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Record W1994746193 · doi:10.2118/01-03-05

Chemical Methods for Heavy Oil Recovery

2001· article· en· W1994746193 on OpenAlexaffabout
Sara Thomas, S.M. Farouq Ali, J.R. Scoular, B. Verkoczy

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)Peraso Technologies (Canada)
Fundersnot available
KeywordsPetroleum engineeringFlooding (psychology)Steam injectionEnvironmental scienceEnhanced oil recoveryOil fieldWaste managementGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract Many mobile heavy oil reservoirs in Saskatchewan and Alberta are unsuitable forthe application of thermal recovery methods, such as steam injection, for anumber of reasons including formation thicknesses of less than 10 m. Oilrecovery from such reservoirs can be accomplished by the use of nonthermalmethods, among which chemical flooding has considerable importance. This paperdiscusses recent laboratory results using chemical flooding techniques. At thesame time, limitations of such methods, limited field experience in heavy oilformations, and possible improvements are also considered. Among the chemicalflooding methods, alkaline and surfactant flooding techniques are moreimportant, partly because the chemicals involved are less expensive, and alsomuch has been learned from past experience in laboratory and field. Thelaboratory studies discussed consisted of surfactant floods and huff n'puff oftwo Lloydminster heavy oils. The recoveries in the floods were as high as 33﹪.The other recovery method discussed involved cyclic stimulation using twosurfactants. Oil recoveries as high as 12﹪ were achieved. Though recovery wasow, such an approach can be cost-effective in special circumstances. Introduction Much of the heavy oil in Saskatchewan and Alberta is mobile under reservoirconditions to the extent that primary production and waterflooding iseconomically feasible, although the recovery factors are low, 5 to 10﹪ in mostcases. Furthermore, the formation thickness is small (85﹪ of the oil in Saskatchewan occurs in formations less than 5 m thick), so that larger spacingsare needed, which makes the application of thermal methods, notablysteamflooding, doubly unattractive. Non-thermal recovery methods, such aschemical recovery processes and immiscible carbon dioxide WAG(Water-Alternating-Gas) process can be economically viable in such reservoirs, even though the recovery factor is low. This paper discusses primarily the morepromising non-thermal chemical flooding methods, selected laboratory and fieldresults, and their limitations. Results of a few experiments involvingchemicals with hot water are also added. Principles of Oil Recovery The two important concepts involved in oil recovery are Mobility Ratio, M, andthe Capillary Number, Nc. Mobility ratio, M, is usually defined as the mobility?ing (= k/ µ, where k is effective permeability and µ is viscosity)of the displacing fluid divided by the mobility ?ed of the displacedfluid (assumed to be oil in this discussion). If M >1, the displacing fluidwill flow past much of the displaced fluid, displacing it inefficiently. Thusthe mobility ratio influences "displacement efficiency," i.e., the(microscopic) efficiency of oil displacement within the pores. For M>>1, the displacing fluid will channel past oil ganglia. This is oftencalled "viscous fingering" For maximum displacement efficiency, M should be=?1, usually denoted as a "favourable"mobility ratio. If M >1(unfavourable), then, in the absence of viscous fingering, it merely means thatmore fluid will have to be injected to attain a given residual oil saturationin the pores.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.007

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 head0.010
GPT teacher head0.268
Teacher spread0.258 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations55
Published2001
Admission routes2
Has abstractyes

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