Identifying Stress Transfer in CSS Reservoir Operations Through Integrated Microseismic Solutions
Bibliographic record
Abstract
Abstract Advanced seismic analysis approaches are used to examine possible correlations between various seismic characteristics and observed in-situ pressure readings as related to CSS heavy oil operations in Canada. In particular, we examined how seismic parameters such as cumulative strain, apparent volume, energy surplus, and cumulative occurrence rates can be used to identify the dynamic transfer of stress from the reservoir to generating depths, generating conditions for possible failure of the cap rock. These techniques are not only applicable to Canadian oil sands, but potentially to any environment where significant deformation results in seismic events giving the opportunity for a real-time analysis of failure conditions. Based on our analyses, changes in cumulative strain mimic observed pressure changes. Similarly, a resulting increase in energy surplus occurred at reservoir depth coincident with the occurrence of observed pressure spikes, followed by a subsequent increase in apparent volume and cumulative occurrence rates at shallower depth subsequent to the observed pressure spikes. Our observations suggest that advanced seismic analysis techniques can be used to potentially identify the transfer of strain and stress from the reservoir to shallower depths and provide the potential to provide real-time feedback mechanism on reservoir behaviour based on observed microseismicity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".