Error handling strategies in multiphase inverse modeling
Parameter estimation by inverse modeling involves the repeated evaluation of a function of residuals. These residuals represent both errors in the model and errors in the data. In practical applications of inverse modeling of multiphase flow and transport, the error structure of the final residuals often significantly deviates from the statistical assumptions that underlie standard maximum likelihood estimation using the least-squares method. Large random or systematic errors are likely to lead to convergence problems, biased parameter estimates, misleading uncertainty measures, or poor predictive capabilities of the calibrated model. The multiphase inverse modeling code iTOUGH2 supports strategies that identify and mitigate the impact of systematic or non-normal error structures. We discuss these approaches and provide an overview of the error handling features implemented in iTOUGH2.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- Earth Sciences Division
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 1005170
- Report Number(s):
- LBNL-4255E; TRN: US201105%%214
- Journal Information:
- Computers and Geosciences, Related Information: Journal Publication Date: 2011; ISSN 0098-3004
- Country of Publication:
- United States
- Language:
- English
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