp. 114–124. [CrossRef]
Suzuki, S. , Caumon, G. , and Caers, J. , 2008, “ Dynamic Data Integration for Structural Modeling: Model Screening Approach Using a Distance-Based Model Parameterization,” Comput. Geosci., 12(1), pp. 105–119. [CrossRef]
Scheidt, C. , and Caers, J. , 2009, “ Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods—Application to a West Africa Deepwater Turbidite Reservoir,” SPE J., 14(4), pp. 680–692. [CrossRef]
Park, J. , Jin, J. , and Choe, J. , 2015, “ Uncertainty Quantification Using Streamline Based Inversion and Distance Based Clustering,” ASME J. Energy Resour. Technol., 138(1), p. 012906. [CrossRef]
Park, K. , and Choe, J. , 2006, “ Use of Ensemble Kalman Filter to 3-Dimensional Reservoir Characterization During Waterflooding,” SPE EUROPEC/EAGE Annual Conference and Exhibition, Vienna, Austria, June 12–15, SPE Paper No. 100178.
Jeong, H. , Ki, S. , and Choe, J. , 2010, “ Reservoir Characterization From Insufficient Static Data Using Gradual Deformation Method With Ensemble Kalman Filter,” Energy Sources, Part A, 32(10), pp. 942–951. [CrossRef]
Caers, J. , 2003, “ History Matching Under Training-Image-Based Geological Model Constraints,” SPE J., 8(3), pp. 218–226. [CrossRef]
Idowu, N. A. , Nardi, C. , Long, H. , Varslot, T. , and Øren, P. E. , 2014, “ Effects of Segmentation and Skeletonization Algorithms on Pore Networks and Predicted Multiphase-Transport Properties of Reservoir-Rock Samples,” SPE J., 17(4), pp. 473–483.


Grahic Jump Location
Fig. 1

Example dataset of PCA process: (a) dataset in 3D space and (b) data in the PCA-assisted 2D

Grahic Jump Location
Fig. 2

Reservoir models in this study with different channel geometries in PCA plane

Grahic Jump Location
Fig. 3

Sampling procedures in this study using PCA and K-means clustering: (a) 400 models in PCA-assisted 2D plane, (b) candidate models (thick dot) and the selected model for the lowest RMSE (cross in the circle), and (c) the five representative models for model regeneration

Grahic Jump Location
Fig. 4

Procedures of the proposed model generation scheme: (a) selected channel reservoir model with two facies, (b) nine subgrid sections of the model for perturbation, (c) perturbation results of sand fraction for each subgrid section, and (d) nine examples of regenerated reservoir models

Grahic Jump Location
Fig. 5

Information on the reference reservoir model: (a) positions and permeability data used and (b) reference reservoir model and the well positions

Grahic Jump Location
Fig. 6

Five representative ensemble members selected (left) and their permeability histogram (right)

Grahic Jump Location
Fig. 7

Ten reservoir models regenerated using the representative members

Grahic Jump Location
Fig. 8

Performances in wells P2, P4, P7, and P8 of each case: (a) oil production rates and (b) watercuts

Grahic Jump Location
Fig. 9

The boxplots of total production at the 1000th day of each case. Values are normalized using the reference performance. (a) oil and (b) water.

Grahic Jump Location
Fig. 10

Information on the reference model in channel reservoir case: (a) static data used (left) and the training image (right) and (b) reference field and the well positions

Grahic Jump Location
Fig. 11

Five reservoir models selected (left) and their permeability histograms (right)

Grahic Jump Location
Fig. 12

Ten examples of regenerated ensemble members in the channel reservoir case

Grahic Jump Location
Fig. 13

Performances in wells P2, P4, P5, and P7 of each case in the channel reservoir case: (a) oil production rates and (b) watercuts

Grahic Jump Location
Fig. 14

Boxplots of total production at the 1000th day in the channelized reservoir case: (a) oil and (b) water



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In