Study on A Method for Measuring the Viscosity Of High Water-Content Heavy Oil-Water Mixture
Bibliographic record
Abstract
In Chinese heavy oil fields, the significant increase of water content in the produced fluids has made high water-content heavy oil-water mixture more common.In the measurement of viscosity of high water-content heavy oil-water mixture, the rotational viscometer method (RVM) cannot be applied, and the current stirring method has a problem that heavy oil is unable to be stirred uniformly at low speeds and deviates from actual operating conditions at high speeds.A stirring method with alternating high and low stirring speeds was designed in this study.The high-low speed stirring method (HLSSM) utilizes a short window period during high speeds when oil and water were coarsely dispersed to realize the viscosity measurement at low speeds.Comparison experiments indicate that the viscosity measured by this method reflects the shear-thinning behavior of non-Newtonian fluid, which is consistent with theoretical expectations.Furthermore, compared with the flow loop method (FLM), the average relative deviation for Oil A is 6.16%, with an average absolute deviation of 3.23 mPa•s, and for Oil B, the average relative deviation is 2.61%, with an average absolute deviation of 3.69 mPa•s, demonstrating that the testing accuracy meets the engineering requirement.This method not only shares the advantages of simple operation and continuous measurement with RVM but also overcome the issue of high cost associated with FLM.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| 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 | 0.000 |
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 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".