Using Data-Driven Technologies to Accelerate the Field Development Planning Process for Mature Field Rejuvenation
Bibliographic record
Abstract
Abstract A data-driven technology and associated workflow for fast identification of field development opportunities in mature oil fields is presented, which accelerates the subsurface field development planning process and reduces the time requirement from months to weeks. Standard workflows in geology and engineering have been automated or machine-assisted, enabling field rejuvenation opportunities to be identified without requiring full-field simulation models. This technology is ideally suited for large, complex oil fields with large data sets (e.g. thousands of wells producing over many decades), and has been deployed in cases of brownfield rejuvenation, asset evaluation during acquisition activities, and as an independent validation system within internal review programs for large oil companies. The opportunities generated using these techniques are subject to a rigorous technical vetting by experienced subject matter experts, with the highest confidence opportunities being matured and high- graded. A case study is presented for a large, stratigraphically complex waterflood in North America, wherein a subsurface field development plan was prepared using these techniques, with specific opportunities in well operations, production uplift, recompletion targeting pay-behind-pipe, infill and step-out drilling locations, and waterflood optimization.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".