A Doomed Reservoir Surprisingly Became a Mature Reservoir with Potential: Water Dump Flooding Case Study in Boca Field
Bibliographic record
Abstract
Water Dump Flooding is less well known for revitalizing mature fields.However, in the Boca Field, specifically Reservoir 95 Y-102, this is exactly what occurred.A periodic review of this mature field suggested abandoning the only producing well in this reservoir, well X-3, because an adjacent well, X-6, located above the dip was known to produce a 99% water cut.Although other wells were produced in the reservoir, only a 12% recovery was achieved.Therefore, an integrated study to re-evaluate the parameters and properties of Reservoir 95 Y-102 began in 2005.During the well analysis, it was found that the water production of well X-6 was the result of the communication behind the casing of the well with the underlying aquifer 101 and not because of the advancement of the oil-water contact, as initially suggested.Recompletion of well X-3 was recommended because an injection process known as dump flooding was underway.In addition, aquifer support for production over the previous seven years strongly indicated that dump flooding would produce the desired production increases.Under sub-optimal conditions, accidental Water Dump Flooding rejuvenated the producing well, increasing production to more than 300 BOPD with an acceptable water cut of 61%.The steps followed for the analysis and understanding of the process that occurred and how we took advantage of this accidental Dump Flooding raised the production from nearly zero to over 100,000 barrels produced in a single year.This lays the foundation for using Dump Flooding as a production and development strategy for other projects in the area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".