Analysis of microearthquake data at Cold Lake and its applications to reservoir monitoring
Bibliographic record
Abstract
Abstract More than four months of continuously recorded micro-earthquake data acquired at Cold Lake, Canada, was analyzed using advanced algorithms for microearthquake location and subsurface tomography. Robust determination of the spatial, temporal, and magnitude distribution of seismicity is the first step toward understanding the relationship between the stress perturbations caused by the cyclic steam stimulation (CSS) process and seismicity. Acquisition geometry was constrained because the receivers were located in a single vertical borehole. Despite this constraint, we were successful in improving event locations by use of the double-difference method, which highlights several tight event clusters. The deep cluster at a depth of 400m, just above the oil reservoir, shows very high seismicity during the CSS processes. A second cluster is observed at shallower depths in the successive steam cycle. This suggests that repeated steaming causes the deformation to spread to shallower depths. The number of events, however, decreases in the second steam cycle. Even though some of the largest events occur below the Clearwater reservoir, we observed few events in the reservoir itself, indicating that the reservoir may be an aseismic region. The size and distribution of seismicity during the first cycle agrees with a Mohr's circle analysis using simple geomechanical modeling.
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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 | 0.000 | 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".