Optimal Application Conditions for Steam-Solvent Coinjection
Bibliographic record
Abstract
Abstract A limited number of laboratory and field evidences showed that steam-solvent coinjection can lead to a higher oil production rate, higher ultimate oil recovery, and lower steam-oil ratio, compared with steam-only injection. However, a critical question still remains unanswered: Under what circumstances the above mentioned benefits can be obtained when steam and solvent are coinjected? To answer this question requires a detailed knowledge of the mechanisms involved in coinjection and reflection of this knowledge to its numerical simulation. Our earlier studies demonstrated that the determining factors for improved oil production rates are relative positions to the temperature and solvent fronts, the steam and solvent contents of the chamber at its interface with reservoir bitumen, and solvent diluting effects on the mobilized bitumen just ahead of the chamber edge. Then, the key mechanisms for improved oil displacement are solvent propagation, solvent accumulation at the chamber edge, and phase transition. This paper deals with this unanswered question by deriving a systematic workflow for selecting an optimum solvent and its concentration in coinjection of a single-component solvent with steam. The optimization considers the oil production rate, ultimate oil recovery, and solvent retention in situ. Multiphase behavior of water-hydrocarbon mixtures in the chamber is explained in detail analytically and numerically. The proposed workflow is applied to simulation of the Senlac SAP pilot project to investigate reasons for its success. Results show that an optimum volatility of solvent can be typically observed in terms of the oil production rate for given operation conditions. This optimum volatility occurs as a result of the balance between two factors affecting the oil mobility along the chamber edge; i.e., reduction of the chamber-edge temperature and superior dilution of oil in coinjection of more volatile solvent with steam. It is possible to maximize oil recovery while minimizing solvent retention in situ by controlling the concentration of a given coinjection solvent. Initiation of coinjection right after achieving the inter-well communication enables the enhancement of oil recovery early in the process. Subsequently, the solvent concentration should be gradually decreased until it becomes zero for the final period of the coinjection. Simulation case studies show the validity of the oil recovery mechanisms described.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".