Azeotropic Heated Vapour Extraction- A New Thermal-Solvent Assisted Gravity Drainage Recovery Process
Bibliographic record
Abstract
Abstract Thermal-Solvent Assisted Gravity Drainage processes are heavy oil recovery processes in which the stimulation mechanism for bitumen viscosity reduction is by heating and dilution. The range of the injected solvent concentration with steam may be low such as in Solvent Assisted-SAGD or very high such as in Heated Vapour Extraction. The performance behaviour of these processes is significantly driven by the complex thermodynamic interaction of steam and solvent, heat transfer, multiphase fluid equilibrium and flow in the porous medium. ExxonMobil and its affiliate Imperial have been optimizing the existing recovery processes and developing new technologies to improve the efficiencies and environmental performance of the heavy oil production operations. Recent focus has been on developing thermal-solvent based recovery processes through an integrated research program that includes fundamental laboratory work, advanced numerical simulation studies, laboratory scaled physical modeling, and field piloting. The research program aims at in-depth investigation and understanding of process physics and mechanisms, and evaluating process performance and behaviour to enable development of new recovery methods and to enhance the performance. This paper focuses on the fundamental concepts of Azeotropic Heated Vapour Extraction, a new thermal solvent recovery technology developed by ExxonMobil-Imperial. This technology takes the combined advantages of the solvent dilution mechanism for enhanced oil production rate with the minimum required energy (GHG emission), as well as the effectiveness of energy transport of steam to minimize the required solvent in circulation for the extraction process. In this paper, the complex solvent-steam phase behaviour and reservoir fluid flow and their interaction under operating conditions are investigated. The analysis of experimental and modeling data shows that the injection of solvent-steam mixture at its azeotropic condition results in significant improvement in process key performance indicators (KPI's). At these conditions, the reservoir is heated to the minimum boiling temperature of the solvent-steam mixture compared to a Heated VAPEX or Solvent Assisted-SAGD process resulting in the reduction of the required energy thereby minimizing the solvent-to-oil ratio. Also, due to phase equilibrium, the vaporization of in-situ water is prevented resulting in the reduction of retained solvent in the depleted zone of the reservoir. It is found that an improvement in the process KPI's is dependent on the volatility of the selected solvent. The process KPI's also vary with operational conditions. The recovery process is optimized for certain reservoir constraints through the selection of the solvent boiling range and consequently the azeotropic steam content in the injected mixture.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".