Field-Scale Analysis of Heavy-Oil Recovery by Electrical Heating
Bibliographic record
Abstract
Summary Electrical heating for heavy-oil recovery is not a new idea, but the commercialization and wider application of this technique require detailed analyses to determine optimal application conditions. In this study, applicability of electrical heating for heavy-oil recovery from two heavy-oil fields in Turkey (Bati Raman and Camurlu) was tested numerically. The physical and chemical properties of the oil samples for the two fields were compiled, and in-situ viscosity reduction during the heating process was measured with and without using iron powder. Iron powder addition to oil samples causes a decrease in the polar components (such as carboxylic and phenolic acids) of oil, and the viscosity of oil can be reduced significantly because of the magnetic fields created by iron powders. Three different iron-powder types at three different doses were tested to observe their impact on oil recovery. Experimental observations showed that viscosity reductions were accomplished at 88 and 63% for Bati Raman and Camurlu crude oils, respectively, after 0.5% iron (Fe) addition, which was determined as the optimum type and dose for both crude-oil samples. Next, field-scale recovery was tested numerically using the viscosity values obtained from the laboratory experiments and physical and chemical properties of the oil fields compiled from the literature. The power of the system, operation period, and the number of heaters were optimized. Economic evaluation performed only on the basis of the electricity cost using the field-scale numerical modeling study showed that the production of 1 bbl petroleum costs approximately USD 5, and at the end of 70 days, 320 bbl of petroleum can be produced. When 0.5% Fe is added, oil production increased to 440 bbl for the same operational time period.
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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 | 0.000 | 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) | 0.001 | 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".