Design of Caprock Integrity in Thermal Stimulation of Shallow Oil-Sands Reservoirs
Bibliographic record
Abstract
Summary Stakeholders in in-situ oil-sands development take caprock-integrity issues seriously. The industry is faced with the challenge of determining an optimal operating pressure in the reservoir where, in general, the pressure should stay significantly low to ensure the caprock integrity while being significantly high for enhanced oil production and economics. This paper presents a comprehensive work program on the subject for a shallow oil-sands play. Caprock integrity considers the induced stress and deformation in a caprock during the thermal stimulation of an oil-sands reservoir. A minifrac-test program is undertaken to define the original in-situ stress state. Laboratory tests are carried out to measure the deformation and strength properties. Simulations are run to calculate the induced stresses and evaluate them against the mechanical strength. This paper describes some important quality-control issues for these activities. For the minifrac tests, multiple cycles and use of flowback are promoted for enhanced efficiency and accuracy. Laboratory tests are recommended on whole cores in a drained condition at a slow strain rate. Numerical simulations should use site-specific and laboratory-measured material properties. On the basis of the limited sensitivity analyses, the thermal expansion coefficient of the reservoir and Young's modulus of the caprock are found to significantly affect the caprock deformation and/or induced stresses.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".