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Record W2329595424 · doi:10.2118/179658-ms

Pore-Scale Observation of Solvent Based Foam During Heavy Oil Recovery

2016· article· en· W2329595424 on OpenAlexaff
Ali Telmadarreie, Japan Trivedi

Bibliographic record

VenueSPE Improved Oil Recovery Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnhanced oil recoveryMicromodelSolventAdsorptionPetroleum engineeringChemical engineeringPulmonary surfactantViscositySurface tensionMaterials scienceOil in placeAqueous solutionViscous fingeringAsphalteneHydrocarbonDiffusionPorous mediumPetroleumChemistryPorosityComposite materialGeologyOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Aqueous based foam injection has gained interest for conventional oil recovery in recent times. Foam can control the mobility ratio and improve the sweep efficiency in oil reservoirs over gas flooding. However, due to the high viscosity of oil, its application in heavy oil reservoirs is challenging. Moreover, oil-wet nature of carbonate reservoirs makes it difficult for aqueous based foam to efficiently remove the heavy oil. On the other hand, hydrocarbon solvents have been used for decreasing the heavy oil viscosity and increase its recovery by diffusion and mixing mechanisms. However, low rate of diffusion/dispersion and inadequate sweep efficiently, especially in heterogeneous reservoirs, are of the main challenges during solvent injection. Combination of foam and solvent (solvent based foam) can overcome the challenges existing in the separate application of aqueous based foam and solvent injection for heavy oil recovery. The challenge is to understand how the combination of solvent and foam will help us to improve the heavy oil sweep efficiency. This paper introduced a new approach to increase sweep efficiency during heavy oil recovery with the help of hydrocarbon solvent-based CO2 foam. Foam was generated with the help of a fluorosurfactant in the hydrocarbon solvent. Static bulk performances of foam were analyzed at different concentrations of surfactant. Surface tension measurement was also performed to study the adsorption of surfactant into the liquid-gas interface and its effect on foamability and foam stability. A specially designed fractured micromodel (oil wet, representing fractured carbonate reservoirs) were used to visualize the pore scale phenomena during solvent based foam injection. A high quality camera was utilized to capture high quality images/movies. According to static experiments, although the value of the surface tension of hydrocarbon solvent was initially low, the addition of surfactant slightly decreased the surface tension further and surfactant adsorption at the interface improved the foam stability. This process was more evident in higher concentration of surfactant. In addition, dynamic pore scale observation through this study revealed that solvent based foam can significantly contribute to heavy oil recovery with different mechanisms. At initial stage, solvent diffuses and mixes with viscous oil and reduce the viscosity. Later, foam bubbles improve the sweep efficiency by diverting the solvent toward untouched part of the porous media. In addition, foam bubbles partially blocked the opening area in matrix/swept-area increasing the contact of solvent and heavy oil, providing better mixing. Therefore, oil is swept much faster and more efficiently from the grain in oil-wet porous media compared to that of conventional solvent flooding. Successful application of solvent based foam can significantly improve the heavy oil recovery in reservoirs with high heterogeneity and oil-wet matrix. Cooperation of diffusion/dispersion and mobility reduction will result in faster oil production and lesser amount of oil will leave behind improving the sweep efficiency.

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 categoriesMeta-epidemiology (narrow)
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 score0.360
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.217
Teacher spread0.203 · 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 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

Citations2
Published2016
Admission routes1
Has abstractyes

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