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Record W3103012074 · doi:10.2118/02-09-04

Flue Gas Injection for Heavy Oil Recovery

2002· article· en· W3103012074 on OpenAlexaffabout
Mingzhe Dong

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)
Fundersnot available
KeywordsFlue gasPetroleum engineeringSteam injectionEnhanced oil recoveryEnvironmental scienceOil in placeOil viscosityWaste managementFossil fuelFuel oilViscosityPetroleumGeologyMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Abstract A majority of heavy oil reservoirs in Saskatchewan are thin and shaly, and are not suitable for thermal recovery methods. For these reservoirs, enhanced oil recovery by an immiscible gas process could potentially recover an additional 200 million m3 of oil. This paper presents the results of a laboratory investigation, including pressure-volume-temperature studies and twodimensional physical model experiments, for evaluating the flue gas injection process for heavy oil recovery. The study examined the effects on viscosity reduction and oil swelling of the presence of O2 and of CO2 content in the flue gas. Physical model tests were carried out to investigate the effects on oil recovery of injection rate, injection mode (vertically downward, vertically upward, and horizontal injection), and slug size. The free-gas mechanism in the flue gas injection process was also studied. Introduction In Saskatchewan and Alberta, there are many thin-pay, heavy and medium oil reservoirs that are unsuitable for thermal recovery techniques. The estimated recovery by primary production and secondary methods is only about 5 - 8% of the initial oil-in-place (IOIP) for the heavy oil reservoirs and about 25% IOIP for the medium oil reservoirs(1). For these reservoirs, enhanced oil recovery (EOR) by an immiscible gas process offers a strong potential to recover more oil. It could, according to previous studies(2–6), recover up to an additional 30% IOIP incremental over that recovered by initial waterflood for some moderately viscous oils. Flue gas injection for heavy oil recovery received a great deal of attention in the 1960s(7, 8). However, it has not been studied in detail. A previous comparative study on immiscible gas injection agents for heavy oil recovery showed that CO2 is the best recovery agent among the three gases tested, and produced gas is slightly more effective than flue gas(9). CO2 has a higher solubility in oil and higher viscosity reduction efficiency than the other two gases. From the theory of fractional flow for viscous fingering(10), it is expected that CO2 will give field applications a better sweep efficiency than the other two gases. However, natural CO2 sources are not available to most oil reservoirs. The cost for CO2 capture from flue gas and other sources may range from $25 to $70/tonne(11). Produced and flue gases are available in large quantities at a much lower cost. With this consideration, produced gas and flue gas can be economically effective agents for heavy oil recovery by immiscible gas injection(9). In a previous study(9), linear coreflood tests were conducted with live and dead Senlac oil/flue gas to compare the relative effectiveness of secondary vs. tertiary flooding and WAG vs. slug injection. In these tests, a total of 0.40 PV flue gas was injected either as a continuous slug in slug floods or in a WAG mode. In secondary tests, gas was injected into the oil-saturated sandpack, whereas in the tertiary injection mode, it was injected into the initially waterflooded core. The WAG tests employed a WAG ratio of 4:1, an even slug size, and a 4-cycle operation.

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 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 score0.793
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations52
Published2002
Admission routes2
Has abstractyes

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