Inferring in-situ stress changes by statistical analysis of microseismic event characteristics
Bibliographic record
Abstract
Two commonly estimated fractal dimensions, called b and D values, represent statistical characteristics in the distribution of magnitude sizes (b) and spatial hypocenter locations (D) of microseismic events, respectively. We establish that the values of these two dimensions are related to specific stress regimes. Through the case study of a heavy-oil field drained using cyclic steam stimulation, we infer that the measured temporal variation in fractal dimension b is most likely due to significant changes in the local stress regime over a seven-month period, ranging from extensional faulting (fractures opening), via a strike-slip regime, to finally compressive faulting (fractures closing). The fracture dimension D indicates predominantly planar-to-spherical hypocenter spatial distributions in the first and last stages, but changes to a more linear-to-planar spatial pattern in the intermediate strike-slip regime when the vertical stress is anticipated to be in between the maximum and minimum horizontal stresses. These changes could be due to localized pore-fluid overpressure. A statistical analysis of the microseismic event locations and their magnitudes is therefore a useful method to understand reservoir geomechanics and thereby facilitate its management.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".