History matching is essential for estimating reservoir performances and decision makings. Ensemble Kalman filter (EnKF) has been researched for inverse modeling due to lots of advantages such as uncertainty quantification, real-time updating, and easy coupling with any forward simulator. However, it requires lots of forward simulations due to recursive update. Although ensemble smoother (ES) is much faster than EnKF, it is more vulnerable to overshooting and filter divergence problems. In this research, ES is coupled with both clustered covariance and selective measurement data to manage the two typical problems mentioned. As preprocessing work of clustered covariance, reservoir models are grouped by the distance-based method, which consists of Minkowski distance, multidimensional scaling, and K-means clustering. Also, meaningless measurement data are excluded from assimilation such as shut-in bottomhole pressures, which are too similar on every well. For a benchmark model, PUNQ-S3, a standard ES with 100 ensembles, shows severe over- and undershooting problem with log-permeability values from 36.5 to −17.3. The concept of the selective use of observed data partially mitigates the problem, but it cannot match the true production. However, the proposed method, ES with clustered covariance and selective measurement data together, manages the overshooting problem and follows histogram of the permeability in the reference field. Uncertainty quantifications on future field productions give reliable prediction, containing the true performances. Therefore, this research extends the applicatory of ES to 3D reservoirs by improving reliability issues.
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March 2017
Research-Article
Use of Clustered Covariance and Selective Measurement Data in Ensemble Smoother for Three-Dimensional Reservoir Characterization
Kyungbook Lee,
Kyungbook Lee
Petroleum and Marine Research Division,
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
Search for other works by this author on:
Seungpil Jung,
Seungpil Jung
E&P Business Division,
SK Innovation,
Seoul 03188, South Korea
SK Innovation,
Seoul 03188, South Korea
Search for other works by this author on:
Taehun Lee,
Taehun Lee
Petroleum and Marine Research Division,
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
Search for other works by this author on:
Jonggeun Choe
Jonggeun Choe
Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr
Search for other works by this author on:
Kyungbook Lee
Petroleum and Marine Research Division,
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
Seungpil Jung
E&P Business Division,
SK Innovation,
Seoul 03188, South Korea
SK Innovation,
Seoul 03188, South Korea
Taehun Lee
Petroleum and Marine Research Division,
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
Korea Institute of Geoscience
and Mineral Resources,
Daejeon 34132, South Korea
Jonggeun Choe
Department of Energy Systems Engineering,
Seoul National University,
Seoul 08826, South Korea
e-mail: johnchoe@snu.ac.kr
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 January 22, 2016; final manuscript received August 2, 2016; published online August 23, 2016. Assoc. Editor: Daoyong (Tony) Yang.
J. Energy Resour. Technol. Mar 2017, 139(2): 022905 (9 pages)
Published Online: August 23, 2016
Article history
Received:
January 22, 2016
Revised:
August 2, 2016
Citation
Lee, K., Jung, S., Lee, T., and Choe, J. (August 23, 2016). "Use of Clustered Covariance and Selective Measurement Data in Ensemble Smoother for Three-Dimensional Reservoir Characterization." ASME. J. Energy Resour. Technol. March 2017; 139(2): 022905. https://doi.org/10.1115/1.4034443
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