Underground ultrasound probing for monitoring carbon dioxide flooding in oil producing reservoirs
Bibliographic record
Abstract
The problem of identifying the position and location of immiscible or miscible layers is a critical issue in methods of carbon dioxide injection for oil producing bodies. Accurate knowledge of the position could lead to an ability to control the position of the carbon dioxide front affecting production, efficiency, economy, and, in the end, profitability. The approach of studying the progress of carbon dioxide in reservoirs using ultrasound is new and may lead to unique and powerful ways of monitoring the position of carbon dioxide, as well as visualizing the reality of the oil producing area. Firstly in this paper, some basic experiments to investigate the acoustic properties in many situations are described. These experiments include the ultrasound reflection properties of stationary water-air, water-oil, air-oil, oil-oil interfaces, as well as those of moving interfaces. Secondly, experiments of ultrasound reflection properties in porous mediums combined with air, water and oil are described. These experiments include reflection of air-water interfaces in porous mediums, oil-water interfaces in porous mediums, and air-oil interfaces in porous mediums. Thirdly, measures of digital signal processing are introduced to obtain position information of interfaces in porous mediums and improve the resolution and accuracy of the location. Ultrasound has many advantages as a tool in underground probing, such as small size, low power consumption and safety.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".