Comparative Evaluation of <i>a</i> Functions for the Soave-Redlich-Kwong Equation of State and the Peng-Robinson Equation of State to Predict Saturation Pressures for Gas(es)-Heavy Oil/Bitumen-Water Systems
Bibliographic record
Abstract
Summary To accurately predict saturation pressures for gas(es)-heavy oil/bitumen-water systems, several α functions have been selected and evaluated at a reduced temperature (Tr) of 0.70 and 0.60 for the Soave-Redlich-Kwong equation of state (EOS) and Peng-Robinson (PR) EOS, respectively. More specifically, 164 data points of measured saturation pressures of gas(es)-heavy oil/bitumen-water systems are collected from the public domain, while all α functions developed for heavy oil-associated mixtures and water have been reviewed and selected. At Tr = 0.70, the former, including three existing α functions, as well as two newly developed α functions at Tr = 0.70 together with three new α functions at Tr = 0.60, and the latter, including two alpha functions, are used to evaluate saturation pressures for various gas(es)-heavy oil/bitumen-water systems under various conditions. The absolute average relative deviation (AARD) between the measured saturation pressures and their predicted ones is found to decrease with either an increase in the pseudocomponent (PC) number or redefining the acentric factor (ω) at Tr = 0.60 other than the conventional one at Tr = 0.70. In addition to validating our coded program, the CMG WinProp module, together with its default binary interaction parameters (BIPs) is used to, respectively, quantify saturation pressures of the aforementioned systems with an overall AARD of 27.34 and 28.39% for the PR EOS and SRK EOS. The recommended α function newly developed at Tr = 0.60 by Chen and Yang (2017) predicts saturation pressures more accurately with an overall AARD of 3.88 and 1.64% by, respectively, treating the heavy oil as one PC and six PCs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 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".