Materials, Well Designs and Numerical Simulations for the Construction of a Low Loss Zone for Radiofrequency Downhole Heating
Bibliographic record
Abstract
Abstract Downhole RF heating continues to be the interest of the petroleum industry because of its advantages over conventional forms of heating. Previous results have shown that without a low-loss dielectric zone (LLZ) around the downhole RF emitter, none of the available linear dipole antennas can work efficiently, and most of the energy is absorbed preferentially in a few meter radius around the radiating well and will not penetrate substantially into the reservoir. To circumvent this problem, low-dielectric materials were proposed, which are composed of a solid mixed with an appropriate binder. These materials were selected to have low dielectric properties so that the RF absorption is minimized, and at the same time, low porosity to prevent water invasion during the RF heating operation. Four solids, Ottawa sand, solvent deasphalted tar, Poly(p-phenylene sulfide) (PPS) and Polyether ether ketone (PEEK) and four binders (polydicyclo pentadiene (DCPD) and phenol-formaldehyde resins (Novolac), a C-Class cement slurry, and a foamed cement) were evaluated by measuring their dielectric properties (dielectric constant and loss tangent) in the frequency range 1 - 2000 kHz and temperatures between 25–200°C. All four solids have low RF absorption as well as low porosity (<1%), and those values did not change significantly with temperature. Also, smaller dielectric properties were found for DCPD and Novolac than those found for the cement materials, and the DCPD binder has a dielectric constant almost half and a loss tangent one order of magnitude lower than those measured for the Novolac resin. Three different designs for the construction of LLZ were considered, which included underreaming the oil well, squeezing a solid-containing binder downhole, and creating a casing-less completion. Numerical simulations show that the use of a low-loss zone around the central emitter leads to a very much improved energy and temperature distribution, and higher penetrations (~12 m) than the case without it.
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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 | 0.001 | 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) | 0.004 | 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".