Enhanced Oil Recovery in Post-CHOPS Cold Heavy Oil Production with Sand Heavy Oil Reservoirs of Alberta and Saskatchewan Part 1: Field Piloting of Mild Heating Technologies
Bibliographic record
Abstract
Abstract Lloydminster area that straddles Alberta and Saskatchewan border contains vast amounts of heavy oil deposits in thin unconsolidated formations. It is believed that the heavy oil resource volume is in the 50 to 70 billion bbl range which makes it a world class resource. This work briefly summarizes the reservoir properties of these formations and provides an overview of the primary CHOPS recovery mechanism which only recovers on average 8% of the original oil in place. Therefore, the target for Enhanced Oil Recovery (EOR) processes are substantial. For instance, if an additional 2% (25% of the primary) oil can be recovered, this means an additional 1 to 1.5 billion bbls of oil production which can sustain the oil industry for many years in this area providing jobs and contributing significantly to government royalties. A number of EOR processes are reviewed in this study from the conventional water flooding technologies to more state of the art processes such as Horizontal well Hot Oil Circulation. It is shown that part of the resource with viscosities less than 5,000 to 10,000 cp can be a target for water/polymer flooding. While steam injection in heavy oil reservoirs can be very successful, more than 95% of the resource in Lloydminster is less than 10 m thick and, thus, is not amenable to steam injection due to excessive heat losses to the surrounding formations. However, EOR processes involving mild heating or no heating can be feasible in these thinner formations. A number of mild heating technologies are discussed. Two of these technologies have been piloted in the field: Hot Water Vapour Process and Horizontal Well Hot Oil Circulation. Field results from these pilots are presented and discussed in this paper. It appears that these technologies can offer significant commercial potential in post-CHOPS reservoirs as well as in areas where CHOPS or horizontal primary production wells have not been successful.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".