Practical Automated Detection of Remaining Oil in Mature Fields Using Production and Geology Data
Bibliographic record
Abstract
Abstract In all development phases of brownfields, identifying sweet spots and the potential remaining oil in place to be recovered is a cornerstone of reservoir management studies and field development operations. This becomes even more imperative for mature waterfloods, where the increasing water cut hinders the ongoing oil production. The current algorithms in waterflood network-based models are capable of building and matching a reservoir model by employing reduced physics data-driven approaches. These schemes estimate the saturation in the entire field; however, the next step in those approaches is adding a fully-integrated and closed loop automated workflow to model the remaining hydrocarbon volumes. The new data analytics approach presented is a systematic bottom-up approach in which the field data, including injection and production, well perforations, pressure history, geological and fluid properties and original oil in place, are integrated. The remaining hydrocarbon thickness and saturation calculated from this methodology are key to target future reactivation and recompletion candidates, therefore potentially reducing CAPEX and OPEX. This automated methodology is robust to handle not only the waterflood but all the development phases. Moreover, it is much faster than the ubiquitous history-matching processes, which require several months on average to be completed. To this end, we have validated the results by a blind test approach to increase the confidence in using the methodology. This proposed workflow greatly improves the assessment capabilities to locate the remaining hydrocarbon resources, and therefore is pragmatic for the prediction of the most prolific future production targets.
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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 | 0.001 | 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 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".