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Record W1991372054 · doi:10.2118/170082-ms

Remote Monitoring of SAGD Operations with Satellite based InSAR

2014· article· en· W1991372054 on OpenAlexaffabout
Michael D. Henschel, Luyi Shen, Todd Shipman, Brad Lehrbass, Guy Aubé

Bibliographic record

VenueSPE Heavy Oil Conference-Canada · 2014
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsCanadian Standards AssociationCanadian Space AgencyAlberta Energy
Fundersnot available
KeywordsInterferometric synthetic aperture radarRemote sensingGeologySatelliteSynthetic aperture radarDeformation (meteorology)GeodesyAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Surface deformation, measured from space, provides a means to remotely monitor Enhanced Oil Recovery (EOR) activities. Using Interferometric Synthetic Aperture Radar (InSAR) to measure surface deformation allows for a common measuring stick with no access issues. Currently surface deformation is measured using many alternative approaches, some of which are less economical and not feasible at the regional scale when compared to InSAR. Information collected from InSAR could be used to develop an informed planning mechanism. Space based SAR data from January 2009 to August 2011 was examined over a SAGD site in Alberta, Canada. The data was extracted from the satellite archives and advanced InSAR processing techniques were applied to measure the surface deformation over the site. The InSAR results show the extreme variability in the ground conditions that are to be expected in the region. The surface deformation was measured using a combination of corner reflectors and infrastructure targets. The highly variable ground conditions make monitoring of subtle deformation signals (2.5 – 5 cm/yr) difficult to measure without the use of installed targets. The challenges of InSAR monitoring without installed targets are addressed to emphasize the importance of on-site ground control points. Furthermore, the surface changes observed with the InSAR results are related to reservoir changes. Geomechanical simulation is dependent on many rock physics parameters and complex geological frameworks. History matching with InSAR observation provides enhanced prediction and estimation of reservoir growth, which can inform decisions related to reservoir performance and caprock integrity. This paper is a result of a collaboration between the Alberta Energy Regulator, MDA Geospatial Services Inc. (MDA), and the Canadian Space Agency (CSA) to examine the requirements for rapid-turnaround measurement of ground deformation over enhanced oil recovery sites in the Alberta Oil Sands. Funding for this effort was partially provided by the CSA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.197
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2014
Admission routes2
Has abstractyes

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