Intelligent and Automated Workflow for Identification of Behind Pipe Recompletions and New Infill Locations Opportunities for an Onshore Middle East Field
Bibliographic record
Abstract
Abstract An integral aspect of field development planning is the process of identifying remaining, feasible, and actionable field development opportunities (FDOs) such as recompletions and new drills. In mature brown fields, this process is extremely difficult to undertake in a reasonable timeframe due to the sheer size of the available datasets (e.g., thousands of wells, decades of production history), geologic complexity (e.g., dozens of layers, faulting, folding, fractures, pinchouts), and engineering challenges (e.g., commingled production and injection, field compartmentalization) unique to each reservoir. The automated framework is a fully streamlined framework that aims at building a comprehensive opportunity inventory consisting of behind-pipe recompletion opportunities, vertical new drill locations, sidetrack opportunities, and optimal deviated/horizontal targets. In addition to opportunity identification, it can generate additional deliverables for vetting purposes including a well book report and a standardized Petrel project. The main objective of this process includes: 1) fast, data-driven, and consistent approach for evaluating and identifying field development opportunities, 2) fast execution time, accelerating processes from months to days, 3) integration of multi-disciplinary data, 4) consistency and repeatability, minimizing subjectivity, and 5) streamlined visuals for opportunity vetting. This framework has been successfully applied to a mature field in the Middle East with more than 1,000 producers and 55 years of production history. Hundreds of recompletion opportunities were initially identified for the field. A final list of opportunities is then generated by running the opportunities through a series of user-provided attribute filters such as minimum initial rate, minimum oil thickness, maximum acceptable uncertainty, and so on. The process is then typically finalized with manual vetting of the opportunities by subject matter experts including geologists, reservoir engineers, and production engineers that ensure the opportunities are geologically sound, mechanically feasible, and meet various validation criteria that are discussed in this document. A final list identified 116 recompletion opportunities in 116 wells with a total potential production upside of around 63,000 B/D.
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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 | 0.000 | 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 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".