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Record W2114121068 · doi:10.2118/2007-012

Effect of Oil/Brine Ratio on Interfacial Tension in Surfactant Flooding

2007· article· en· W2114121068 on OpenAlexafffund
Y.P. Zhang, S.G. Sayegh

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)
FundersPetroleum Technology Research Centre
KeywordsCitationPulmonary surfactantBrineEnhanced oil recoveryFlooding (psychology)Surface tensionComputer scienceLibrary scienceChemistryPetroleum engineeringEngineeringChemical engineeringPhysicsPsychology

Abstract

fetched live from OpenAlex

Abstract The difficulty of determining the effective interfacial tension (IFT) in porous media limits the modeling and prediction of surfactant flood performance. Surfactant dilution, adsorption and partitioning - occurring as the aqueous solution is injected into the reservoir and as it contacts the oil - will raise the effective in-situ IFT from the nominal value as measured traditionally. This change will have a corresponding influence on the oil displacement efficiency. A laboratory study of the interfacial tension behaviour of oil/surfactant-brine systems was conducted. The effective equilibrated oil-surfactant IFT - that is, the IFT closest to that actually produced by partitioning effects in the porous medium - was found to change greatly from the nominal values. When the ratio of oil to brine reached 40:60, the effective equilibrated IFT for the systems approached the original value of crude oil/brine without surfactant, apparently losing much of the advantage provided by a surfactant flood. However, the interfacial tensions between equilibrated oils and a fresh surfactant solution indicate that injecting additional chemicals would maintain the IFT at a reasonably low level. This was confirmed with visual micromodel floods: oil displacement efficiency was poor when equilibrated surfactant- brine solution was injected into a model containing equilibrated oil, and then greatly improved by injecting fresh surfactant solution. These findings are important for progress towards designing successful chemical floods. Introduction Enhanced oil recovery (EOR) by surfactant flooding has become more attractive in recent years. Low interfacial tension at low surfactant concentrations, and acceptable adsorption levels are considered to be important design parameters in optimising chemical systems for recovering trapped oil from petroleum reservoirs.[1,2] Ultra-low interfacial tensions of less than 10−3 mN/m have been reported with less than 0.1 wt% surfactant concentration measured by the traditional spinning drop method.[3] However, interfacial tension can be very difficult to accurately extrapolate from laboratory conditions to reservoir-like conditions. In a surfactant flood, the best surfactant performance depends on the characteristics of crude oil and brine, reservoir conditions, and several other stringent requirements, such as low retention, compatibility, and thermal and aqueous stability. Surfactant retention is due in part to adsorption on the rock surfaces, but other loss mechanisms Because there are limitations to studying the effect on interfacial tension of dilution, adsorption and partitioning of surfactant solution upon injection into the reservoir, it is not surprising that many studies use the IFT without considering adsorption and partitioning to predict surfactant flood performance. The traditional method of measuring ultra-low interfacial tensions (down to 10−3 mN/m) between two fluid phases is the spinning drop technique. In this test, a small drop of oil, of which the volume is less than 0.1 cm3, is injected inside a thin tube filled with a surfactant solution (approximate volume 1 cm3), and the tube is rotated at a high speed. The interfacial tension of the oil against water is able to be calculated from the angular speed of the tube and the diameter of the oil drop. The interfacial tension is obtained using a 0.1 oil-to-water ratio.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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 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

Citations8
Published2007
Admission routes2
Has abstractyes

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