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Record W2039838043 · doi:10.2118/00-02-das

Enhanced Oil Recovery - What We Have Learned

2000· article· en· W2039838043 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsOil in placeEnhanced oil recoveryPetroleum engineeringOil productionOil reservesFossil fuelOil fieldPetroleum industryOil sandsGeologyEnvironmental scienceEngineeringPetroleumWaste managementEnvironmental engineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.232
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it