Identification of deformation pattern changes caused by enhanced oil recovery (EOR) using InSAR

L. Chang (Corresponding Author), Ou Ku, Ramon Hanssen

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Continuous hydrocarbon production and steam/water injection cause compaction and expansion of the reservoir rock, leading to irregular downward and upward ground movements. Detecting such anthropogenic ground movements is of importance, as they may significantly influence the safety and sustainability of hydrocarbon production activities, in particular, enhanced oil recovery (EOR) and even lead to local hazards, e.g. earthquakes and sinkholes. As InSAR (Interferometric Synthetic Aperture Radar) can routinely deliver global ground deformation observations on a weekly basis, with millimetre-level precision, it can be a cost-effective, and less labour intensive tool to monitor surface deformation changes due to hydrocarbon production activities. Aimed at identifying the associated deformation pattern changes, this study focuses on InSAR deformation model optimization, in order to automatically detect irregularities, both spatially and temporally. We apply multiple hypothesis testing to determine the best model based on a library of physically realistic canonical deformation models. We develop a cluster-wise constrained least-squares estimation method for parameter estimation, in order to directly introduce contextual information, such as spatio-temporal correlation, into the mathematical model. Here a cluster represents a group of spatially correlated InSAR measurement points. Our approach is demonstrated over an enhanced oil recovery site using a stack of TerraSAR-X images
Original languageEnglish
Pages (from-to)1495-1505
Number of pages11
JournalInternational journal of remote sensing
Volume40
Issue number4
Early online date16 Oct 2018
DOIs
Publication statusPublished - 2019

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enhanced oil recovery
synthetic aperture radar
ground movement
hydrocarbon
TerraSAR-X
sinkhole
hypothesis testing
reservoir rock
estimation method
seismic hazard
compaction
labor
sustainability
safety
cost
water

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID
  • UT-Hybrid-D

Cite this

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title = "Identification of deformation pattern changes caused by enhanced oil recovery (EOR) using InSAR",
abstract = "Continuous hydrocarbon production and steam/water injection cause compaction and expansion of the reservoir rock, leading to irregular downward and upward ground movements. Detecting such anthropogenic ground movements is of importance, as they may significantly influence the safety and sustainability of hydrocarbon production activities, in particular, enhanced oil recovery (EOR) and even lead to local hazards, e.g. earthquakes and sinkholes. As InSAR (Interferometric Synthetic Aperture Radar) can routinely deliver global ground deformation observations on a weekly basis, with millimetre-level precision, it can be a cost-effective, and less labour intensive tool to monitor surface deformation changes due to hydrocarbon production activities. Aimed at identifying the associated deformation pattern changes, this study focuses on InSAR deformation model optimization, in order to automatically detect irregularities, both spatially and temporally. We apply multiple hypothesis testing to determine the best model based on a library of physically realistic canonical deformation models. We develop a cluster-wise constrained least-squares estimation method for parameter estimation, in order to directly introduce contextual information, such as spatio-temporal correlation, into the mathematical model. Here a cluster represents a group of spatially correlated InSAR measurement points. Our approach is demonstrated over an enhanced oil recovery site using a stack of TerraSAR-X images",
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Identification of deformation pattern changes caused by enhanced oil recovery (EOR) using InSAR. / Chang, L. (Corresponding Author); Ku, Ou; Hanssen, Ramon.

In: International journal of remote sensing, Vol. 40, No. 4, 2019, p. 1495-1505.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Chang, L.

AU - Ku, Ou

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