Radio-Frequency Heating Combined With Solvent Injection for Heavy-Oil Recovery
Bibliographic record
Abstract
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 180709, “Heavy-Oil Recovery by a Combination of Radio-Frequency Heating With Solvent Injection,” by I. Bogdanov, SPE, S. Cambon, and M. Mujica, CHLOE, and A. Brisset, SPE, Total, prepared for the 2016 SPE Canada Heavy Oil Technical Conference, Calgary, 7–9 June. The paper has not been peer reviewed. The most popular technology for producing heavy oil (HO) and bitumen is reservoir heating, mainly by steam injection. Electromagnetic (EM) heating is a capable alternative. Radio-frequency heating (RFH) results from the microwave effect and has the advantage of avoiding problems associated with water supply and treatment. The advantages of RFH technology can be strengthened with solvent injection. After a period of preheating, solvent injection may lead to improved oil recovery because of additional decrease in oil viscosity. Introduction The complexity of HO recovery has inspired profound modifications in production technology. To find a successful technology, combinations of known mechanisms are considered frequently. Many of these modifications use solvent coinjection. No single commercial reservoir simulator is able to compute the interaction of an RF antenna with a reservoir. Despite this, field-scale RFH simulations have been performed with coupled simulators, which separately model reservoir dynamics and in-situ EM-field distribution. Although limited in computational performance, this approach can be used successfully for field-scale simulations. The principal object of the numerical study presented in this paper is the solvent/HO mixture under RFH conditions in an HO reservoir. The study has attempted to shed light on whether solvent-assisted RFH technology is capable of improving oil-recovery efficiency and to specify its advantages and draw backs in a typical Athabasca deposit. Simulation Model Large-scale RFH models have numerically simulated realistic oil recovery, providing a technical basis for critical analysis of oil-recovery processes. The numerical methodology based on loose coupling of reservoir and EM-field simulators has been developed recently. A coupling code has been built that simultaneously launches two simulators and initializes problem geometries and grids. The strong point of this approach is that the thermal multicomponent flow and the EM-field calculations can be presented on different grids adapted to their specific solutions. Main Results and Discussion Preheating. Preheating is necessary to enhance well connectivity and allow solvent injection. This period is common in thermal recovery for highly viscous oils. Two important advantages of RFH-based oil recovery during the preheating phase are that (1) well-injectivity problems do not exist for EM fields and that (2) the EM heating field is volumetric, spread over a certain distance in the reservoir, and, hence, may be more efficient than standard steam-circulation options. It should be noted, however, that other factors such as heating geometry related to well configuration can affect the efficiency of this process and should not be neglected.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".