Improvement of the Recovery Factor Using Nano-Metal Particles at the Late Stages of Cyclic Steam Stimulation
Bibliographic record
Abstract
Abstract Cyclic steam stimulation (CSS) has recently re-gaining attention as an alternative to the current applications, such as SAGD or CHOPS. However, low recovery factor and excessive water production are critical problems to tackle. These late-stage experiences require a revisit to the options of recovery improvement and reduction of water production at the mid- and late-stages of the cycles. This research proposes a new approach for this purpose and investigates the effects of nano-metal particles introduced into the reservoir on the recovery factor after several cycles of steam injection. We begin with CSS experiments on heavy-oil saturated sandpacks studying and clarifying the influence of the nanoparticle presence on the efficiency of the process. It is confirmed that the use of nanoparticle in the first place increases the oil recovery factor along with a higher water production. Then, one run of experiment is performed by introducing the nanoparticle in the sixth cycle of CSS treatment. It is found that a substantial increase in the recovery factor is achieved in such a simulated CSS experiment, which cannot be achieved by injecting steam alone. This experimental study on the addition of nickel particles to improve the recovery factor and extend the life of CSS process will not only add value to nano-technology use in the oil industry from a physics point of view, but will also provide insight into the operating parameters for better late- stage CSS project development.
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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 | 0.000 | 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".