Studies and Pilot Project on Steam Stimulation with Multiple Fluids for Offshore Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Steam flooding and stimulation processes have proven to be the most promising method for commercial in situ recovery of heavy oil. For high quality and thick oil reservoirs, these processes can achieve oil recovery of over 30% OOIP. However, for thin, deep and offshore oil reservoirs, they are uneconomic due to excessive heat loss to overburden and great requirement to heat the reservoir rock. A new process, Steam and Multiple Fluids (SMF), is being developed to improve efficiency of the steam stimulation process for offshore heavy oil reservoirs. It involves the addition of non-condensable gases to the injected steam. Injected gases accumulate in the region away from the well and lower the temperature. Only the region’s temperature near the well is close to the temperature of steam. Heat loss to the overburden and the heat requirement to heat reservoir rock can be significantly reduced due to the lower temperature requirement. Considerable saving can be achieved from reduction in quantity of steam required for the process. This process is studied by using laboratory experiments and numerical simulations via a 3D thermal model for an offshore heavy oilfield. The results show that, compared to the cold production and standard steam stimulation processes, the oil rate from SMF is the highest. The application of this process makes production of offshore heavy oil economic and should extend the range of reservoirs that can be produced economically. A pilot test for calibrating this new process is reported.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".