Viscosity Predictions for Crude Oils and Crude Oil Emulsions Using Low Field NMR
Bibliographic record
Abstract
Abstract Knowledge of oil viscosity is vital to the petroleum industry, and is especially important when considering production of heavy oil and bitumen. As viscosity increases, conventional measurements become progressively less accurate and more difficult to obtain. Oil viscosities measured in the lab may also be not indicative of true in-situ viscosities. An alternate method is required for predicting oil viscosity, especially if this method can be applied in-situ. Stable crude oil emulsions are prevalent in many stages of the production and transport of heavy oil and bitumen. Knowledge of emulsion viscosity is necessary for determining energy requirements for transport and upgrading of the produced crude. Low field nuclear magnetic resonance is examined in this work for its potential to predict viscosity of crude oil and crude oil emulsions. NMR is an attractive alternative to conventional viscosity measurements, because it can provide fast, unbiased and non-destructive data. A correlation is presented that predicts fluid viscosities from under 1 cP to over 3 000 000 cP over 25 – 80°C, making it valid over a wider range of viscosities and temperatures than any other published NMR viscosity correlation. With tuning, this model can predict very accurate changes in viscosity with temperature for a single oil. An NMR emulsion viscosity model is also presented that uses the oil viscosity and water fraction, both determined from NMR, to predict emulsion viscosity. This correlation is able to provide order of magnitude emulsion viscosity predictions for a wide range of emulsion water cuts and viscosities. Work has also been done to extend the viscosity predictions to in-situ viscosity measurements, which can then be extracted from logs. Preliminary findings on in-situ oil viscosity are encouraging, and indicate that NMR has great potential as a tool for in-situ viscosity determination.
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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) | 0.000 | 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".