Bibliographic record
Abstract
Abstract SAGD (steam-assisted gravity drainage) is a robust thermal process that has revolutionized the economic recovery of heavy oil and bitumen from the immense oil sands deposits in western Canada, which have 1.6 to 2.5 trillion barrels of oil in place. With steam injection, reservoir pressures and temperatures are raised. These elevated pressure and temperatures alter the rock stresses sufficiently to cause shear failure within and beyond the growing steam chamber. The associated increases in porosity, permeability, and water transmissibility accelerate the process. Pressures ahead of the steam chamber are substantially increased, which promote future growth of the steam chamber. A methodology for determining the optimum injection pressure for geomechanical enhancement is presented, which allows operators to custom-tailor steam pressures to their reservoirs. In response, these geomechanical enhancements of porosity, permeability, and mobility alter the growth pattern of the steam chamber. The stresses in the rock will determine the directionality of the steam chamber growth, and these are largely a function of the reservoir depth and tectonic loading. By anticipating the SAGD growth pattern, operators can optimize on the orientation and spacing of their wells. Monitoring of the SAGD process is central to understanding where the process has been successful. Methods of monitoring the steam chamber are presented, including the use of satellite radar interferometry. Monitoring is particularly important to ensure caprock integrity, as it is paramount that SAGD operations be contained within the reservoir. There are several quarter-billion dollar SAGD projects in western Canada that are currently in the design stage. It is essential that these designs use a fuller understanding of the SAGD process in order to optimize on well placement and facilities design. Only by including the interaction of SAGD and geomechanics can we achieve a more complete understanding of the process.
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 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) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".