A Tangent-Line Approach for Effective Density Used in Ideal Mixing Rule: Part I—Prediction of Density for Heavy-Oil/Bitumen Associated Systems
Bibliographic record
Abstract
Summary Accurate prediction of density of an oil/gas mixture by using the ideal mixing (IM) rule is a great challenge, and its progress is still far from satisfactory. The method proposed by Standing and Katz (1942) for determining methane and ethane apparent densities is limited to only black oils and volatile oils. The methods recently proposed by Saryazdi (2012) and Saryazdi et al. (2013) to determine effective densities of methane through n-heptane (C1 through n-C7) and CO2 have shown some success, respectively, though limitations remain and the extent of their applications is still constrained. In this study, we developed a tangent-line approach for the effective density of C1 through n-C8, CO2, N2, toluene, cyclohexane, and dimethyl ether (DME). This method is more general and flexible than the extrapolation method proposed by Saryazdi (2012). A comprehensive database is established to first develop new correlations with one set of data and then compare them with the other. We successfully extended using the IM rule with effective density (IM-E) to condensate/bitumen systems, solvent/bitumen fraction systems, and solvent/bitumen systems with substantial extraction [i.e., emergence of a solvent-rich liquid phase (denoted as the L1 phase)] by properly treating the densities of condensate, bitumen fractions, extracts, and residues. This study focuses on heavy-oil/bitumen-associated systems, and the observed patterns and trends for different systems will be presented and explained in Part II of this study (Chen and Yang 2020).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".