Heavy Oil Viscosity Measurements: Best Practices and Guidelines
Bibliographic record
Abstract
Viscosity is an important parameter in reservoir development, especially in heavy oil production, processing, and transportation. Accurate measurement (±5%) of heavy oil viscosities can be affected by sample handling, storage, and cleaning procedures. In addition, the type of viscometers and the corresponding experimental procedures can impact the accuracy of viscosity measurements. The objectives of this paper are to present the results of a systematic evaluation and comparison of different viscometers typically used in heavy oil viscosity measurements, provide references on the subject of viscometer selection, recommend developed measurement procedures for each viscometer, and generate a reliable viscosity database of dead and live heavy oils. The systematic study was performed using three viscometers typically used in heavy oil systems: a capillary viscometer (CV), an electromagnetic viscometer (EMV), and a rheometer (Rh). Viscosity measurements were performed over a range of temperature and pressure conditions varying from 293 to 422 K (from 20 to 149 °C) and from atmospheric pressure to 31.0 MPa (4500 psia). Three dead heavy oil samples ranging from 20° to 11° American Petroleum Institute (API) gravity and three live heavy oil samples prepared with gas/oil ratios (GORs) of 44.5, 30.3, and 17.8 Sm 3 /Sm 3 by the three dead oils and methane gas were used. The study results showed that, within working limitations, each well-calibrated viscometer can reproduce reported values of viscosity standards with a relative error of less than 5%. The Rh with an open to atmospheric system provides the highest viscosity measurement scale but is limited to lower temperature tests to minimize light and/or intermediate component losses. The EMV and CV provide reasonably consistent viscosity values for both dead and live heavy oil samples as long as key conditions related to the experimental setup, measurement procedures, and sample preparation are met.
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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 | 0.039 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.014 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.015 |
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".