EOR Strategies for a Conventional Heavy Oil Reservoir with Large Aquifer in Greater Fula Oilfield, Sudan
Bibliographic record
Abstract
Abstract Thermal recovery technology particularly cyclic steam stimulation (CSS) is always an effective means to develop the conventional heavy oil reservoirs, which can be validated from literature. While most of the heavy oil reservoirs developed by CSS are the thick, well-deposited, high quality reservoirs and there are no much reports of producing oil from mid-depth oil reservoirs with large acquifers. In this paper, according to the petrophysical properties and geologic characteristics of the target block F in Greater Fuld oilfield in Sudan, based on the oil test results, detailed 3D geologic model is established and the type well model for CSS and SF is extracted, to study the real performance with the real geological properties. The development zone, the perforation strategies, the cylic steam injection quantity, the steam injection rate, soak time, and cyclic period are optimized for CSS. Based on the production performance of CSS, the optimal cycles of CSS followed by SF is determined. And the wellpattern and well spacing, the parameters of SF such as unit steam injection rate, steam quality, effects of bottom acquifer on the SF are also simulated and optimized. The simulation results indicate that the thermal recovery technique especially 4 cycles of CSS followed by SF can acquire satisfied performance, which shows an effective and economic future in the development of the heavy oil deposits in Greater Fula Oilfield.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".