A Semianalytical Model for Simulating Combined Electromagnetic Heating and Solvent-Assisted Gravity Drainage
Bibliographic record
Abstract
Summary Solvent/thermal hybrid methods have been proposed recently to enhance heavy-oil recovery and to overcome the shortcomings that are encountered when either method is solely applied. One of the methods for this hybridization is to combine electromagnetic (EM) heating and solvent injection to facilitate heavy-oil production by gravity drainage. This approach has several advantages including reduced CO2 emissions, decreased water consumption, and appropriateness for water-hostile reservoirs. We are currently lacking any mathematical model for better understanding, designing, and optimizing this hybrid technique, which is partly attributed to this technique still being in its infancy. We propose a semianalytical model to predict the oil-flow rate resulting from the combined EM heating and solvent-assisted gravity drainage. The model first calculates the temperature distribution within the EM-excited zone caused by the radiation-dominated EM heating. Using different attenuation coefficients within and beyond the vapor chamber, the model can properly describe the corresponding temperature responses in these regions. Next, an average temperature of the chamber edge contributed by EM heating is used to estimate the temperature-dependent properties, such as vapor/liquid equilibrium ratios (K-values), heavy-oil/solvent-mixture viscosity, and solvent diffusivity. Subsequently, a 1D diffusion equation is used to calculate the solvent-concentration distribution ahead of the chamber edge. Eventually, the oil-flow rate is evaluated with the calculated temperature and solvent distributions ahead of the chamber edge. The proposed model is validated against the experimental results obtained in our previous study, and the predicted oil-flow rate agrees reasonably well with the experimental data. The proposed model can efficiently predict the oil-flow rate of this hybrid process. We conduct sensitivity analyses to examine the effect of major influential factors on the performance of this hybrid technique, including EM heating powers, solvent types, solvent-injection pressures, and initial reservoir temperatures. The modeling results demonstrate that a higher EM heating power, a heavier solvent, and a higher solvent-injection pressure could accelerate the oil-recovery rate, but tend to lower the net present value (NPV) and increase the energy consumption. In summary, the newly proposed model provides an efficient tool to understand, design, and optimize the combined technique of EM heating and solvent-assisted gravity drainage.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".