Managing of Hybrid Steam-solvent Injection Distribution for Maximizing Recovery Factor in Heterogeneous Reservoir
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage process has been done and successfully implemented for the producing of the Canadian heavy oils and bitumen. Although it is commercially proven, but it still uses high energy intensity and has high environmental impact. To reduce those side effects, hybrid steam-solvent is proposed as one of the alternative processes in order to reduce the problems. There are some challenges in reservoir heterogeneity correlated with steam-solvent injection. In conventional process, spreading of steam-solvent is able to be controlled using bottom-hole pressure injection and liquid production rate. In heterogeneous reservoirs however, each part of the horizontal well will have different optimum operation conditions. This paper presents a strategy for hybrid steam-solvent optimization using ICVs (interval control valves) to maximize energy efficiency. To provide an overview of this phenomenon, the synthetic reservoir model is built from McMurray Formation. Bitumen saturation distribution and rock properties are created using geostatistic method and validated using several wells. The reservoir models were run using several operating conditions to achieve the most realistic interpretation. Sensitive variables are steam injection pressure, length grouping of perforations, managing of perforation openings, and solvent concentration. Afterwards, steam efficiency and solvent affectivities are evaluated during 15 years of project life. Simulation results show that the managing of steam injection will increase heat efficiency up to 7.4% compared with conventional perforations. Furthermore, in the hybrid steam-solvent injection, the recovery factor will be better 8% compared to without adjustment in solvent concentration of 10% volume. Finally, retention of solvent injection can also reduce until 30% by this managing of processes.
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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".