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Record W2041444223 · doi:10.2118/2002-164

Flow Visualization Studies of the Effect of Foamed Gel Microstructure on Gas-Blockage Effectiveness and its Importance on Foamed Gel Trapping in Porous Media

2002· article· en· W2041444223 on OpenAlexafffund
Laura Romero‐Zerón, Apostolos Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPorous mediumTrappingMicrostructureMaterials sciencePorosityVisualizationFlow (mathematics)Flow visualizationChemical engineeringMechanicsComposite materialComputer sciencePhysicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This study describes a pore level approach taken to investigate the complex flow of foamed gels in porous media. Foamed gels can be used as blocking agents to control excessive gas or water production in oil reservoirs. Although the use of foamed gels as plugging agents has been studied for some time, the mechanisms of foamed gel flow in porous media are not so clear. Through visual observation in transparent etched-glass micromodels, the effect of foamed gel microstructure or texture on blockage effectiveness and on trapping mechanisms in porous media are evaluated. The experimental observations demonstrate that foamed gels provide a higher flow restriction capability than conventional aqueous foams. Photographs and videotapes of flooding tests in a micromodel show very high oil recovery, as a result of foamed gel flooding. Visual observations of pore-level behavior indicate that foam bubbles are regenerated and reshaped within the porous media by snap-off, which seems to be the predominant mechanism. To achieve effective fluid flow restriction using foamed gels, it seems important to keep within the porous media a balance between the saturation of trapped discontinuous gas phase, and the gel configuration in the pore space. Introduction Foam is applied broadly as a mobility-control and profile modification agent for flow in porous media. Foam is a dispersion of a relatively large volume of gas in a small volume of a liquid. It is generated inside a porous medium when a liquid containing a foaming agent is mixed with either an externally injected or an in situgas (1), with the gas occupying typically 50%to 99% of the total volume. Foam mobility measured in porous media is many orders of magnitude smaller than that of the constituent gas. This mobility reduction is achieved primarily because the gas phase is dispersed into bubbles, which are generally about the size of the pore channels(2). Additionally, flowing lamellae encounter significant drag because of the presence of pore walls and constrictions(3). This performance makes foams suitable for three potential applications; 1) Mobility control, improving the displacement efficiency of gas drive processes; 2) Mobility control and flow impediment, improving the sweep efficiency of other fluid injection processes; 3) Partial or total pore blockage, restricting the flow of undesired fluids and plugging of high permeable oil "thief" zones(4). Performance of foam that is already capable of strong mobility control may be further improved by addition of suitable polymer or gelant(5). A foamed gel is created by means similar to those used for aqueous foam generation. The major difference between foamed gels and aqueous foams is that the external phase of the foamed gel crosslinks, greatly enhancing the mechanical stability of the foam system (6). The feasibility of using foamed gels for profile modification has been demonstrated by Miller and Fogler(6). Their findings verified that foamed gel is suitable for diversion of injected water into the lowpermeability zone of a parallel, non-communicating arrangement of cores. Later on, Wassmuth et. al,(7) evaluated an effective near wellbore blocking and diverting gel-foam treatment to control gas channeling.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.022
GPT teacher head0.267
Teacher spread0.245 · 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
Published2002
Admission routes2
Has abstractyes

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