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Record W2069827970 · doi:10.2118/2006-014

Enhanced Heavy Oil Recovery by Immiscible WAG Injection

2006· article· en· W2069827970 on OpenAlexafffundabout
Y.P. Zhang, S.G. Sayegh

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)
FundersPetroleum Technology Research Centre
KeywordsPetroleum engineeringEnhanced oil recoveryEnvironmental scienceComputer scienceGeology

Abstract

fetched live from OpenAlex

Abstract As heavy oil begins to overtake conventional oil in western Canada's energy supply, it becomes increasingly urgent to address the greater technical challenges posed by enhanced heavy oil recovery. This study investigates the technical feasibility of using CO2and enriched flue gas in an immiscible water-alternating-gas (WAG) injection process in those heavy oil reservoirs for which thermal recovery methods are likely to be uneconomic. In addition to phase behaviour and fluid property measurements of CO2, N2, and an enriched flue gas mixed with a heavy crude oil (12.4 ° API), this study focused on coreflooding tests of immiscible WAG injection at reservoir conditions.Additional tertiary recoveries of around 6% initial oil in place were obtained. The results indicate that N2 in the enriched flue gas (i.e., 70% N2 + 30% CO2) did not have a detrimental effect on oil recovery. Addition of a foaming agent with the injected CO2 was also beneficial. The phase behaviour measurements indicate that the viscosity reduction mechanism of a conventional immiscible injection process cannot alone account for the results obtained in the laboratory corefloods. Additional mechanisms are suggested for oil recovery and water blocking by free gas.The analysis discussed in this paper seeks to establish a better understanding of the possible mechanisms involved in the heavy oil immiscible gas flood process, and thereby improve oil recovery performance. Introduction Heavy oils are playing an increasingly important role in supplying Canada's energy needs, as global energy consumption escalates and conventional oil resources shrink. However, enhanced recovery of the vast heavy oil resource in west-central Saskatchewan faces greater technical challenges than do light oils. Heavy oil in this area is not only very viscous, but is also located in thin and shallow formations. The study discussed here investigated the technical feasibility of using CO2 and enriched flue gas in a water-alternating-gas injection process to enhance recovery from those heavy oil reservoirs for which thermal recovery methods are likely to be uneconomic. Heavy oil reservoirs in west-central Saskatchewan typically have low reservoir pressures; miscibility between the oil and injected solvent gases, such as CO2, cannot be achieved. Immiscible gas injection appears to be a practical enhanced oil recovery (EOR) method for these heavy oil reservoirs. In an mmiscible water-alternating-gas process, gas and water are alternately injected: the water following gas injection drives the reduced-viscosity oil, resulting in displacement with an improved mobility ratio. In addition to reducing viscosity, the dissolved gas also swells the oil so that, for a given fixed residual oil saturation, less oil remains after a waterflood. These two mechanisms have been demonstrated by numerous laboratory phase behaviour studies, coreflood tests and simulations.1–5 Analysis of results from a tertiary CO2 injection field test revealed that incremental oil production by immiscible CO2 injection has two components. The first is an instantaneous response, probably resulting from gas displacing oil that was not being displaced by water. The second component is the long-term effect caused by viscosity reduction, swelling, and relative permeability alteration.6

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.498
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)0.0010.000

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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 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

Citations20
Published2006
Admission routes3
Has abstractyes

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