Oil Recovery Mechanisms in Bitumen-Bearing Carbonate Rocks
Bibliographic record
Abstract
Abstract Bitumen-bearing carbonate reservoirs, dominated by the 406 billion barrel resource in the Grosmont formation, will provide billions of barrels of recoverable oil for the Province of Alberta. A small contribution to this development was an extensive series of initial laboratory experiments that were conducted for multiple producers under a variety of conditions. The experiments include measuring oil recovery to varying water and steam processes, both separately and in combination with each other. Displacement procedures included both washing/soaking and direct floods with temperatures ranging up to 260°C. These laboratory tests were conducted over a six year period. As this research was conducted across several producers independently, a planned statistical design was not addressed through this process. Rather, the experiments are exploratory in nature, examining the primary recovery mechanisms contributing to production performance and ultimate recovery of bitumen in these heterogeneous systems. It has been shown that bitumen can be readily recovered from fractured and connected open porosity that is accessible to steam through a gravity drainage process. Furthermore, carbonates at elevated temperatures become more water wet, and water imbibition can play a significant role in accessing recovery from the oil-bearing rock matrix. The impact of imbibition is significant, and wet steam proves a much more efficient displacing medium than dry steam. The recovery of oil from lab-scale models also shows a significant impact of thermal expansion and gas drive, which can lead to significant oil displacements at early times, particularly in areas where the localized open porosity network is extensive. Bitumen recovery from carbonate systems is achieved through a combination of these processes: thermal expansion, gas drive, gravity drainage of oil out of connected open porosity, and imbibition of water into the rock matrix.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".