A comparison of passive seismic monitoring of fracture stimulation from water and CO2 injection
Bibliographic record
Abstract
Abstract Hydraulic fracturing is used to create pathways for fluid migration and to stimulate production. Usually, water is the injected fluid, although alternative fluids such as carbon dioxide (CO2) have been used recently. The amount of fracturing that CO2 can induce is also of interest for the security of carbon capture and storage. Hydraulic fracturing is usually monitored using passive seismic arrays, detecting microseismic events generated by the fracturing. It is of interest to compare the amount of seismicity that CO2 injection can generate in comparison with water. With this in mind, we have analyzed a passive seismic data set monitoring the injection of water and supercritical CO2 under very similar conditions, allowing us to make a direct comparison be-tween the fluids. We examined event locations and event magnitudes, and we used shear-wave splitting to image the fractures that are generated. For both fluids, the event locations map the formation of fractures moving away from the injection well with normals parallel to the minimum principal stress. The events during water injection are limited to the injection depth, while during CO2 injection, activity migrates above the injection depth. Event magnitudes are similar in both cases, and larger event magnitudes appear to correlate with higher injection pressures. Shear-wave splitting suggests that water injection generates more fractures, though the data quality is not good enough to make a robust conclusion about this. The comparability between water and CO2 injection means that lessons can be learned from the abundant experience of conventional water injection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".