Microseismic Case Study: A deeper look into the microseismic events. What events are really related to stimulated reservoir.
Bibliographic record
Abstract
Summary Yangarra Resources Ltd. (Yangarra) was preparing to develop the Cardium sand on their acreage in the Willesden Green area of Alberta. To optimally drain the acreage, horizontal well density and the number of frac stages employed using multi stage fracture stimulation was questioned. To answer this, Yangarra contracted Reservoir Imagining Ltd. (Reservoir Imaging) to acquire downhole microseismic (MS) data in a vertical well they drilled near the initial proposed horizontal well. In addition to the MS data it was decided to acquire a VSP survey to refine the velocity model, tie an existing 3D reflection seismic survey and confirm the phase and integrity of the 3D seismic processing. One of the basic questions that a microseismic survey addresses is the fracture length achieved during a fracture stimulation operation. The ‘first look’ results of the MS survey showed microseismic events were reaching as far as 250 meters from the well bore and up to 250 meters above and 175 meters below the Cardium sand. The question was ‘to what extent were the observed microseismic events linked and stimulated and therefore actually contributing to production’. By analyzing the timing and amplitude of the microseismic events it became apparent that not all events should be used in estimating the stimulated rock volume. By making a series of assumptions and filtering the data, we were able to come up with a more reasonable estimate for stimulated rock volume and thus provide data that would yield an appropriate well density to efficiently drain the reservoir.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".