Feasibility Evaluation of RF Heating Heavy Oil Reservoir Based on the Interaction Between Rocks and Electromagnetic Waves
Bibliographic record
Abstract
ABSTRACT: Currently, radio frequency (RF) heating is emerging as an alternative to steam thermal recovery methods, owing to its numerous advantages including cleanliness, environmental friendliness, and cost-effectiveness. Achieving maximum heating distance and temperature during RF heating hinges on understanding the interaction between RF electromagnetic waves and rocks. To address this, the present study elucidates the RF heating mechanism, establishes geometric and mathematical models accounting for rock properties, and validates the mathematical model using laboratory experiments. The calculations reveal that lower specific heat, thermal conductivity, relative permittivity, and density of rocks, coupled with higher rock electrical conductivity, contribute to higher maximum reservoir temperatures. Additionally, the maximum heating distance increases with decreasing specific heat and density of rocks. Optimal values of thermal conductivity, relative permittivity, and electrical conductivity exist, maximizing the heating distance. These findings provide crucial guidance for optimizing rock properties, enhancing maximum heating temperature, and extending heating distances before implementing RF heating technology. 1. INTRODUCTION Heavy oil resources are abundant around the world and have broad development and utilization prospects. However, the characteristics of heavy oil with high viscosity and poor fluidity greatly increase the difficulty of extraction. Currently, the steam recovery technologies are widely used for heavy oil extraction, but it has gradually exposed many shortcomings such as high energy consumption, high carbon emissions, and difficulty in effectively exploiting low permeability, deep and thin layers of heavy oil. So many oil companies have no option but to develop new heavy oil mining technologies. A novel heavy oil extraction technology combined with electromagnetic power appears and is collectively referred to as "RF heating" by some oil companies and scholars. Some literatures (Godard and Rey-Bethbeder; Hu and Li et al.; Saeedfar and Lawton et al.; Bera and Babadagli, 2015; Ghannadi and Irani et al., 2016) have introduced its advantages, such as being environmentally friendly, highly heating efficiency, extracting resource in deep oil-bearing formation, avoiding extra heat loss and so on. In the early days, some scholars done some important researches of RF heating for improving heavy oil recovery. In recent years, there were some latest studies on the development of RF heating. world-renowned companies have conducted several researches on RF heating technology. The most representative one is a project called "ESEIEH" carried out in the Steepbank mine in Canada in 2012. Several antennas that emit RF electromagnetic waves are mainly used to radiate electromagnetic waves to reduce the viscosity of heavy oil (Rassenfoss, 2012). In this project, the first phase of field testing has been successfully completed. Besides, Bientinesi et al. conducted an RF heating experiment and proved that RF heating can effectively reduce the heavy oil viscosity (Bientinesi and Petarca et al., 2013). In 2017, Bera and Babadagli experimentally confirmed that Ni and Fe nanoparticles added to the heavy mixture can improve the heating efficiency of RF electromagnetic waves (Bera and Babadagli, 2017). Harris Company developed the "Heatwave" technology to exploit the abundant heavy oil and oil sand resources in Canada, and the field test was successful. In the test, an antenna was uses to radiate electromagnetic waves with the frequency of 6.78MHz into the reservoir, and the maximum heating distance reaches 12.5m (Wise and Patterson, 2016). Wang et al. studied the RF heating mode based on the antenna arrays in 2018 (Wang and Gao et al., 2018; Wang and Gao et al., 2018). The simulation results showed that the antenna array configuration can greatly increase the heating range of heavy oil reservoirs (Wang and Gao et al., 2019; Wang and Gao et al., 2020). However, based on the current research progress, there is a lack of research on the influence of the rock properties on the maximum temperature distribution and furthest heating distance, making it difficult to accurately predict the heating range and heating temperature.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".