MétaCan
Menu
Back to cohort
Record W2143666640 · doi:10.2118/2000-064

Structure/Performance Relationships for Surfactant Stabilized Foams in Porous Media

2000· article· en· W2143666640 on OpenAlexafffund
Laurier L. Schramm, Susan M. Kutay

Bibliographic record

VenueCanadian International Petroleum Conference · 2000
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPorous mediumPulmonary surfactantPorosityChemical engineeringMaterials scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract While much is known about foam performance in porous media some key knowledge-gap areas remain, one of which is the relationship between surfactant and foam performance in porous media. We compare the viscosities of two different types of polymer-thickened foams in laminar pipeline (bulk) flow versus the same foams flowing in consolidated sandstone rock. For one kind of surfactant-stabilized foam, the apparent viscosity in the rock is an order of magnitude greater than the viscosity of the "same" foam in pipeline flow. However, for another kind of surfactant, the foam apparent viscosity in the rock is very similar to the viscosity of the "same" foam in pipeline flow. Using advanced imaging techniques, various core samples from each kind of experiment were examined. It was found that the morphology of the second kind of foam was consistent with an effective foam viscosity that was comparable to that experienced in laminar-flow, pipeline loop experiments. On the other hand, the morphology of the first kind of foam contained features consistent with the order of magnitude higher viscosity found for its flow in rock versus flow in the pipeline loop. Our measurements show that at least part of the explanation for the observed differences among different surfactant systems with respect to bulk flow versus constrained flow is foam morphology. This work is important to the specification and formulation of the most effective surfactants for varying applications including mobility control, blocking and diverting. Introduction Stable aqueous foams are required in a variety of industrial processes, particularly in the petroleum industry's improved oil recovery (IOR) applications. A major challenge in formulating an effective foaming agent is the proper selection of surfactants. Harsh chemical environments are sometimes present in oil reservoirs and several hundred papers have been published in the past thirty five years identifying desirable foam-forming characteristics. These are reviewed elsewhere for harsh1,2 and less demanding environments3. The foaming capability of a surfactant relates to both foam formation and foam persistence, which are influenced by many bulk and interfacial physical properties4. Unfortunately, it is generally found that the performance of foams in porous media is not easily predicted on the basis of these physical properties5, although they can be exploited to increase foamability and foam persistence. For example, water-soluble polymers can stabilize foams by increasing either the surface or bulk viscosity of the film, thereby increasing the film elasticity or decreasing the film drainage rate. They are often effective at lower concentrations than other organic additives, and more compatible with different types of foaming systems. Polymer-thickened foams have been increasingly utilized in IOR with some commercial success6. In IOR, the unique physical structure and surface properties of foam produce a high flow impedence that improves the efficiency of crude oil production. The physical properties of the foam films likely play an important, as yet poorly defined, role in the passage of foam through porous rock. Many laboratory investigations into the generation of foam in porous media have been carried out using a variety of media.

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 categoriesInsufficient payload (model declined to judge)
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 score0.285
Threshold uncertainty score0.999

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)0.0020.000

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 head0.217
Teacher spread0.199 · 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 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

Citations6
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueCanadian International Petroleum ConferenceSame topicEnhanced Oil Recovery TechniquesFrench-language works237,207