Incremental Oil Success From Waterflood Sweep Improvement in Alaska
Bibliographic record
Abstract
Abstract Waterflood thief zones in communication with the rest of the reservoir are a severe and previously challenging problem. This paper gives an introduction to the nature of a novel, heat-activated polymer particulate. Details of a trial of this in–depth diversion system, resulting in commercially significant incremental oil from a BP Alaskan field are presented. The system of one injector and two producers was selected because of a high water oil ratio and low recovery factor, which was recognized as an indicator of the presence of an injection water thief zone and confirmed by study of a previous injection survey. The area around the wells is bounded by faults so the system can be considered to be isolated from surrounding wells and operations. The position of the thermal front in the reservoir, tracer transit times, injection rates and inter-well separations indicated that the slowest reacting of the three commercial grades available was most appropriate for the trial. The treatment was designed using laboratory tests, and numerical simulation informed by pressure and chemical tracer tests. Long sandpack tests indicated permeability reduction factors of 11 to 350 for concentrations of 1500 to 3500 ppm active particles in sand of 560 to 670 mD permeability at 149°F. 15,587 gallons of particulate product was dispersed, using 8,060 gallons of dispersing surfactant, into 38,000 barrels of injected water and pumped over 3 weeks at a concentration of 3300 ppm active particles. Placement deep in the reservoir between injector and producer was confirmed by pressure fall off analysis and injectivity tests. The incremental oil predicted from the simulation was 50,000 to 250,000 bbl over 10 years. In fact over 60,000 barrels of oil was recovered in the first 4 years at a cost comparable with traditional well work and less than sidetracking.
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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 | 0.001 | 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".