The Viability of Oil Extraction from Trinidad Tar Sands by Radio Frequency Heating - A Simulation Approach
Bibliographic record
Abstract
Abstract Trinidad has tar sands resources of about 2 billion barrels of oil on land in the Parrylands/Guapo and Brighton areas. With an oil price of over USD 25 per barrel, commercial extraction of oil from Trinidad tar sands is viable but requires careful study. The relatively small extent of this tar sand (about 10,000 acres and with depths varying from surface to less than 500 feet) and with an oil in place of about 1000 times smaller than the Canadian tar sands, large scale surface mining and in-situ methods such as SAGD and VAPEX processes or their variants are not practical and also for environmental reasons. In this study we explore the viability of oil extraction from Trinidad tar sands by radio frequency (RF) heating. RF heating does not require an overburden and is cheaper than SAGD and VAPEX and is also environmentally friendly since no steam, water and solvents are needed. Studies have also shown RF heating to be uniform, quicker and with deeper penetration than direct electrical (resistive) heating and an oil recovery between 50 to 80 % can be achieved. Preliminary studies indicated that Trinidad tar sands are wetting and with permittivity in the range 38 −100, which makes it suitable for RF heating. The COMSOL Multiphysics® software was used to simulate oil sand samples contained within a copper chamber and the RF heating was applied via a dipole antenna. Temperature- time heating plots were generated using an operating frequency of 10 MHz and a current of 50 Amperes. This data and the physical properties of the tar sands were then simulated using the CMG software. The results indicated an oil recovery in the range 30-60%. An energy balance was then conducted and the results show that commercial extraction of oil from Trinidad tar sands by RF heating is viable with an oil price of over USD 25 per barrel.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".