Correlations of Characteristics of Saskatchewan Crude Oils/Asphaltenes with Their Asphaltenes Precipitation Behavior and Inhibition Mechanisms: Differences between CO<sub>2</sub>- and <i>n</i>-Heptane-Induced Asphaltene Precipitation
Bibliographic record
Abstract
The structural and molecular characteristics of the asphaltenes of four oils light oil, L-O; medium oils, M1-O and M2-O; and heavy oil, H-O from the Weyburn and adjacent areas in Saskatchewan, Canada were determined and correlated with the oils' asphaltene precipitation behavior and mechanism, as well as their chemical inhibitor effectiveness, for the purpose of determining differences between CO 2 and hydrocarbon flooding for enhanced oil recovery (EOR). A multitechnique approach involving Fourier transform infrared (FTIR) spectroscopy, proton nuclear magnetic resonance ( 1 H NMR) spectroscopy, 13 C NMR spectroscopy, gated spin−echo (GASPE) spectroscopy, inductively coupled plasma (ICP), elemental analysis, saturates−aromatics−resins−asphaltenes (SARA) analysis, molecular weight, and density studies was used for characterization of the crude oils and their n -heptane-derived asphaltenes. Results showed that the asphaltene precipitation behavior and mechanism each were strong functions of the oil and asphaltene characteristics. Interestingly, there were striking contrasts in these relationships, depending on whether CO 2 or n -heptane was used as the flooding (i.e., precipitating) agent. In addition, there were differences in the inhibition effectiveness and mechanism, depending on the type of flooding agent used. Furthermore, these differences also were dependent on the type of chemical inhibitor used.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".