Satellite Monitoring of Cyclic-Steam Stimulation Without Corner Reflectors
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 184971, “Satellite Monitoring of Cyclic-Steam Stimulation Without Corner Reflectors,” by Michael D. Henschel and Jonathan Dudley, MDA, and Peter Chung, Canadian Natural Resources Limited, prepared for the 2017 SPE Canada Heavy Oil Technical Conference, Calgary, 15–16 February. The paper has not been peer reviewed. Accurate monitoring of the ground deformation of a field under cyclic steam stimulation (CSS) can be used to calibrate predictive models, show the effectiveness of steam injection, and demonstrate the effect of the recovery operation on the surface elevation. Interferometric synthetic aperture radar (InSAR) uses radar returns from the ground to calculate highly precise estimates of the ground change. The authors discuss a new way of extracting deformation information from radar imagery, contributing to improved accuracy of InSAR surface-elevation monitoring. Introduction The challenge in the Canadian oil sands is to achieve robust measurements with limited infrastructure. Essentially, the problem is to develop robust measurements of the ground movement without reliance on buildings, pipelines, or even installed targets [such as corner reflectors (CRs)]. The case study described here shows exceptional results for monitoring of regions without traditional point targets. The operations and mechanism of thermal recovery of steam-assisted gravity drainage (SAGD) and CSS differ. SAGD is a continuous process of balanced injection and production, while CSS is a cyclic process of injection, soaking, and depletion. The methodology provided depends on an understanding of the expected ground movement but does not rely on fore-knowledge of the motion itself. The rate of change in ground deformation associated with a CSS operation is key to the extraction of the signal (deformation) over the time required for this monitoring. The relatively short time scales and the high magnitude of the changes experienced in CSS are well-suited to the nontraditional methods described here. The methods are validated by measurements of surface elevation by use of real-time kinematic (RTK) global-positioning-system (GPS) surveys acquired in the Primrose field coincident to the InSAR observations. The net-over-injection (NOI) metric provided by Canadian Natural Resources Limited (CNRL) is also used to demonstrate the causal relationship between injection and production volumes and the observed ground heave and subsidence, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".