Bibliographic record
Abstract
Abstract After thousands of field tests of EOR processes, we have learned that Enhanced Oil Recovery, or EOR, is difficult, expensive, and more likely to be a commercial failure than a success. At the same time, we have little choice but to strive to make EOR work, because the oil remaining in the reservoirs after primary recovery and waterflooding (secondary) can range from 50 - 60﹪ of the original oil in place (OOIP) in the case of a light oil (30 - 35 ° API), to 90﹪ in the case of a Saskatchewan/California type heavy oil (∼15 ° API). The volume of such "unrecoverable" oil in existing reservoirs is about 5 ? 109m3 (30 billion bbls) in Canada, and about 10 times as much in U.S.A. Worldwide, it could be something like two trillion bbls. Speaking of oil sands, where primary recovery is nil, EOR yields all of the production. Clearly EOR will play an ever increasing role in Canada and other countries with unconventional hydrocarbon resources. We shall briefly discuss status of EOR, the processes, and what lies ahead. Our purpose is to show that EOR has an important place in oil production, but one must be clear about the objective: is it to test a process or is it to make money? FIGURE 1: Production life of a typical oil reservoir (Available in full paper) EOR and IOR Figure 1 depicts the life of a typical oil reservoir. After discovery, oil production rate increases in the development stage, reaching a plateau. This can be because of regulation, demand, oil prices, or the field size. At some point the decline stage starts, and if something is not done, the field eventually reaches economic limit, and is abandoned. Good reservoir engineering practice calls for pressure maintenance, followed by (or simultaneously) waterflooding, this being the least expensive option. But even after that over one-half of the oil is left in the reservoir. (Table 1 shows typical recovery factors for a light and a heavy oil, as well as the oil saturations at different stages). If the reservoir is a candidate for EOR, the selected process should be developed early, as shown in Figure 1, because the "lead time" for developing a known recovery method from conception to commercial production can be 10 to 20 years. Examples abound: Cold Lake CSS (Cyclic Steam Stimulation) 20 years, Peace River In Situ Project 18 years, Gregoire Lake In Situ Combustion Project 20 years (unsuccessful), SAGD 15 years, etc. Most miscible carbon dioxide projects have taken 10 - 15 years to reach commercial stage. When the oilfield is offshore, the time window for improving oil production is very narrow because of limited platform life, and the choice of a recovery process is limited by platform space and logistics. The aim is to improve oil production during the primary stage, rather than waiting until after a gas/waterflood. This is true even for onshore fields, but may be less critical. IOR or Improved Oil Recovery refers to this type of approach. In this discussion we shall speak of EOR only.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".