Evaluation of the Hybrid Process of Electrical Resistive Heating and Solvent Injection through Numerical Simulations
Bibliographic record
Abstract
Abstract Electrical Resistive Heating (ERH) has been proposed as a thermal recovery method for heavy oil reservoirs with low environmental impact. ERH could potentially be an alternative to steam-related processes in the reservoirs which are not suitable for steam injection methods due to low incipient injectivity and formation incompatibility. Meanwhile, Vapor Extraction (VAPEX) has been tested as an environmentally sustainable oil recovery method in both lab scale and field scale. However, the field test results showed that this process is not efficient and economical due to low mass transfer and low horizontal well efficiency. This paper presents a hybrid process of ERH with Solvent Injection. The hybrid process could enhance horizontal well efficiency and overall oil production rate, with less environmental impact than other steam-related thermal processes. Numerical simulations were conducted to evaluate this process via CMG’s STARS. Well pattern similar to that in classical SAGD process is used. The electrode is placed along with the producer or injector and solvent is injected from the injector. This process has three features which contribute to the enhanced oil flow: (1) the heat from producer establishes good communication between the injector and the producer by reducing viscosity; (2) the in-situ generated heat through ERH along with the horizontal wellbore is insusceptible to reservoir heterogeneity. Thereby the horizontal well conformity can be improved; (3) the solvent can reduce the viscosity of the heavy oil in unheated zone where the ERH can not reach; while it can also assist viscosity reduction of heavy oil in the heated zone. The factors affecting this hybrid process, such as electrode placement, voltage, well distance and heterogeneity effect, lateral pattern and water saturation, were also discussed in this paper. The simulation results showed that this hybrid process can improve the oil rate 2 to 5 times over VAPEX.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".