Experimental Investigation of Combined Electromagnetic Heating and Solvent Assisted Gravity Drainage for Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Electromagnetic (EM) heating holds a large potential in heavy oil recovery since it can reduce carbon emission and avoid excessive water usage, and is applicable for water hostile reservoirs such as shale oil reservoirs. Combining solvent injection and EM heating might further reduce the energy intensity of the process. The merits of using solvent in EM heating include diluting heavy oil and thereby increasing its mobility, serving as a heat carrier by reinforcing heat convection in porous media and facilitating gravity drainage by forming a vapor chamber. Detailed experimental investigations are needed to investigate the mechanism of such a complex process and to specify the most influential factors of this hybrid and expensive process to determine optimal operational conditions. In this study, we conduct a series of laboratory experiments to investigate the mechanisms of combined EM heating and solvent assisted gravity drainage for heavy oil recovery. During experiments, sand pack samples contained in Buchner filter funnel are placed in a microwave oven. Solvent injection can be initiated together with EM heating to simulate the hybrid process of combined EM heating and solvent assisted gravity drainage. We investigate the effects of influential factors on the process efficiency, including initial water saturation, solvent types (n-hexane and n-octane), introduction methods of solvents (injection or premixed with oil), combination strategies of solvent injection and EM heating (simultaneous or alternate means), and EM heating power. Temperatures of the sand pack and oil recoveries are simultaneously recorded. Experimental results show that combined EM heating and solvent assisted gravity drainage could effectively enhance heavy oil recovery compared with EM heating or solvent use alone. A higher heating power provides a faster temperature rise and earlier oil production in the sand pack. Moderate initial water saturation could increase the heating speed, leading to a higher oil recovery. Solvent injection can further enhance the viscosity reduction and swelling effect of heavy oil due to EM heating. Compared with n-hexane, n-octane provides higher vertical displacement efficiency and oil recovery under the same experimental conditions. Alternate EMH and solvent injection is more cost effective due to the lower energy consumption compared to the simultaneous EM heating and solvent injection.
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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".