A Comparative Field-Scale Simulation Study on Feasibility of SAGD and ES-SAGD Processes in Naturally Fractured Bitumen Reservoirs
Bibliographic record
Abstract
Thermal processes are widely applied in heavy oil and bitumen reservoirs. Steam-Assisted Gravity Drainage (SAGD) and Expanding Solvent-SAGD (ES-SAGD) are the two most promising techniques for production of heavy oil and bitumen reservoirs. The feasibility and efficiency of the aforementioned processes have been studied extensively for sandstone reservoirs. As there are some naturally fractured heavy oil resources in the world similar thorough studies are necessary to find out the applicability of these processes in these types of reservoirs. In this work several numerical simulations have been performed to investigate the feasibility of these methods in naturally fractured reservoirs (NFR). Dual permeability option was applied in this study. This work tries to cover the effect of several parameters such as reservoir thickness, fracture permeability, matrix permeability, fracture spacing, steam quality and oil viscosity on SAGD and ES-SAGD processes. Results have elucidated that basically both methods are efficient and economically applicable in naturally fractured reservoirs. Recovery factor and cumulative steam-oil ratio (CSOR) were economical parameters to evaluate the performance of processes. However, in some cases net amount of injected energy and oil production rate were considered for evaluation too. Generally ES-SAGD had better performance based on the recovery factor, CSOR, oil production rate and net amount of injected energy. Effect of solvent type and concentration were studied for ES-SAGD process only. For this purpose, pentane, hexane and heptane were selected. Pentane displayed better performance in earlier periods of production; however the heavier solvents showed better recovery performance at later times.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".