Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario

Autor: Christian Rohde, Markus Köppel, Dirk Pflüger, Fabian Franzelin, Ilja Kröker, Wolfgang Nowak, Andrea Barth, Bernard Haasdonk, Gabriele Santin, Dominik Wittwar, Sergey Oladyshkin
Rok vydání: 2018
Předmět:
FOS: Computer and information sciences
Mathematical optimization
Computer science
0208 environmental biotechnology
Monte Carlo method
02 engineering and technology
010502 geochemistry & geophysics
01 natural sciences
65D05
65D15
65C20
Computational Engineering
Finance
and Science (cs.CE)
FOS: Mathematics
Mathematics - Numerical Analysis
Computers in Earth Sciences
Uncertainty quantification
Computer Science - Computational Engineering
Finance
and Science
0105 earth and related environmental sciences
Polynomial chaos
Computer Science - Numerical Analysis
Sparse grid
Numerical Analysis (math.NA)
020801 environmental engineering
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Kernel (statistics)
Benchmark (computing)
Probability distribution
Interpolation
Zdroj: Computational Geosciences. 23:339-354
ISSN: 1573-1499
1420-0597
DOI: 10.1007/s10596-018-9785-x
Popis: A variety of methods is available to quantify uncertainties arising within the modeling of flow and transport in carbon dioxide storage, but there is a lack of thorough comparisons. Usually, raw data from such storage sites can hardly be described by theoretical statistical distributions since only very limited data is available. Hence, exact information on distribution shapes for all uncertain parameters is very rare in realistic applications. We discuss and compare four different methods tested for data-driven uncertainty quantification based on a benchmark scenario of carbon dioxide storage. In the benchmark, for which we provide data and code, carbon dioxide is injected into a saline aquifer modeled by the nonlinear capillarity-free fractional flow formulation for two incompressible fluid phases, namely carbon dioxide and brine. To cover different aspects of uncertainty quantification, we incorporate various sources of uncertainty such as uncertainty of boundary conditions, of parameters in constitutive relations, and of material properties. We consider recent versions of the following non-intrusive and intrusive uncertainty quantification methods: arbitrary polynomial chaos, spatially adaptive sparse grids, kernel-based greedy interpolation, and hybrid stochastic Galerkin. The performance of each approach is demonstrated assessing expectation value and standard deviation of the carbon dioxide saturation against a reference statistic based on Monte Carlo sampling. We compare the convergence of all methods reporting on accuracy with respect to the number of model runs and resolution. Finally, we offer suggestions about the methods’ advantages and disadvantages that can guide the modeler for uncertainty quantification in carbon dioxide storage and beyond.
Databáze: OpenAIRE
Externí odkaz: