Experimental and Numerical Comparison of Flooding Schemes to Enhance Recovery of Light / Medium Heavy Oil in an Offshore Oilfield
Bibliographic record
Abstract
Abstract This paper shows how to develop the light/medium heavy oil reservoirs and to optimize the production in ND offshore oilfield by using experimental and numerical technologies. The reservoirs in ND oilfield which has large reserves of the light/medium heavy oil (viscosity from 50 to 750 mPa·s) are very complex; faults are well developed and divide the oilfield into many blocks. The current drive mechanism is a water flooding or natural depletion bringing the average pressure down sharply, and reaches its production/economic limit in some of the similar offshore heavy oil reservoirs. The low primary recovery factor and the potentially vast remaining oil in these reservoirs necessitates considering applying improved oil recovery technologies for reservoirs in ND oilfield. Both physical tests and numerical simulations on different kinds of flooding schemes are examined and compared in this paper to enhance the recovery of light/medium heavy oil reservoirs. More specifically, water flooding schemes under different injected water temperatures (50°C, 150°C, 200°C), gas flooding schemes using carbon dioxide/natural gas medium, WAG schemes by carbon dioxide are conducted respectively. Other flooding schemes, such as cyclic steaming, steam flooding and combustion process are not investigated since they are normally unable to perform well in light/medium heavy oil reservoirs. We also present an example of the use of the ND-B1 reservoir as a typical model by simulating the flooding schemes. The results of numerical simulation were derived for comparing with experiments parameters. This paper provides a good reference for other similar reservoirs in China to recover light/medium viscosity heavy oil.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".