Real-Time Imaging of SAGD Steam Conformance By Using White Noise Reflection Processes
Bibliographic record
Abstract
Abstract 4D seismic imaging requires extensive time to setup, implement, and process to provide information on the progress of recovery efforts such as estimates of the size and shape of reservoirs and their internal artifacts. Conventional seismic imaging results in a resolution on the order of tens of meters. As an alternative, white noise reflection processes use sub-noise signals to image reservoirs and can potentially do this at scales below 1 meter. Both simulations and lab experiments show that reflections from white noise processes can be used advantageously to localize discontinuities and track their movement through media. For example, for steam-based oil sands recovery processes, it is critical to have an understanding of the steam conformance to improve the efficiency of the recovery process. White noise signaling technologies can be used to monitor the spatial distribution of fluids e.g. a steam chamber interface, and objects e.g. shale layers and concretions, at higher frequencies resulting in finer resolution in real-time compared to conventional methods. The results demonstrate that acquisition and ranging of discontinuities in the laboratory can be achieved at the centimeter scale. The methods are extended to concurrent use of multiple transducers to improve directionality and triangulation of discontinuities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".