Injection Pressures for Geomechanical Enhancement of Recovery Processes in the Athabasca Oil Sands
Bibliographic record
Abstract
Abstract This paper describes a geomechanical approach to the determination of injection pressures that will result in formation shearing within the Athabasca oil sands. The enhancement of in situ permeabilities, resulting from shearing, is a beneficial phenomenon and often an essential prerequisite of many successful in situ recovery processes. This geomechanical analysis is based upon the general geological setting in the Athabasca oil sands, observations made from field data, published laboratory data, and geomechanical principles. The first objective was to estimate the existing stress state in the rock. This determination was based on elastic theory and tectonics, and fitted to field measurements of the minimum in situ stress from minifrac tests in the Athabasca oil sands. The methodology also calculates the maximum horizontal stress, a parameter that is extremely difficult to measure, yet is an important parameter for most stress analyses. The resultant stress anisotropy creates the potential for formation shearing; a potential that is unleashed once the effective stresses are sufficiently reduced with high injection pressures. Lastly, the amounts by which injection pressures must be increased for shear failure are provided, assuming typical values of formation strength. The analysis begins with a determination of the minimum in situ stress, as a function of depth. This is the fracture closure pressure that is a useful parameter in determining fracture treatment pressures, fracture containment, and fracture orientation. The associated tectonic strain is used to calculate the maximum horizontal stress. Using the maximum and minimum stresses, the injection pressure at which extensive shearing will occur is calculated. This is the geomechanical optimum operating pressure. While other considerations may favour a lower operating pressure, recovery processes that rely upon high in situ permeabilities may not be viable at lower injection pressures. The methodology presented is applicable to other regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".