Similarity of the Effect of Different Dissolved Gases on Heavy-Oil Viscosity
Bibliographic record
Abstract
Summary An analysis of viscosity data for mixtures of different gases dissolved in three different heavy oils and bitumens revealed that, at the same molar concentrations and at the same pressure, each of these gases reduced the oil-phase viscosity by almost the same amount. Because the gases that were examined included both hydrocarbons and nonhydrocarbons, it was concluded that this behavior could be generalized to include most of the gases encountered in, or injected into, heavy-oil and bitumen reservoirs. This principle was discovered in the new results of a study on two heavy oils. These oils were mixed with methane or carbon dioxide or propane to achieve vapor/liquid equilibrium at various pressures. When the measured oil-phase viscosities were adjusted to the same pressure without further change to their compositions, and were subsequently plotted against gas concentration in mole percentage, all the values fell on approximately the same curve. The same behavior was subsequently observed in gas/bitumen data that had been published previously by other authors. Although it can be reasoned that the adjusted viscosities must begin to diverge at high concentrations when different gases are used, the differences were not experimentally discernable even at dissolved-gas concentrations as high as 60 mol%. The effect was the same when more than one dissolved gas was present. The application of this uniformity principle is expected to make it easier to compare the costs of using different solvent gases to reduce the viscosity of heavy oils and bitumens during enhanced-oil-recovery (EOR) operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".