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Record W2083558109 · doi:10.2118/04-02-01

Structure/Performance Relationships for Surfactant and Polymer Stabilized Foams in Porous Media

2004· article· en· W2083558109 on OpenAlexafffund
Susan M. Kutay, Laurier L. Schramm

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsSaskatchewan Research Council (Canada)University of Calgary
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaChemical Institute of Canada
KeywordsMaterials sciencePorous mediumViscosityPorosityEnhanced oil recoveryPulmonary surfactantPolymerLaminar flowComposite materialRheologyPermeability (electromagnetism)Chemical engineeringChemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract The addition of polymer has the potential to enhance both the viscosity and the stability of surfactant-stabilized foams. However, the degree to which the bulk properties of polymer-thickened foams are retained or enhanced in porous rock is not well understood and is difficult to predict. We have compared the viscosities, at equivalent shear rates, of two different types of polymer-thickened foams in laminar pipeline (bulk) flow vs. the same foams flowing in consolidated sandstone rock. For one kind of foam, the apparent viscosity in the rock is very similar to that in pipeline flow. However, for another kind of foam, the apparent viscosity in the rock is an order of magnitude greater than that in pipeline flow. Low-energy scanning electron microscopy was used to examine the pore-scale morphology of the two foams in the rock. It was found that the morphologies of the two foams explain at least a large part of the observed differences in foam flow properties between bulk (pipeline) flow and constrained (porous medium) flow. This work is important to the specifications and formulation of the most effective surfactants for varying applications including mobility control, blocking, and diverting. Introduction Aqueous foams are used in a variety of the petroleum industry's enhanced oil recovery flooding techniques(1). For example, surfactant stabilized foams have been used as mobility control agents in gas-flooding(2–4). The foam, which has an apparent viscosity greater than the gas, lowers the gas mobility in the swept and/or higher permeability regions of the formation. Thus, the foam will divert some of the gas into other parts of the reservoir formation that were previously unswept, or poorly swept, to recover additional oil. Significant foam stability is a prerequisite for the successful application of foam flooding. There are also many other applications of foams in the petroleum industry, all requiring controlled stability(5–8). Foams have also been used as blocking agents because of their selective ability to reduce the gas permeability(9). Foam that has been developed for a blocking application must meet different requirements than foam that has been developed for sweep efficiency applications. A blocking foam must possess the ability to completely fill a selected volume in all locations where the gas could travel through, and to act as a barrier to flow. The gas blocking foam must stay in place and possess long-term stability, providing the largest possible gas mobility reduction for the longest periods. For the formulation of either kind of foam, one of the challenges that must be met is the proper selection of foam-forming surfactants. The foaming capability of a surfactant relates to both foam formation and foam persistence, which are influenced by many bulk and interfacial properties(10). Unfortunately, it is generally found that the performance of foams in porous media is not easily predicted based on these physical properties(11), although they can be exploited to increase foamability and foam persistence. Harsh chemical environments are sometimes present in oil reservoirs and several reviews have been published identifying desirable foam-forming characteristics for harsh and less demanding environments(1, 12).

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 score0.381
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.191
Teacher spread0.183 · 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

Citations43
Published2004
Admission routes2
Has abstractyes

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