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Record W2015515352 · doi:10.2118/06-02-01

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

2006· article· en· W2015515352 on OpenAlexafffundabout
Y.P. Zhang, S.G. Sayegh, Mingzhe Dong

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of ReginaSaskatchewan Research Council (Canada)
FundersPetroleum Technology Research CentreUniversity of Alberta
KeywordsFlue gasPetroleum engineeringResidual oilEnhanced oil recoverySaturation (graph theory)Environmental scienceOil fieldFuel oilWaste managementChemistryEngineering

Abstract

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Abstract This paper focuses on phase behaviour measurements with reservoir oil-CO2 mixtures and on coreflooding tests in the huffn- puff mode to characterize the system, determine the influential mechanisms, and supply data for simulation of the field implementation. The results indicate that significant amounts of CO2 could dissolve in the oil, which caused oil swelling and viscosity reduction. During the 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. Corefloods were performed 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. Coreflooding results suggest that the huff-n-puff process may be more suitable to oil-wet than water-wet reservoirs. Introduction Various technologies have been applied in tertiary oil recovery processes, such as gas miscible/immiscible injection and chemical flooding. Among these enhanced oil recovery (EOR) methods, the huff-n-puff process has been reported to be economic at an oil price of less than US$20/STB and CO2 costs of US$40/ton(1). For example, it was shown in a flue-gas huff-n-puff project(2)that oil production rates stabilized, and the project proved to be cost effective with small investment requirements and low operating costs. In another case(3), the CO2 huff-n-puff process was not successful in increasing incremental oil recovery. However, there were reduced water-handling and electrical requirements during the injection, soak, and flow phases, which were beneficial to the project. There is increasing interest in CO2/flue-gas huff-n-puff injection into single wells because the process is relatively easy to apply and does not require a large initial capital outlay. The process typically begins with the injection of a slug of gas into a single well. This is followed by a shut-in or soak period to allow the gas to dissolve into the oil, swell its volume, and reduce its viscosity. The same well is then returned to production and the response is monitored. In reservoirs with poor inter-well communication, this single-well approach may be one of the best ways, and sometimes the only way, to accelerate response in underperforming wells. Since miscibility between the reservoir oil and injected gas is not a requirement of the huff-n-puff process, it is well suited for low pressure reservoirs and for gases with high minimum miscibility pressures such as flue gas. The mechanisms involved in the production of oil during gas huff-n-puff are diverse and complex. The following mechanisms have been mentioned in the literature(4–6):oil viscosity reduction;oil swelling;solution gas drive;relative permeability hysteresis due to reduced water saturation, drainage/imbibition, and wettability alternation;repressurization;gas diffusion and mass transfer; and,interfacial tension reduction in the zone near the wellbore. The purpose of this work is to investigate the potential for applying the CO2 huff-n-puff process in a medium-gravity oil reservoir in Saskatchewan.

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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 score0.024
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
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.002
GPT teacher head0.185
Teacher spread0.182 · 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

Citations39
Published2006
Admission routes3
Has abstractyes

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