Experimental and Numerical Modeling of Heavy-Oil Recovery by Electrical Heating
Bibliographic record
Abstract
Abstract Electrical heating for heavy-oil recovery is not a new idea but commercialization and wider application of this technique require detailed analyses for determination of 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 experimentally and numerically. The physical and chemical properties of the oil samples for the two fields were compiled and measured. Then, core samples were exposed to electrical heating and oil recovery performances by the retort technique were determined for different conditions. Experiments with and without using iron powder were analyzed and in-situ viscosity reduction during the heating process was determined through a history matching process using the simulation of the laboratory experiments. Experimentally obtained oil recovery and temperature distributions were used in this history matching exercise. Iron powder addition to oil samples causes a decrease in the polar components of oil and the viscosity of oil can strongly be influenced by the magnetic fields created by iron powders. Therefore, 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 as 88% and 63% for Bati Raman and Camurlu crude oils, respectively, after 0.5% Fe addition, which was determined as the optimum type and dose for both crude oil samples. Different parameters (thermal diffusion coefficients, oil viscosity, and relative permeabilities) that are needed in numerical modeling as data were determined through experimentally validated numerical modeling study. Furthermore, field scale recovery was tested numerically using the parameters obtained from laboratory scale experimental and numerical modeling results. The power of the system, operation period and the number of heaters were optimized. Economic evaluation done using the field scale numerical modeling study showed that the production of one barrel petroleum costs about 5 USD and at the end of 70 days, 320 barrels petroleum can be produced. When 0.5% Fe is added, oil production increased to 440 barrels for the same operational time period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".