Examining the Capability of Statistical Models to Mitigate Induced Seismicity during Hydraulic Fracturing of Shale Gas Reservoirs
Bibliographic record
Abstract
Injection into the subsurface is carried out by industry for a variety of reasons, for example, storing wastewater, enhanced oil recovery, and hydraulic frac- ture stimulation. By increasing subsurface pressures, injection can trigger felt seismic- ity (i.e., sufficient magnitude to be felt at the surface) on pre-existing faults. As the number of cases of felt seismicity associated with hydraulic fracturing (HF) has increased, strategies for mitigating induced seismicity are required. However, most hydraulic stimulation activities do not induce felt seismicity. Therefore, a mitigation strategy is required that is capable of differentiating the normal case from abnormal cases that trigger larger events. In this article, we test the ability of statistical methods to estimate the largest event size during stimulation, applying these approaches to two datasets collected during hydraulic stimulation in the Horn River Shale, British Columbia, where HF was observed to reactivate faults. We apply these methods in a prospective manner, using the microseismicity recorded during the early phases of a stimulation stage to make forecasts about what will happen as the stage continues. We do so to put ourselves in the shoes of an operator or regulator, where decisions must be taken based on data as it is acquired, rather than a post hoc analysis once a stimulation stage has been completed. We find that the proposed methods can provide a reasonable forecast of the largest event to occur during each stage. This means that these methods can be used as the basis of a mitigation strategy for induced seismicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".