Prediction of Injection Induced Formation Shear
Bibliographic record
Abstract
Abstract Formation integrity is a critical confinement factor in any injection process such as waste injection, CO2 capture and storage, and thermal/pressure injection in oilfields. Rock can fail in tensile, shear or in combination of complex modes. In situ stresses variation caused by injection may shear the caprock posing continued risk of containment breach. We developed an integrated approach to predict the alteration of in situ stresses and shear failure potential by combining data from sonic logs, image logs, mini-frac test data, formation pressure measurement, and rock mechanical core test data. In this approach, three-dimensional Mechanical Earth Models containing the reservoir, overburden, under-burden and side-burden were constructed. Coupled simulations were then run between dynamic reservoir model and geomechanical model to quantify stresses change induced by injection. The resulted formation shear and surface heave will then be calculated regarding the location and timing of occurrence during the planed injection operations. The methodology has been applied to several steam injection case studies in Northern Alberta oil sands area, Canada. Our analysis indicated that three years of steam injection would cause up to 2 MPa stresses contrast; formation shear failure was forecasted to occur earlier than formation tensile failure at a same time step; the calculated surface heave due to steam injection was around three 3 cm. The simulation results enabled the optimization of injection scheme and proactive monitoring plans to avoid catastrophic events.
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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 | 0.001 |
| 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) | 0.001 | 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".