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Record W2424148124 · doi:10.2118/181753-pa

Pore-Scale Investigations on the Dynamics of Gravity-Driven Steam-Displacement Process for Heavy-Oil Recovery and Development of Residual Oil Saturation: A 2D Visual Analysis

2016· article· en· W2424148124 on OpenAlexafffund
Francisco J. Argüelles‐Vivas, Tayfun Babadagli

Bibliographic record

VenueSPE Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsResidual oilMaterials scienceWettingMicroscale chemistrySaturation (graph theory)Petroleum engineeringMechanicsMineralogyComposite materialGeology

Abstract

fetched live from OpenAlex

Summary The dynamics of a gravity-driven injection process for heavy-oil recovery at the pore scale and the mechanisms leading to the formation of residual oil saturation (ROS) were investigated. The 10 × 15-cm and 5 × 5-cm 2D visual sandpack models (a single layer of sintered microscale glass beads) were prepared and placed into a transparent vacuum chamber to prevent heat loss. The processes were recorded with a high-speed camera to obtain visual data at the pore scale. This process represents the lateral spreading of the steam chamber (half symmetric chamber growth) during steam-assisted gravity drainage (SAGD) for heavy-oil recovery. Oil-trapping mechanisms yielding to the formation of ROS were described and analyzed because of (1) lateral expansion, (2) simultaneous vertical and lateral expansion, (3) pore and particle size, (4) heterogeneities (pore- and particle-size distribution), and (5) wettability. Attention was also given to the ceiling region of the steam chamber and its interaction with the mobilized region at the lateral boundaries of the chamber.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.333

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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations22
Published2016
Admission routes2
Has abstractyes

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