An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling
Abstract
We present that in numerical modeling of geological carbon sequestration (GCS), uncertainty quantification (UQ) is usually needed to evaluate the impact of uncertain model parameters on model predictions caused by limited measurements and incomplete knowledge of the parameters. However, UQ for GCS is computationally expensive due to the large ensemble of complex and lengthy model simulations. In this study, we propose an adaptive Kriging method to build a fast-to-evaluate surrogate of the GCS model to alleviate the heavy computational burden. The surrogate model is efficiently generated using a Taylor expansion-based adaptive experimental design algorithm that combines a distance-based exploration criterion and an exploitation criterion to adaptively search for informative training samples. In addition, we analyze the uncertainty brought by substituting the surrogate for the actual simulation model and explore its influence on UQ results. Lastly, our method is demonstrated in a synthetic GCS model and its performance is evaluated in comparison with the conventional Monte Carlo sampling. Results indicate that our method can greatly improve the computational efficiency in UQ and provide an effective and reliable UQ solution with the consideration of surrogate uncertainty.
- Authors:
-
- Nanjing University (China)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Florida State Univ., Tallahassee, FL (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1495937
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computers and Geosciences
- Additional Journal Information:
- Journal Volume: 125; Journal Issue: C; Journal ID: ISSN 0098-3004
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING; Geological carbon sequestration; Surrogate modeling; Adaptive experimental design; Kriging; Uncertainty quantification
Citation Formats
Mo, Shaoxing, Shi, Xiaoqing, Lu, Dan, Ye, Ming, and Wu, Jichun. An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling. United States: N. p., 2019.
Web. doi:10.1016/j.cageo.2019.01.012.
Mo, Shaoxing, Shi, Xiaoqing, Lu, Dan, Ye, Ming, & Wu, Jichun. An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling. United States. https://doi.org/10.1016/j.cageo.2019.01.012
Mo, Shaoxing, Shi, Xiaoqing, Lu, Dan, Ye, Ming, and Wu, Jichun. Tue .
"An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling". United States. https://doi.org/10.1016/j.cageo.2019.01.012. https://www.osti.gov/servlets/purl/1495937.
@article{osti_1495937,
title = {An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling},
author = {Mo, Shaoxing and Shi, Xiaoqing and Lu, Dan and Ye, Ming and Wu, Jichun},
abstractNote = {We present that in numerical modeling of geological carbon sequestration (GCS), uncertainty quantification (UQ) is usually needed to evaluate the impact of uncertain model parameters on model predictions caused by limited measurements and incomplete knowledge of the parameters. However, UQ for GCS is computationally expensive due to the large ensemble of complex and lengthy model simulations. In this study, we propose an adaptive Kriging method to build a fast-to-evaluate surrogate of the GCS model to alleviate the heavy computational burden. The surrogate model is efficiently generated using a Taylor expansion-based adaptive experimental design algorithm that combines a distance-based exploration criterion and an exploitation criterion to adaptively search for informative training samples. In addition, we analyze the uncertainty brought by substituting the surrogate for the actual simulation model and explore its influence on UQ results. Lastly, our method is demonstrated in a synthetic GCS model and its performance is evaluated in comparison with the conventional Monte Carlo sampling. Results indicate that our method can greatly improve the computational efficiency in UQ and provide an effective and reliable UQ solution with the consideration of surrogate uncertainty.},
doi = {10.1016/j.cageo.2019.01.012},
journal = {Computers and Geosciences},
number = C,
volume = 125,
place = {United States},
year = {Tue Jan 29 00:00:00 EST 2019},
month = {Tue Jan 29 00:00:00 EST 2019}
}
Web of Science
Works referenced in this record:
A review of surrogate models and their application to groundwater modeling: SURROGATES OF GROUNDWATER MODELS
journal, August 2015
- Asher, M. J.; Croke, B. F. W.; Jakeman, A. J.
- Water Resources Research, Vol. 51, Issue 8
Geological storage of CO2: Application, feasibility and efficiency of global sensitivity analysis and risk assessment using the arbitrary polynomial chaos
journal, November 2013
- Ashraf, Meisam; Oladyshkin, Sergey; Nowak, Wolfgang
- International Journal of Greenhouse Gas Control, Vol. 19
Two-dimensional reactive transport modeling of CO2 injection in a saline aquifer at the Sleipner site, North Sea
journal, September 2007
- Audigane, P.; Gaus, I.; Czernichowski-Lauriol, I.
- American Journal of Science, Vol. 307, Issue 7
Convergence assessment of numerical Monte Carlo simulations in groundwater hydrology: TECHNICAL NOTE
journal, April 2004
- Ballio, Francesco; Guadagnini, Alberto
- Water Resources Research, Vol. 40, Issue 4
Multiphase Modeling of Geologic Carbon Sequestration in Saline Aquifers
journal, February 2015
- Bandilla, Karl W.; Celia, Michael A.; Birkholzer, Jens T.
- Groundwater, Vol. 53, Issue 3
Bayesian Calibration and Uncertainty Analysis for Computationally Expensive Models Using Optimization and Radial Basis Function Approximation
journal, June 2008
- Bliznyuk, Nikolay; Ruppert, David; Shoemaker, Christine
- Journal of Computational and Graphical Statistics, Vol. 17, Issue 2
Gaussian process modelling for uncertainty quantification in convectively-enhanced dissolution processes in porous media
journal, January 2017
- Crevillén-García, D.; Wilkinson, R. D.; Shah, A. A.
- Advances in Water Resources, Vol. 99
Geochemical aspects of CO2 sequestration in deep saline aquifers: A review
journal, September 2015
- De Silva, G. P. D.; Ranjith, P. G.; Perera, M. S. A.
- Fuel, Vol. 155
Adaptive sequential sampling for surrogate model generation with artificial neural networks
journal, September 2014
- Eason, John; Cremaschi, Selen
- Computers & Chemical Engineering, Vol. 68
Comparison of Gaussian process modeling software
journal, April 2018
- Erickson, Collin B.; Ankenman, Bruce E.; Sanchez, Susan M.
- European Journal of Operational Research, Vol. 266, Issue 1
Comparison of optimization algorithms for parameter estimation of multi-phase flow models with application to geological carbon sequestration
journal, April 2013
- Espinet, Antoine J.; Shoemaker, Christine A.
- Advances in Water Resources, Vol. 54
Estimation of plume distribution for carbon sequestration using parameter estimation with limited monitoring data: CO
journal, July 2013
- Espinet, Antoine; Shoemaker, Christine; Doughty, Christine
- Water Resources Research, Vol. 49, Issue 7
Geochemical Implications of Gas Leakage associated with Geologic CO 2 Storage—A Qualitative Review
journal, August 2012
- Harvey, Omar R.; Qafoku, Nikolla P.; Cantrell, Kirk J.
- Environmental Science & Technology, Vol. 47, Issue 1
An overview of the underground disposal of carbon dioxide
journal, January 1997
- Holloway, Sam
- Energy Conversion and Management, Vol. 38
Uncertainty analyses of CO2 plume expansion subsequent to wellbore CO2 leakage into aquifers
journal, August 2014
- Hou, Zhangshuan; Bacon, Diana H.; Engel, Dave W.
- International Journal of Greenhouse Gas Control, Vol. 27
Probabilistic analysis of CO2 storage mechanisms in a CO2-EOR field using polynomial chaos expansion
journal, August 2016
- Jia, Wei; McPherson, Brian J.; Pan, Feng
- International Journal of Greenhouse Gas Control, Vol. 51
Developments since 2005 in understanding potential environmental impacts of CO2 leakage from geological storage
journal, September 2015
- Jones, D. G.; Beaubien, S. E.; Blackford, J. C.
- International Journal of Greenhouse Gas Control, Vol. 40
A response surface model to predict CO2 and brine leakage along cemented wellbores
journal, February 2015
- Jordan, Amy B.; Stauffer, Philip H.; Harp, Dylan
- International Journal of Greenhouse Gas Control, Vol. 33
An adaptive Gaussian process-based iterative ensemble smoother for data assimilation
journal, May 2018
- Ju, Lei; Zhang, Jiangjiang; Meng, Long
- Advances in Water Resources, Vol. 115
Persistent questions of heterogeneity, uncertainty, and scale in subsurface flow and transport: PERSISTENT QUESTIONS IN SUBSURFACE FLOW AND TRANSPORT
journal, August 2015
- Kitanidis, Peter K.
- Water Resources Research, Vol. 51, Issue 8
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design
journal, June 2017
- Liu, Haitao; Ong, Yew-Soon; Cai, Jianfei
- Structural and Multidisciplinary Optimization, Vol. 57, Issue 1
An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations: AN IMPROVED MLMC METHOD
journal, December 2016
- Lu, Dan; Zhang, Guannan; Webster, Clayton
- Water Resources Research, Vol. 52, Issue 12
An efficient Bayesian data-worth analysis using a multilevel Monte Carlo method
journal, March 2018
- Lu, Dan; Ricciuto, Daniel; Evans, Katherine
- Advances in Water Resources, Vol. 113
A Taylor Expansion‐Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling
journal, December 2017
- Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing
- Water Resources Research, Vol. 53, Issue 12
A new model for predicting the hydraulic conductivity of unsaturated porous media
journal, June 1976
- Mualem, Yechezkel
- Water Resources Research, Vol. 12, Issue 3
Bayesian inference for the uncertainty distribution of computer model outputs
journal, December 2002
- Oakley, J.
- Biometrika, Vol. 89, Issue 4
A concept for data-driven uncertainty quantification and its application to carbon dioxide storage in geological formations
journal, November 2011
- Oladyshkin, S.; Class, H.; Helmig, R.
- Advances in Water Resources, Vol. 34, Issue 11
Uncertainty analysis of carbon sequestration in an active CO2-EOR field
journal, August 2016
- Pan, Feng; McPherson, Brian J.; Dai, Zhenxue
- International Journal of Greenhouse Gas Control, Vol. 51
Reduced order models for many-query subsurface flow applications
journal, May 2013
- Pau, George Shu Heng; Zhang, Yingqi; Finsterle, Stefan
- Computational Geosciences, Vol. 17, Issue 4
Reduced order modeling in iTOUGH2
journal, April 2014
- Pau, George Shu Heng; Zhang, Yingqi; Finsterle, Stefan
- Computers & Geosciences, Vol. 65
Review of surrogate modeling in water resources: REVIEW
journal, July 2012
- Razavi, Saman; Tolson, Bryan A.; Burn, Donald H.
- Water Resources Research, Vol. 48, Issue 7
Design and Analysis of Computer Experiments
journal, November 1989
- Sacks, Jerome; Welch, William J.; Mitchell, Toby J.
- Statistical Science, Vol. 4, Issue 4
Computational Modeling of the Geologic Sequestration of Carbon Dioxide
journal, January 2009
- Schnaar, Gregory; Digiulio, Dominic C.
- Vadose Zone Journal, Vol. 8, Issue 2
Variance-based sensitivity analysis of model outputs using surrogate models
journal, June 2011
- Shahsavani, D.; Grimvall, A.
- Environmental Modelling & Software, Vol. 26, Issue 6
Assessment of parametric uncertainty for groundwater reactive transport modeling
journal, May 2014
- Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.
- Water Resources Research, Vol. 50, Issue 5
Global sensitivity analysis using polynomial chaos expansions
journal, July 2008
- Sudret, Bruno
- Reliability Engineering & System Safety, Vol. 93, Issue 7
Assessing leakage detectability at geologic CO2 sequestration sites using the probabilistic collocation method
journal, June 2013
- Sun, Alexander Y.; Zeidouni, Mehdi; Nicot, Jean-Philippe
- Advances in Water Resources, Vol. 56
Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning
journal, April 2018
- Sun, Alexander Y.; Jeong, Hoonyoung; González-Nicolás, Ana
- Computers & Geosciences, Vol. 113
Assessment and management of risk in subsurface hydrology: A review and perspective
journal, January 2013
- Tartakovsky, Daniel M.
- Advances in Water Resources, Vol. 51
Gaussian process emulators for quantifying uncertainty in spreading predictions in heterogeneous media
journal, August 2017
- Tian, Liang; Wilkinson, Richard; Yang, Zhibing
- Computers & Geosciences, Vol. 105
A Fuzzy Hybrid Sequential Design Strategy for Global Surrogate Modeling of High-Dimensional Computer Experiments
journal, January 2015
- van der Herten, J.; Couckuyt, I.; Deschrijver, D.
- SIAM Journal on Scientific Computing, Vol. 37, Issue 2
A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1
journal, January 1980
- van Genuchten, M. Th.
- Soil Science Society of America Journal, Vol. 44, Issue 5
Review: Approaches to research on CO2/brine two-phase migration in saline aquifers
journal, August 2014
- Wang, Dayong; Dong, Bo; Breen, Stephen
- Hydrogeology Journal, Vol. 23, Issue 1
Evaluating two sparse grid surrogates and two adaptation criteria for groundwater Bayesian uncertainty quantification
journal, April 2016
- Zeng, Xiankui; Ye, Ming; Burkardt, John
- Journal of Hydrology, Vol. 535
Improved Nested Sampling and Surrogate-Enabled Comparison With Other Marginal Likelihood Estimators
journal, February 2018
- Zeng, Xiankui; Ye, Ming; Wu, Jichun
- Water Resources Research, Vol. 54, Issue 2
An efficient integrated approach for global sensitivity analysis of hydrological model parameters
journal, March 2013
- Zhan, Che-sheng; Song, Xiao-meng; Xia, Jun
- Environmental Modelling & Software, Vol. 41
An efficient, high-order perturbation approach for flow in random porous media via Karhunen–Loève and polynomial expansions
journal, March 2004
- Zhang, Dongxiao; Lu, Zhiming
- Journal of Computational Physics, Vol. 194, Issue 2
Uncertainty Quantification in CO 2 Sequestration Using Surrogate Models from Polynomial Chaos Expansion
journal, June 2012
- Zhang, Yan; Sahinidis, Nikolaos V.
- Industrial & Engineering Chemistry Research, Vol. 52, Issue 9
An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling: Sparse-Grid Method for Bayesian Inference
journal, October 2013
- Zhang, Guannan; Lu, Dan; Ye, Ming
- Water Resources Research, Vol. 49, Issue 10
An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems: ADAPTIVE GAUSSIAN PROCESS-BASED INVERSION
journal, August 2016
- Zhang, Jiangjiang; Li, Weixuan; Zeng, Lingzao
- Water Resources Research, Vol. 52, Issue 8
Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification
journal, August 2018
- Zhu, Yinhao; Zabaras, Nicholas
- Journal of Computational Physics, Vol. 366
Works referencing / citing this record:
Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification
journal, May 2019
- Mo, Shaoxing; Zabaras, Nicholas; Shi, Xiaoqing
- Water Resources Research, Vol. 55, Issue 5
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification
text, January 2018
- Mo, Shaoxing; Zabaras, Nicholas; Shi, Xiaoqing
- Unpublished
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification
text, January 2018
- Mo, Shaoxing; Zabaras, Nicholas; Shi, Xiaoqing
- arXiv