Dielectric Properties of Synthetic Oil Sands
Bibliographic record
Abstract
Abstract Dielectric logging tools have been used for some time in attempting to characterize oil reservoirs, and in particular to provide in-situ measurements of oil and water saturation. The particular sensitivity of basic electrical measurements, e.g. resistivity, to the presence of water made the original use of this approach most relevant to the determination of water-filled porosity. In-depth dielectric spectroscopic analyses reveal contributions from underlying processes in materials, including electronic, ionic (electric double layer, EDL) and interfacial (Maxwell-Wagner, M-W) polarization, as well as molecular orientation. Our particular objective is the evaluation of dielectric spectroscopy as a means of characterizing the physico-chemical and structural characteristics of oil sands. Relatively recently, there has been a resurgence in interest in the application of dielectric techniques to unconsolidated heterogeneous systems. The present study builds on recent developments in the literature, and, specifically to determine the extent to which the potentially dominant effects of water can be overcome in order to access additional information, such as particle size and wettability. This initial study has therefore involved investigating a range of synthetic oil sands systematically prepared from bitumen, water, sand and clay, to compare the behaviour with results obtained from a real core sample (obtained from a BP asset). The low-frequency dielectric spectroscopic analysis (10-2 to 107 Hz) yields complex properties (e.g. conductivity, permittivity, impedance) with frequency dependent in-phase and quadrature components. Within this frequency range, dielectric spectra are dominated by EDL and M-W polarization effects. By varying the sample preparation methods, it has been possible to observe structural differences with respect to water.
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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 | 0.000 |
| 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) | 0.001 | 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".