Advances in Electrical Heating Technology for Heavy Oil Production
Bibliographic record
Abstract
Abstract Electrical downhole heating has been used for many years for flow assurance and now is being adapted for reservoir stimulation, viscosity reduction and "in situ" conversion of heavy oil. This paper starts with a short review of flow assurance applications in Alaska and Canada as described in SPE-165323-MS. It then reviews the current and developing technology and some of the heat transfer parameters for use of high voltage high power electrical heaters in a number of types of applications. In the past heater voltages have been limited to below 600 volts for mineral insulated cable heaters. Significant material and processing advances have now permitted operation at 4160 volts. This has a number of operational advantages in providing longer length heater capabilities and less parasitic heating loss in the overburden. MI cable production technology is now available to fabricate MI cable heaters that can produce 1600 meters lengths without external splices. The thermal heat transfer from the well casing to the reservoir is usually the limiting factor on the amount of energy that can be transferred from the electrical heater to the formation. Both constant power and constant temperature heaters are explained with the emphasis on in operation reliability of each type of heater. The paper concludes with an economic analysis of the opportunity provided by a high voltage MI cable heater system in a horizontal well.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".