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Title: The compressed state Kalman filter for nonlinear state estimation: Application to large-scale reservoir monitoring

Journal Article · · Water Resources Research
DOI:https://doi.org/10.1002/2015WR017203· OSTI ID:1469116
 [1];  [1];  [2];  [3];  [4]
  1. Stanford Univ., CA (United States). Dept. of Civil and Environmental Engineering
  2. Stanford Univ., CA (United States). Dept. of Mechanical Engineering
  3. Stanford Univ., CA (United States). Dept. of Mechanical Engineering, and Inst. for Computational and Mathematical Engineering, Jen-Hsun Huang Engineering Center
  4. Stanford Univ., CA (United States). Dept. of Civil and Environmental Engineering, and Inst. for Computational and Mathematical Engineering, Jen-Hsun Huang Engineering Center
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Reservoir monitoring aims to provide snapshots of reservoir conditions and their uncertainties to assist operation management and risk analysis. These snapshots may contain millions of state variables, e.g., pressures and saturations, which can be estimated by assimilating data in real time using the Kalman filter (KF). However, the KF has a computational cost that scales quadratically with the number of unknowns, m, due to the cost of computing and storing the covariance and Jacobian matrices, along with their prod-ucts. The compressed state Kalman filter (CSKF) adapts the KF for solving large-scale monitoring problems. The CSKF uses N preselected orthogonal bases to compute an accurate rank-N approximation of the covari-ance that is close to the optimal spectral approximation given by SVD. The CSKF has a computational cost that scales linearly in m and uses an efficient matrix-free approach that propagates uncertainties using N + 1 forward model evaluations, where N << m. Here in this paper we present a generalized CSKF algorithm for nonlin-ear state estimation problems such as CO2 monitoring. For simultaneous estimation of multiple types of state variables, the algorithm allows selecting bases that represent the variability of each state type. Through synthetic numerical experiments of CO2 monitoring, we show that the CSKF can reproduce the Kal-man gain accurately even for large compression ratios (m/N). For a given computational cost, the CSKF uses a robust and flexible compression scheme that gives more reliable uncertainty estimates than the ensemble Kalman filter, which may display loss of ensemble variability leading to suboptimal uncertainty estimates.

Research Organization:
Stanford Univ., CA (United States)
Sponsoring Organization:
USDOE; National Science Foundation (NSF)
Grant/Contract Number:
FE0009260
OSTI ID:
1469116
Journal Information:
Water Resources Research, Vol. 51, Issue 12; ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 19 works
Citation information provided by
Web of Science

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Cited By (3)

Hydroclimatic variability and predictability: a survey of recent research journal January 2017
Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting journal January 2018
Hydroclimatic Variability and Predictability: A Survey of Recent Research journal March 2017

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97 MATHEMATICS AND COMPUTING
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