A Novel Experimental Setup to Analyze Model Thin Films Representing Cores for an Ultrasonic Radiation Study of Petroleum Reservoirs
Bibliographic record
Abstract
An apparatus was developed for a visual representation of conventional core flooding tests using a Model Thin Film (MTF) setup. The configuration was intended to provide direct visual representation of a flooding process. For our purposes, we investigated asphaltene deposition on a thin-film core sample, by evaluating the oil recovery before and after subjecting rock samples to a sonication process to remove asphaltene deposits, part of an ongoing project. The process involved saturating a specific volume of core sample with an asphaltic crude oil sample and recording flow pressures throughout the process. In order to have a full grade asphaltene deposition on the core sample, an alkane reagent, heptane, was used as a solvent to subsequently flood the rock system. After the formation of the skin and asphaltic sediments, we conducted an oil flood and monitored flow pressures, higher inlet pressures confirmed plugging and asphaltic deposition in the rock matrix. The model thin film setup proved to be a very good demonstrational and experimental apparatus, as it provided excellent visual information relating to the oil flood, and allowed routine experimental pressure, temperature and flow readings to be taken. The prospect of obtaining accurate experimental results from the model thin film is bright. This apparatus is designed to be used for the ultrasonic radiation study of petroleum reservoirs.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".