Use of Quantitative Risk Analysis Methods to Determine the Expected Drilling Parameter Operating Window Prior to Operation Start: Example From Two Wells in the North Sea
Bibliographic record
Abstract
Abstract While in a well-defined context, drilling operations can be optimized to a great extent, other more complex drilling operations, either due to challenging trajectories or taking place in more unpredictable geological setting, may incur undesirable delays due to unforeseen difficulties. For such challenging drilling operations, it is more and more common to perform pre-operation verifications in the form of "drill the well on the paper" or "drill the well in the simulator". Such methods may help the engineering team to discover some of the flaws of the operation plan but they fall short in providing quantitative risk assessments of the studied drilling operation plan. A methodology based on uncertainty propagation throughout all the input parameters of the drilling program has been developed to quantitatively assess the inherent risks levels embedded in a drilling operation plan. Hundreds of simulations are performed automatically to estimate a safe drilling parameter operating window for every depth of the planned drilling operation. The analysis of the obtained window size allows to determine the risk levels and which of them are the most predominant. This allows to check if the plan is robust and to prepare for countermeasures in case difficulties should be encountered. This quantitative risk analysis method has been applied to two challenging drilling operations performed in the North Sea. When a case has been described, the analysis is performed automatically and does not require any human intervention. The analysis of the results may thereafter be used by the drilling team to focus on the most essential challenges ahead of the drilling operation during the "drill the well simulation" session.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".