Study on Start-Up Process of SAGD by Solvent: Experiment Research and Process Design
Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) has been used to develop the “super heavy” oil reservoirs in Canada. The viscosity can reach more than 30,000 cp at 50°C. Moreover, owing to their continental deposit origin, the reservoirs have a low porosity and permeability. Because of these challenges, the conventional steam circulation start-up process takes 6 to 12 months before the well pair can be switched to production. Solvent has been used to start-up SAGD with success. But now, low price of oil and high cost of solvent make solvent-assisted start-up process limited. This paper applies experimental schemes, such as viscosity reduction rate evaluation, core flooding, and 3D physical simulation, tests solvent performance, optimizes process parameters, and designs process solutions. Apply numerical simulation to test solvent-assisted SAGD start-up effect and calculate the cost. This paper researches a unique low-cost solvent compare with xylene. The basic properties and core flood experiment show that the two solvents are similar with viscosity reduction rate, asphalt dissolution rate, and injection pressure, and the price of solvent is 18% lower. The 3D model experiment shows that the average start-up time is reduced by 15%, and steam injection volume is reduced by 21.4%. The numerical simulation results show that without solvent, it will take 180 d for start-up process, and with solvent, the time has reduced by 50% and takes 90 days. Cost calculation results show that the cost will reduce 18% by solvent compared to xylene. Moreover, the production rate has been improved in production stage. This paper applies a 3D physical model to simulate the solvent-assisted SAGD start-up process. Research conclusions show the start-up mechanism of solvent and the process of temperature change of steam chamber.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".