Predicting the azimuth of natural fractures and in situ horizontal stress: A case study from the Sichuan Basin, China
Bibliographic record
Abstract
ABSTRACT The azimuth of fractures and in situ horizontal stress are important factors in planning horizontal wells and hydraulic fracturing for unconventional resource plays. The azimuth of natural fractures can be directly obtained by analyzing image logs. The azimuth of the maximum horizontal stress σH can be predicted by analyzing the induced fractures on image logs. The clustering of microseismic events also can be used to predict the azimuth of in situ maximum horizontal stress. However, the azimuth of natural fractures and the in situ maximum horizontal stress obtained from image logs and microseismic events are limited to the wellbore locations. Wide-azimuth seismic data provide an alternative way to predict the azimuth of natural fractures and maximum in situ horizontal stress if the seismic attributes are properly calibrated with interpretations from well logs and microseismic data. To predict the azimuth of natural fractures and in situ maximum horizontal stress, we have focused our analysis on correlating the seismic attributes computed from pre- and poststack seismic data with the interpreted azimuth obtained from image logs and microseismic data. The application indicates that the strike of the most-positive principal curvature k1 can be used as an indicator for the azimuth of natural fractures within our study area. The azimuthal anisotropy of the dominant frequency component of offset vector title seismic data can be used to predict the azimuth of maximum in situ horizontal stress within our study area that is located in the southern region of the Sichuan Basin, China. The predicted azimuths provide important information for the subsequent well planning and hydraulic fracturing.
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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".