Should You Trust Your Heavy Oil Viscosity Measurement?
Bibliographic record
Abstract
Abstract Heavy oil viscosity is one of the few criteria available to help predict if cold production will give economic rates, or if thermal processes will be required to reduce the oil viscosity to achieve the required rates. If cold production is selected, viscosity is again used to help determine whether vertical or horizontal wells should be used. Viscosity data are also used to adjust cold production exploitation strategies if the production rates are significantly lower than expected. Petrovera conducted an extensive viscosity data collection project in a newly developed Elk Point area reservoir with lower than expected, and more erratic than expected, cold production rates. Oil samples collected over short periods of time resulted in viscosity values for the same well varying by a factor of four or more, with similar variation between close-spaced wells. Collection of repeated samples and submission of those samples to several commercial labs resulted in similar viscosity measurement scatter. To further evaluate viscosity data scatter, multiple samples were collected from one well at the same time using the same procedures. These samples were then submitted to several labs in triplicate using, three different well names, to achieve an unbiased test. Reported viscosity scatter was again large. The objectives of this paper are to:display the results of the study to bring this issue to the forefront for discussion, andencourage commercial labs to develop an industry-wide standard method of heavy oil sample cleaning and viscosity measurement. Introduction Why Is It Important to Know Heavy Oil Viscosity Accurately? Heavy oil exploitation is an important segment of the oil and gas industry in Canada1 and a number of other countries2. Motivating factors for exploitation of Canadian heavy oil and bitumen are the large volumes of resource in place and the high historic demand for asphalt-based products that are more readily obtained from heavy oil and bitumen. Western Canadian heavy oil and bitumen reservoirs (Figure 1) have been exploited with varying degrees of success for more than 60 years3. Early workers in the field of heavy oil and bitumen exploitation quickly determined that primary production responses (also called cold production responses) varied greatly from field to field. They also discovered that addition of heat in the form of steam often greatly increased the production response. Viscosity became one of the most valued criteria in their efforts to predict production response from a new field using easily measured parameters. The popularity of the viscosity screening criterion has been manifest by the fact that virtually all technical papers on heavy oil production response or production process development include a discussion of oil viscosity. Unfortunately the accuracy of some production forecasts based on oil viscosity has been poor, and this study was conducted to address one possible cause. This discussion will not dwell on other rheologic properties of heavy oil and bitumen, as these properties are not routinely measured. Rheology is well documented in the literature4–6. Several factors other than viscosity can strongly influence heavy oil and bitumen production rates.
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 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 | 0.009 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.061 |
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".