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Record W4240905978 · doi:10.2118/2003-015

Determining the Most Profitable ASP Flood Strategy for Enhanced Oil Recovery

2003· article· en· W4240905978 on OpenAlexafffundabout
Y.P. Zhang, D. Mills, Mingzhe Dong

Bibliographic record

VenueCanadian International Petroleum Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of ReginaSaskatchewan Research Council (Canada)
FundersPetroleum Technology Research Centre
KeywordsFlood mythComputer sciencePetroleum engineeringBusinessEngineeringGeography

Abstract

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Abstract While chemical floods have proven technically successful, the high cost of chemicals makes it challenging to develop a cost-effective tertiary process. If high interfacial tension (IFT) exists between the oil and water phases, the resulting capillary forces will resist externally applied viscous forces. This could cause the injected water and chemicals to bypass the residual oil and go to waste. The experimental studies presented here include reservoir fluid characterization, IFT measurements, and coreflood tests, all critical elements in designing a costeffective ASP (alkaline/surfactant/polymer) injection strategy. Coreflood tests used either sandpacks or composite reservoir cores with a selected medium crude oil. Injected surfactant concentration, slug size, chasing fluid, and residual oil saturation were the varied parameters. The optimal surfactant concentration of 0.15 wt% and slug size of 0.5 pore volume (PV) obtained relatively high oil recovery while maintaining a favorably high displacement efficiency ratio. Incremental recovery was 23–41% initial oil in place (IOIP) in sandpack tests and about 16% IOIP with reservoir cores. Overall, these coreflood results indicate that ASP flooding is a suitable enhanced oil recovery method for medium oil if the right chemical concentration and slug size are selected. Introduction The use of ASP flooding to recover oil left behind by waterflooding has become more common in recent years. Several significant developments have made ASP flooding a viable option for field enhanced oil recovery (EOR) projects and more attractive than polymer or micellar/polymer flooding. First, because world oil consumption continues to grow while production in many mature fields continues to decline, oil prices are predicted to stabilize above US$20/bbl. Second, newly developed cheaper surfactants have reduced the cost of EOR formulations by 40 to 60%, while maintaining the same high oil recoveries obtained using more expensive surfactants. The incremental cost per barrel of oil produced has been reduced to a reasonable level, around US$1.44 to $5.83.1 Third, chemical flooding can be evaluated in the oilfield faster and more economically than in the past using the single-well EOR pilot test and field-scale numerical simulations. Fourth, waterfloods in most mature reservoirs have experienced early water breakthrough and have reached their economic limit; therefore, oil companies have to consider applying new EOR technologies to increase recovery factors in such fields. The proven medium oil resource in southwest Saskatchewan, estimated at 3.1 billion barrels, is located in reservoirs characterized by thin pay (two to eight meters in thickness) and shaly sand. The estimated oil recovery by primary and secondary methods from these reservoirs is about 25% IOIP. 2 There has been little development of medium oil reservoirs in southwest Saskatchewan by enhanced oil recovery technologies, and conventional methods are nearing their economic limit. The Saskatchewan Research Council (SRC) has conducted extensive studies to develop ASP flooding for a selected reservoir"Instow"with reserves of about 22.1?106 m3 (140 million barrels) of medium oil originally in place. Instow field is located in ranges 18 and 19 of townships 9 and 10 in southwest Saskatchewan.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.540

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 head0.029
GPT teacher head0.262
Teacher spread0.233 · 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 designSimulation or modeling
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

Citations5
Published2003
Admission routes3
Has abstractyes

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