Identification and Analysis of Fields for Waterflood-Enhanced Recovery Efforts
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Changing economic conditions allow for a new examination of mature and abandoned fields to identify oil that is now economically recoverable (reserves). In mature oil producing basins there are many inactive or limited stripper production fields, all potential waterflood candidates. We present a methodology of identifying such fields, quantifying the incremental waterflood production, and creating a field development plan. Production forecasts are generated in a timely and cost effective manner for development scenarios with Time Dynamic Volumetric Balancing methods. The field reviewed and used as an example is the Belcherville Field. It is a Caddo Conglomerate, located in Montague County, Texas, discovered in 1946. Developed in the 1950's, 9 wells produced 3 mmbo of primary production before being abandoned in 1967. Production was from the channel deposits of the Caddo formation that are draped over a gentle anticline. A comparative study of 7 other fields in the same trend indicates waterflooding can recover and additional 84% of primary recoverable oil.
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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) | 0.000 | 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) | 0.000 | 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 it