Scaling up of Surfactant EOR Field Implementation in an Offshore Field Through Optimal Pilot Design
Bibliographic record
Abstract
Summary Wettability Alteration from oil-wet to water-wet condition is a very promising EOR technique for producing significant incremental oil recovery from oil-wet tight pores. Surfactant EOR has been widely linked with IFT reduction through continuous surfactant injection for long durations. NOC along with DOW Chemicals developed an alternate Surfactant which induced wettability alteration in oil-wet cores at Lab. This Lab concept was derisked at field level through an injectivity test first (S. Furqan Gilani et al, 2018) and then sequentially scale up via single injector pilot (Neeraj Rohilla et. al, 2022). After the success of the first Pilot involving deeper injection a second pilot was recently completed in a different sub-surface environment involving shallow injection. Second pilot has helped prove permanency of the wettability alteration even after the injection of surfactant has been stopped. Unlike IFT reducing surfactant, wettability altering EOR requires only 9-month long treatment of injector, this tilts the economics related to this EOR to a favorable place and makes it one of the most robust and economical EOR technologies available for an offshore field. After successful implementation of two field trials for an offshore carbonate reservoir, comprehensive studies are being undertaken to scale-up from pilot to field-wide implementation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".