Identifying Fault Activation in Unconventional Reservoirs in Real Time Using Microseismic Monitoring
Bibliographic record
Abstract
Abstract Identification of fault related microseismicity in hydraulic fracture treatments is crucial to understanding how treatments are stimulating a reservoir. Evaluating b values in combination with event source mechanism provides a reliable and intuitive method for separating fault related microseismic events from standard fracture related events. Typically this analysis is conducted after a treatment is complete and serves as a diagnostic tool to provide possible explanations for reduced production or designing future treatments on nearby wells to avoid an identified fault feature. Being able to identify such features in real time allows the operator to not only identify faults but to stop treatment and avoid these features all together saving time and materials that would otherwise be pumped into an area that doesn’t contribute to the overall stimulation of the reservoir, and could reduce production on the well. When evaluating b-values in real time, a technique that can identify faulting early in the initiation of fault stimulation is crucial for preserving the most resources during treatment.
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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".