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Record W3085595429 · doi:10.1021/acsomega.0c02058

Foam Dynamics in Limestone Carbonate Cores

2020· article· en· W3085595429 on OpenAlexfundno aff
M. G. Aarra, Abdul Majid Murad, Jonas Solbakken, Arne Skauge

Bibliographic record

VenueACS Omega · 2020
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersEnergi Simulation
KeywordsCarbonateEnhanced oil recoverySaturation (graph theory)Chemical engineeringFossil fuelAdsorptionPetroleum engineeringMaterials scienceGas oil ratioMineralogyChemistryGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

There is an increasing interest in foam applications in heterogeneous carbonate reservoirs to improve gas sweep and mitigate a high gas–oil ratio (GOR) in production wells. However, foam has been studied in sandstones more than in carbonates, and there are few experimental investigations considering matrix transport properties of foam in carbonates. Thus, this study takes a fundamental approach to improve our understanding of foam generation and transport process in the absence and presence of remaining oil in carbonates by co-injection of Alpha Olefin Sulfonate (AOS) solution and nitrogen (N2) in outcrop Indiana Limestone at high pressure and temperature after satisfying adsorption. In the oil-free core, development of the foam generation transient period and its transition into steady-state foam was rapid for all gas fractions, where the strongest foam was obtained at 90% gas fraction. Foam properties were successfully reproduced at different gas fractions. At remaining oil saturation, foam generation and propagation were significantly delayed and were observed at a high AOS surfactant concentration (5 wt %). Persistent foams were obtained both with and without remaining oil present, which withstood pressure gradients of N2 up to 0.5 bar/m for extended times. Therefore, if correctly designed, foam gas shut-off can be a low-cost low-risk technique to reduce problems with high GOR, gas-handling, and gas reinjections.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.505

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)0.0000.000

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 head0.211
Teacher spread0.202 · 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 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

Citations11
Published2020
Admission routes1
Has abstractyes

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