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Surface energy and wetting behavior of reservoir rocks

2014· article· en· W2006087698 on OpenAlexfundno aff
Naveed Arsalan, Jan Buiting, Quoc P. Nguyen

Bibliographic record

VenueColloids and Surfaces A Physicochemical and Engineering Aspects · 2014
Typearticle
Languageen
FieldChemistry
TopicAdsorption, diffusion, and thermodynamic properties of materials
Canadian institutionsnot available
FundersCMG Reservoir Simulation Foundation
KeywordsWettingSurface tensionPetroleum reservoirInverse gas chromatographySurface energyGeologyPetroleum engineeringMineralogyChemistryMaterials scienceThermodynamicsComposite material

Abstract

fetched live from OpenAlex

An accurate description of the surface chemistry of the reservoir rock–fluid system is essential to understand the attractive forces between the various phases (crudes, brines and the rock surface). These physico-chemical interactions determine the fundamental nature of the reservoir wettability and the wetting behavior of fluids on the reservoir rock surface. Inverse gas chromatography (IGC) is used to characterize the surface chemistry of a Saudi Arabian reservoir rock (henceforth referred to as ‘reservoir rock’) at different moisture coverage and temperatures. This information combined with the surface tension of the interacting reservoir fluids is utilized to develop a new method for quantifying wettability in terms of a wettability index. This index is based on the relative magnitude of the work of adhesion between the rock surface and the competing oleic/aqueous phase.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), 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

Citations50
Published2014
Admission routes1
Has abstractyes

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