For decision making, it is crucial to have proper reservoir characterization and uncertainty assessment of reservoir performances. Since initial models constructed with limited data have high uncertainty, it is essential to integrate both static and dynamic data for reliable future predictions. Uncertainty quantification is computationally demanding because it requires a lot of iterative forward simulations and optimizations in a single history matching, and multiple realizations of reservoir models should be computed. In this paper, a methodology is proposed to rapidly quantify uncertainties by combining streamline-based inversion and distance-based clustering. A distance between each reservoir model is defined as the norm of differences of generalized travel time (GTT) vectors. Then, reservoir models are grouped according to the distances and representative models are selected from each group. Inversions are performed on the representative models instead of using all models. We use generalized travel time inversion (GTTI) for the integration of dynamic data to overcome high nonlinearity and take advantage of computational efficiency. It is verified that the proposed method gathers models with both similar dynamic responses and permeability distribution. It also assesses the uncertainty of reservoir performances reliably, while reducing the amount of calculations significantly by using the representative models.
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January 2016
Research-Article
Uncertainty Quantification Using Streamline Based Inversion and Distance Based Clustering
Jihoon Park,
Jihoon Park
Department of Energy Resources Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: jhpark86@snu.ac.kr
Seoul National University,
Seoul 151-744, Korea
e-mail: jhpark86@snu.ac.kr
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Jeongwoo Jin,
Jeongwoo Jin
Department of Energy System Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: jin8146@snu.ac.kr
Seoul National University,
Seoul 151-744, Korea
e-mail: jin8146@snu.ac.kr
Search for other works by this author on:
Jonggeun Choe
Jonggeun Choe
Department of Energy Resources Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: johnchoe@snu.ac.kr
Seoul National University,
Seoul 151-744, Korea
e-mail: johnchoe@snu.ac.kr
Search for other works by this author on:
Jihoon Park
Department of Energy Resources Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: jhpark86@snu.ac.kr
Seoul National University,
Seoul 151-744, Korea
e-mail: jhpark86@snu.ac.kr
Jeongwoo Jin
Department of Energy System Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: jin8146@snu.ac.kr
Seoul National University,
Seoul 151-744, Korea
e-mail: jin8146@snu.ac.kr
Jonggeun Choe
Department of Energy Resources Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: johnchoe@snu.ac.kr
Seoul National University,
Seoul 151-744, Korea
e-mail: johnchoe@snu.ac.kr
1Present address: Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, jhpark3@stanford.edu.
2Corresponding author.
Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received June 30, 2014; final manuscript received July 11, 2015; published online September 29, 2015. Editor: Hameed Metghalchi.
J. Energy Resour. Technol. Jan 2016, 138(1): 012906 (6 pages)
Published Online: September 29, 2015
Article history
Received:
June 30, 2014
Revised:
July 11, 2015
Citation
Park, J., Jin, J., and Choe, J. (September 29, 2015). "Uncertainty Quantification Using Streamline Based Inversion and Distance Based Clustering." ASME. J. Energy Resour. Technol. January 2016; 138(1): 012906. https://doi.org/10.1115/1.4031446
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