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Record W4240764292 · doi:10.2118/2007-134

Potential For Alkali-Surfactant Flooding In Heavy Oil Reservoirs Through Oil-in-Water Emulsification

2007· article· en· W4240764292 on OpenAlexafffundabout
J. Bryan, Apostolos Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWater floodingPulmonary surfactantPetroleum engineeringAlkali metalFlooding (psychology)Environmental scienceGeologyChemical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Alkali-surfactant flooding is an established enhanced oil recovery technique in conventional oil reservoirs, whereby the injected chemical reduces the oil/water interfacial tension, leading to less trapping of oil ganglia. In the past, there have been some studies of alkali and alkali-surfactant flooding of heavy oil systems as well, and it has been observed that chemical injection can lead to improved oil recovery. The heavy oil recovery mechanism proposed in this work is the creation of oil-in-water emulsions, which may form under conditions of low interfacial tension and shear due to flow through rock pores. Oil may either be produced in the water (emulsification & entrainment) or the droplets may plug the rock pores, leading to improved sweep efficiency (emulsification & entrapment). Both of these mechanisms are investigated in laboratory systems of varying rock permeability, using a heavy oil with a viscosity of 11 000 mPas. When oil-inwater emulsions form, the oil recovery can be improved significantly, even without the addition of polymer for mobility control. The effect of permeability and varying injection rates are considered, to understand how different ranges of shear affect the efficiency of these emulsion systems. Introduction Several countries in the world, notably Canada and Venezuela, contain significant deposits of heavy oil and bitumen. As Canadian conventional oil reserves continue to decline, the industry interest is now shifting rapidly towards the recovery of this unconventional crude. While the immensity of this resource base is exciting, heavy oil reservoirs pose unique challenges when designing recovery strategies. The Canadian oil sands are unconsolidated, high porosity and high permeability reservoirs. Ease of flow is therefore not an issue, as it is in many conventional oil reservoirs. Rather, the single biggest impediment to the successful recovery of heavy oil and bitumen is the high oil viscosity. Heavy oil reservoirs are a special subset of our oil sands, whereby the oil viscosity at reservoir temperature varies from around 50 mPas up to around 50 000 mPas. At reservoir conditions the oil requires high pressure draw downs in order for it to flow even through the permeable sands, after which point the reservoir has been depleted of all of its natural energy. In order to recover additional heavy oil, energy has to be injected into the reservoir. Often, this takes the form of a fluid that displaces the oil, meaning that the oil must be made to flow to production wells. Most improved/enhanced oil recovery schemes focus on reduction of the oil viscosity through the application of heat or miscible solvents. However, many of the Canadian heavy oil reservoirs are relatively small and thin, and have been disturbed to an unknown extent during primary production. Therefore, the development of injection strategies that are inexpensive (i.e. non-thermal) and easy to control will be of considerable benefit to heavy oil producers. In this work, alkali-surfactant solution is investigated as a potential non-thermal injection fluid. It is demonstrated that through the injection of low concentrations of alkali and preformed surfactant solution, oil recovery can be increased significantly above that of waterflooding.

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 score0.363
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
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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

Citations14
Published2007
Admission routes3
Has abstractyes

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