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DOI | 10.1002/2017WR021870 |
Randomized Truncated SVD Levenberg-Marquardt Approach to Geothermal Natural State and History Matching | |
Bjarkason, Elvar K.; 39;Sullivan, John P.; 39;Sullivan, Michael J. | |
2018-03-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:3页码:2376-2404 |
文章类型 | Article |
语种 | 英语 |
国家 | New Zealand |
英文摘要 | The Levenberg-Marquardt (LM) method is commonly used for inverting models used to describe geothermal, groundwater, or oil and gas reservoirs. In previous studies, LM parameter updates have been made tractable for highly parameterized inverse problems with large data sets by applying matrix factorization methods or iterative linear solvers to approximately solve the update equations. Some studies have shown that basing model updates on the truncated singular value decomposition (TSVD) of a dimensionless sensitivity matrix achieved using Lanczos iteration can speed up the inversion of reservoir models. Lanczos iterations only require the sensitivity matrix times a vector and its transpose times a vector, which are found efficiently using adjoint and direct simulations without the expense of forming a large sensitivity matrix. Nevertheless, Lanczos iteration has the drawback of being a serial process, requiring a separate adjoint solve and direct solve every Lanczos iteration. Randomized methods, developed for low-rank matrix approximation of large matrices, are more efficient alternatives to the standard Lanczos method. Here we develop LM variants which use randomized methods to find a TSVD of a dimensionless sensitivity matrix when updating parameters. The randomized approach offers improved efficiency by enabling simultaneous solution of all adjoint and direct problems for a parameter update. |
英文关键词 | inversion geothermal reservoir simulation randomized SVD singular value decomposition Levenberg-Marquardt adjoint method |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000430364900052 |
WOS关键词 | RESERVOIR PERFORMANCE PREDICTIONS ; UNCERTAINTY QUANTIFICATION ; GEOSTATISTICAL APPROACH ; ALGORITHM ; FLOW ; PARAMETERIZATION ; SIMULATION ; MODEL ; RML |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/20084 |
专题 | 资源环境科学 |
作者单位 | Univ Auckland, Dept Engn Sci, Auckland, New Zealand |
推荐引用方式 GB/T 7714 | Bjarkason, Elvar K.,39;Sullivan, John P.,39;Sullivan, Michael J.. Randomized Truncated SVD Levenberg-Marquardt Approach to Geothermal Natural State and History Matching[J]. WATER RESOURCES RESEARCH,2018,54(3):2376-2404. |
APA | Bjarkason, Elvar K.,39;Sullivan, John P.,&39;Sullivan, Michael J..(2018).Randomized Truncated SVD Levenberg-Marquardt Approach to Geothermal Natural State and History Matching.WATER RESOURCES RESEARCH,54(3),2376-2404. |
MLA | Bjarkason, Elvar K.,et al."Randomized Truncated SVD Levenberg-Marquardt Approach to Geothermal Natural State and History Matching".WATER RESOURCES RESEARCH 54.3(2018):2376-2404. |
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