Error handling strategies in multiphase inverse modeling
Abstract
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.
- Authors:
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- Earth Sciences Division
- OSTI Identifier:
- 1005170
- Report Number(s):
- LBNL-4255E
Journal ID: ISSN 0098-3004; TRN: US201105%%214
- DOE Contract Number:
- DE-AC02-05CH11231
- Resource Type:
- Journal Article
- Journal Name:
- Computers and Geosciences
- Additional Journal Information:
- Related Information: Journal Publication Date: 2011; Journal ID: ISSN 0098-3004
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54; 58; CONVERGENCE; EVALUATION; MULTIPHASE FLOW; SIMULATION; TRANSPORT
Citation Formats
Finsterle, S, and Zhang, Y. Error handling strategies in multiphase inverse modeling. United States: N. p., 2010.
Web. doi:10.1016/j.cageo.2010.11.009.
Finsterle, S, & Zhang, Y. Error handling strategies in multiphase inverse modeling. United States. https://doi.org/10.1016/j.cageo.2010.11.009
Finsterle, S, and Zhang, Y. 2010.
"Error handling strategies in multiphase inverse modeling". United States. https://doi.org/10.1016/j.cageo.2010.11.009. https://www.osti.gov/servlets/purl/1005170.
@article{osti_1005170,
title = {Error handling strategies in multiphase inverse modeling},
author = {Finsterle, S and Zhang, Y},
abstractNote = {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.},
doi = {10.1016/j.cageo.2010.11.009},
url = {https://www.osti.gov/biblio/1005170},
journal = {Computers and Geosciences},
issn = {0098-3004},
number = ,
volume = ,
place = {United States},
year = {Wed Dec 01 00:00:00 EST 2010},
month = {Wed Dec 01 00:00:00 EST 2010}
}