Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017WR022284 |
Discrete Regularization for Calibration of Geologic Facies Against Dynamic Flow Data | |
Khaninezhad, Mohammad-Reza1; Golmohammadi, Azarang1; Jafarpour, Behnam1,2 | |
2018-04-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:4页码:2523-2543 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Subsurface flow model calibration involves many more unknowns than measurements, leading to ill-posed problems with nonunique solutions. To alleviate nonuniqueness, the problem is regularized by constraining the solution space using prior knowledge. In certain sedimentary environments, such as fluvial systems, the contrast in hydraulic properties of different facies types tends to dominate the flow and transport behavior, making the effect of within facies heterogeneity less significant. Hence, flow model calibration in those formations reduces to delineating the spatial structure and connectivity of different lithofacies types and their boundaries. A major difficulty in calibrating such models is honoring the discrete, or piecewise constant, nature of facies distribution. The problem becomes more challenging when complex spatial connectivity patterns with higher-order statistics are involved. This paper introduces a novel formulation for calibration of complex geologic facies by imposing appropriate constraints to recover plausible solutions that honor the spatial connectivity and discreteness of facies models. To incorporate prior connectivity patterns, plausible geologic features are learned from available training models. This is achieved by learning spatial patterns from training data, e.g., k-SVD sparse learning or the traditional Principal Component Analysis. Discrete regularization is introduced as a penalty functions to impose solution discreteness while minimizing the mismatch between observed and predicted data. An efficient gradient-based alternating directions algorithm is combined with variable splitting to minimize the resulting regularized nonlinear least squares objective function. Numerical results show that imposing learned facies connectivity and discreteness as regularization functions leads to geologically consistent solutions that improve facies calibration quality. |
英文关键词 | subsurface model calibration discrete regularization alternating directions sparse reconstruction K-SVD dictionary |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000434186400002 |
WOS关键词 | TRANSIENT HYDRAULIC TOMOGRAPHY ; ENSEMBLE KALMAN FILTER ; PRIOR INFORMATION ; PUMPING TESTS ; STEADY-STATE ; HETEROGENEITY ; STATISTICS ; IDENTIFICATION ; REPRESENTATION ; DICTIONARIES |
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/21417 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Southern Calif, Dept Elect Engn, Los Angeles, CA 90089 USA; 2.Univ Southern Calif, Dept Chem Engn & Mat Sci, Los Angeles, CA 90007 USA |
推荐引用方式 GB/T 7714 | Khaninezhad, Mohammad-Reza,Golmohammadi, Azarang,Jafarpour, Behnam. Discrete Regularization for Calibration of Geologic Facies Against Dynamic Flow Data[J]. WATER RESOURCES RESEARCH,2018,54(4):2523-2543. |
APA | Khaninezhad, Mohammad-Reza,Golmohammadi, Azarang,&Jafarpour, Behnam.(2018).Discrete Regularization for Calibration of Geologic Facies Against Dynamic Flow Data.WATER RESOURCES RESEARCH,54(4),2523-2543. |
MLA | Khaninezhad, Mohammad-Reza,et al."Discrete Regularization for Calibration of Geologic Facies Against Dynamic Flow Data".WATER RESOURCES RESEARCH 54.4(2018):2523-2543. |
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