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Record W2586508643 · doi:10.2118/184971-ms

Satellite Monitoring of Cyclic Steam Stimulation without Corner Reflectors

2017· article· en· W2586508643 on OpenAlexaboutno aff
Michael D. Henschel, J. P. Dudley, Peter Chung

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsInterferometric synthetic aperture radarRemote sensingGeologySynthetic aperture radarGeodesyInterferometrySatelliteRadarSteam injectionEnvironmental scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Surface movement of a field under cyclic steam stimulation (CSS) is induced by the thermal processes used to extract bitumen from the reservoir. The ground movement can be related to reservoir dilation and compaction and provides a record of the effect of injection and production. Accurate monitoring of the ground deformation that occurs at a CSS field can be used to calibrate predictive models, show the effectiveness of steam injection, and demonstrate the impact of the recovery operation on the surface elevation. Accurate monitoring, however, requires the ability to consistently measure large non-linear changes in the surface height over relatively small areas. CSS sites can experience 20+ cm of surface heave in one month. The spatial extent of the ground movement is related to the well layout. Along short distances, 10s of metres, the ground movement (heave and subsidence) may be more than 2 cm. Interferometric Synthetic Aperture Radar (InSAR) uses radar returns from the ground to calculate very precise estimates of the ground change. In arid regions, InSAR can be used to capture a very high density of ground movement points. In this case, measurements with a density of approximately every 3 m are possible with the RADARSAT-2 satellite. The ground conditions in arid regions are ideal for radar observation. The ground conditions in the region of the Alberta oil sands are not ideal for InSAR monitoring. The amount of ground water, variations due to seasonal change, and the sparsity of effective radar reflectors led to the development of InSAR methods that employ installed targets (or corner reflectors). This methodology works very well to capture the relatively slow ground change associated with steam assisted gravity drainage (SAGD) operations. The same measurement process at a CSS operation would require an extreme density of corner reflectors. It would not be environmentally or economically feasible to install corner reflectors at a spacing of 75 m across a CSS operation. To improve the accuracy of InSAR surface elevation monitoring, we have created a new way of extracting deformation information from radar imagery. This has worked particularly well for the CSS process at the Primrose field. The methodology extracts the signal in high-noise conditions and dramatically increases the effectiveness of InSAR observations over a CSS field. The new InSAR methodology exploits the spatial and temporal characteristics of the radar images to produce a more robust estimate of the ground movement than had been possible previously. The wide-area InSAR measurements are compared to measurements from Global Positioning System (GPS) and to the net over injection (NOI) metric, which describes the amount of steam going into the reservoir less the amount of produced fluids. The GPS time series and the InSAR measurements show excellent agreement. The point source nature of the GPS data, however, restricts the spatial comparison that is possible. In this case, the NOI data shows the progression of injection / production spatially and temporally. The ground movement patterns from the NOI and the InSAR data are very similar. Increases in NOI are very highly correlated with surface heave. Subsidence seems to lag decreases in NOI slightly but is still clearly positively correlated.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

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

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 head0.028
GPT teacher head0.276
Teacher spread0.248 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2017
Admission routes1
Has abstractyes

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