Drilling with Aerated Drilling Fluid from a Floating Unit Part 2: Drilling the Well
Bibliographic record
Abstract
Abstract The biggest challenge of the aerated fluid drilling technology development was overcome with the well in Albacora being drilled with the semi-submersible platform Petrobras 17 (P17). A previous paper1 described the stages before the drilling activity: planning, new equipment, rig modifications and all the preparation for drilling the well. This paper describes the drilling operation itself. All the important points and differences relative to the conventional drilling method are highlighted, with special emphasis to the new equipment installed at the platform: the vertical compact separator, the automatic control system, and the RiserCap™rotating control head. The operation of the new equipment, performance and the problems observed are presented and discussed. The points of improvement are suggested and ways to move forward with this new technology also discussed in the paper. The present work also addresses the positive impact of this field test relative to the implementation of the light-weight fluids technology from floating units. This initiative has been carried out systematically by Petrobras in the form of a Joint Industry Project (JIP) and has been congregating operators, service companies and consultants. The next steps of this project to use effectively light-weight fluids for drilling in deepwater are discussed. The experienced obtained with this operation was crucial to direct and guide the most important points to be studied and developed in the next phase of the project.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".