Thermal Solvent Reflux and Thermal Solvent Hybrid Experiments
Bibliographic record
Abstract
Abstract The experiments and numerical simulations described in this paper were performed to determine the effectiveness of the thermal solvent reflux and steam solvent hybrid processes and to determine the key parameters involved in these two processes. Steam-assisted gravity drainage (SAGD) is the current technology of choice for Athabasca reservoirs. It is commercially proven and delivers high oil rates and high ultimate recoveries. However, it is energy intensive. In addition, steam injection may be limited by lack of water, by regulatory issues or by the fact that some reservoirs are water sensitive. Vapour-assisted petroleum extraction (VAPEX) does not have the energy and water problems associated with SAGD. However, it is inherently slower than SAGD. One solution to the above difficulties is to combine processes by heating the horizontal wellbores. The heat serves to initiate communication between the injector and the producer. In addition, it will increase the rate of diffusion of the solvent into the oil. Heat may be applied by electric heaters, with a closed steam or glycol loop, or by direct injection of steam. In the first three of four experiments described in this report, the wells were electrically heated. In the fourth experiment, steam was co-injected with the solvent. Two experiments using Hillmond oil showed that similar results were obtained whether heating was obtained by electrically heating the wells (thermal solvent reflux process) or by direct steam injection (steam solvent-hybrid process). For these two experiments, the oil rate and recovery were similar. Numerical simulations were used to history match the experiments and effective diffusivity values were estimated. Introduction The objective of the thermal solvent reflux experiments was to develop a solvent-assisted process for recovery of heavy oil from thick, cold reservoirs such as Cold Lake and Athabasca. The thermal solvent process concept (Figure 1) is • Inject solvent and produce oil through horizontal wells • Heat the injection and production wells to reboil the solvent in situ (solvent reflux) (i.e., in-situ recycle of solvent) The advantages of the thermal solvent reflux process are • Requires less heat than SAGD • Less steam plant emissions • Smaller solvent recycle plant on surface • Smaller injection facility needed • Does not inject water into the reservoir • Suitable for reactive reservoirs • No treatment of boiler feed water required • Minimal water disposal • Minimal oil/water separation facilities required • Heat will speed mass transfer over cold VAPEX
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".