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Record W4242417411 · doi:10.2118/2004-021

Laboratory Investigation of Enhanced Light-Oil Recovery by CO/Flue Gas Huff-n-Puff Process

2004· article· en· W4242417411 on OpenAlexafffundabout
Y.P. Zhang, S.G. Sayegh, S. Huang, M. Dong

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of ReginaSaskatchewan Research Council (Canada)
FundersPetroleum Technology Research Centre
KeywordsFlue gasProcess (computing)Process engineeringEnvironmental sciencePetroleum engineeringLight crude oilWaste managementMaterials scienceChemistryComputer scienceEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract This paper reports on the feasibility of using carbon dioxide and flue gas in huff-n-puff cyclic gas injection to enhance light oil recovery from a waterflooded reservoir. Phase behaviour measurements were carried out on recombined reservoir oil/CO2 mixtures in a cyclic pressure mode to simulate field application of the process. Significant amounts of CO2 dissolved in the oil causing oil swelling and viscosity reduction. During pressure depletion (puff cycle) the oil retained CO2 preferentially to methane; thus, the beneficial swelling and viscosity effects were maintained over an extended portion of this cycle. The Peng-Robinson equation of state was successfully tuned to match the laboratory phase behaviour data in both pressure-increasing and -decreasing modes. Corefloods were performed in the huff-n-puff mode to investigate the effect of waterflood residual oil saturation and injection gas composition (CO2 and enriched flue gas) on oil recovery. Incremental oil recovery was observed to be sensitive to waterflood residual oil saturation and to the process application scheme. Gas utilization factors of these tests varied from as high as 17.2 Mscf/STB to as low as 2.8 Mscf/STB. These results suggest that the process is feasible, but that field application factors have to be optimized for maximum economic benefit. Introduction Elswick East Midale field, located in southeast Saskatchewan, was discovered in 1965. As shown in Figure 1, oil production from the field did not occur until 1987. Waterflooding began in late 1991; oil production increased significantly as a result, but decreased sharply after 1994. Additional development of horizontal wells within the field in later 1998 raised the monthly oil production again. However, until September 2003, Elswick East Midale field had produced only about 19.1% of the initial oil in place (IOIP).(1) The water cut has currently exceeded 85%, therefore enhanced oil recovery (EOR) methods are needed for increasing the recovery factor. Various technologies have been applied in tertiary oil recovery processes, such as gas miscible/immiscible injection and chemical flooding. Among these EOR methods, the huff-npuff process has been reported to be economic at an oil price of less than US$20/STB and CO2 costs of US$40/ton.(2) For example, it was shown in a flue-gas huff-n-puff project(3) that oil production rates stabilized, and the project proved to be costeffective with small investment requirements and low operating costs. In another case,(4) the CO2 huff-n-puff process was not successful in increasing incremental oil recovery. However, there during the injection, soak, and flow phases, which were beneficial to the project. The principal obstacle to developing EOR technologies is economics, which is affected by the special features of petroleum reservoirs. It is essential to develop cost-effective EOR technologies and to accelerate technology transfer to the field so that more oil resources can be recovered. There is increasing interest in CO2/flue-gas huff-n-puff injection into single wells because the process is relatively easy to apply and comparatively inexpensive. Although the huff-n-puff process is showing substantial oil recoveries in some oil reservoirs, experimental work has to be conducted to characterize the interaction of the injected gas with the crude oil under the candidate reservoir conditions before a field pilot starts.

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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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.216
Teacher spread0.210 · 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.

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

Citations14
Published2004
Admission routes3
Has abstractyes

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