Interpretation Challenges for In Situ Stress from Mini-Frac Tests in Soft Rocks/Hard Soils
Bibliographic record
Abstract
The importance of measuring in-situ stresses as an essential input factor to caprock integrity analysis is realized for both the technical management of SAGD and CCS projects (design of optimal operating pressure) and environmental reasons (loss of caprock containment). A common technique to carry out stress tests in impermeable and weak rocks, like clay shale, is to combine sleeve fracturing with micro-hydraulic fracturing test for which premature initiation of the fracture at the packer level can be avoided. Following a brief introduction of the micro-hydraulic fracture technique, field data obtained from a micro-hydraulic fracturing test program completed to assess the far field in-situ stress state for a proposed low pressure Steam-Assisted Gravity Drainage (LP-SAGD) pilot project located southeast of Fort McMurray, Alberta, Canada has been analyzed. Pressure analyses during and after micro-hydraulic fracturing are used to compute the minimum in situ stress at depth and illustrate the wide variability in the results. Issues surrounding stress alterations in the near wellbore region, low permeability and low injection volumes are shown to contribute to incorrect estimates of the far field minimum in situ stress.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".