Assessment of Oil Recovery Methods for Reservoirs in the Flemish Pass Basin
Bibliographic record
Abstract
Summary In recent years, the Flemish Pass Basin has been gaining momentum as an area of potential high-volume resources on the frontier of remote, deep-water offshore oil development. This simulation study utilizes three sector models representing regional, discovered reservoirs, and two tuned fluid models representing oil sampled from wells in the Flemish Pass Basin. Generally speaking, WAG is considered a late-life enhanced oil recovery (EOR) technique, while implementing WAG immediately upon first oil for secondary recovery is less common; however, may be equally or more valuable. This study aims to evaluate three secondary oil recovery methods, water flooding, gas flooding, and water-alternating-gas (WAG) flooding. Each recovery method is simulated with Schlumberger’s ECLIPSE reservoir simulator and uses a combination of three distinct reservoir geo-models and two fluid models. This study is a sensitivity analysis using geo-models that represent three discovered regions and two sampled fluids from the Flemish Pass Basin. The study is aimed at evaluating the effects of the various recovery methods over a duration of either five- or twenty-year forecast periods. Results from this study capture an inherent uncertainty by drawing from eighteen simulation cases to quantify the relative benefit of each recovery method. These results indicate that using WAG as a secondary recovery method can yield a 4% to 10% increase in recovery over water or gas flood, and that secondary WAG can extend a well pair’s production plateau by up to 80% in specific circumstances. Further observations indicate that secondary WAG in light oil reservoirs yield a ∼10% increase in recovery over secondary water or gas flooding. Using WAG in a medium oil reservoir yields a 4% to 9% increase in recovery over water flood, and a 2% to 16% increase in recovery over gas flood. In terms of geology, WAG is observed to be most valuable in ultra-high-quality reservoirs. The better the reservoir quality, the more recovery improvement. In terms of fluids, the medium oil responds best to the gas injection phase of WAG while the light oil appears to respond well to both phases. During development optimization, these trends can be accounted for in the injection cycle timing and duration for each phase. In terms of using WAG as a tertiary recovery method after a period of water or gas flooding, tertiary WAG is observed to be most beneficial in the low to medium quality reservoirs. Tertiary WAG extends the production duration and results in a ∼4% increase in recovery beyond water flooding. Study results go on to quantify the differences in water and gas breakthrough as a factor of pore volume injected (PVI) and conclusions further indicate which reservoirs are best suited for each recovery method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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) | 0.000 | 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 teacher head, 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".