Constraining geomechanical models by using microseismic-derived fractures from source mechanisms
Bibliographic record
Abstract
Abstract Geomechanical modeling of hydraulic stimulations requires data to build and calibrate models to predict decline curves and other aspects of long-term reservoir performance. Initialization of these models requires knowledge of the preexisting fractures and geologic properties of the media. Orientations of different fracture sets, their intensities, and spacing, along with characterization of their size scales, critically impact geomechanical predictions of the stimulations in terms of proppant and fluid placement and the decline of reservoir productivity. With sufficient sampling of azimuths around the stimulation, the mechanisms and associated fracture planes and stress/strain conditions can be reconstructed through seismic-moment tensor inversion (SMTI) of recorded microseismic-event waveforms. At smaller scale lengths, signal-to-noise ratios can be low, and events might not be observed at high enough quality to permit SMTI. To extend the characterization of these fractures to smaller scales, a stochastic optimization algorithm is used, designed to search for optimally placed fractures in the reservoir that intersect with event locations while constraining their orientations from the same distribution observed at larger (SMTI-resolvable) length scales. Effectively, this technique allows for extension of the power law governing fracture distribution to smaller scales by invoking observed trends in self-similar behavior. In turn, characterization of the wider band of fractures in the reservoir provides necessary inputs into geomechanical models to predict fluid and proppant distributions from the full band of generated microseismicity and long-term behavior of the reservoir through decline-curve estimation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".