Benchmarking offshore drilling: methodology and case study
Bibliographic record
Abstract
Abstract Oil exploration is complex and very expensive, presenting long-term returns and considerable risks of accidents. Saving time is the guideline of any optimization method to reduce drilling costs and CO2 emissions. Highly variable performances are found in any oilfield, even in similar depth ranges, due to multiple interactions between the many factors involved in the drilling operations. Understanding such gaps is complex, as different technologies and levels of energy are used in each bit run along an exploration campaign, affecting in different ways their performances. The objective of the methodology presented in this article is to provide tools to identify which are the factors and how they affect the productive time used to drill oilwells, allowing to measure the contributions of each factor in increasing the efficiency of the drilling operations. A case study analyzes a 35-year exploratory campaign offshore Brazil, where data of the 109 wells drilled in an oilfield is used to benchmark different performances, demonstrating the advantages of the methodology in oilwell planning and in finding the paths for reducing drilling costs and CO2 emissions. As drilling for O&G, CCUS and geothermal wells is expected to continue yet for decades, the learnings and the economic benefits for the operators will extended in the long terms to the whole society.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".