Dielectric Measurement Method for Dynamic Monitoring of Water-Saturated Sand Pack with and without Evaporation
Bibliographic record
Abstract
Abstract Thermal-based recovery techniques will continue to be used in bitumen and heavy oil recovery. Among them, electromagnetic (EM) heating is promising, especially in situations in which there are technical and environmental issues with steam-based methods. To design the EM process, precise knowledge of the dielectric properties of the target formation is of great importance. To achieve the research goals, a new patented impedance spectroscopy setup is used. An alternating electric field is applied to the sample using an impedance analyzer. The impedance measurements of the clean sand sample, which is pre-saturated with deionized water or brine, are measured at an elevated temperature and pressure to mimic reservoir conditions. There are two successive cycles of heating up to 170 °C. The first heating cycle is followed by a cool-off period, while the second heating cycle culminates with evaporation. The impedance was dynamically measured during each heating cycle. In addition, the dielectric properties of fresh water-gas and brine saturated sands were demonstrated. Results show that the electrical conductivity and dielectric constant of the samples are significantly influenced when the NaCl solution is the saturating fluid. Downward trends in both the electrical conductivity and relative dielectric constant were observed as the steam quality improved, which indicates the creation of a desiccated zone.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".