Automatic Performance Analysis and Estimation of Risk Level Embedded in Drilling Operation Plans
Bibliographic record
Abstract
Abstract It is not unusual that the difficulties encountered during a drilling operation can be tracked down to choices made during the planning stage. However, generating a robust drilling operation plan is not easy as there are often substantial uncertainties associated with the actual geological context. To address this problem, a method is proposed that evaluates quantitatively the risk levels of a drilling operational plan as a function of the underlying uncertainty associated with its description. To achieve that goal, the first task is to describe precisely the sources of uncertainties. The limits by which a drilling operation shall conform may be uncertain therefore defining fuzzy risk boundaries. The actual well construction may deviate from the plan, either because of unavoidable uncertainties in measurements as with the actual wellbore position, or because adjustments will be made during the operation such as changes in mud weight and rheology. Finally, the actual possible drilling performance may be subject to unforeseen geological variations and unprecise formation rock strength characteristics. Equipped with this probabilistic environment, we can perform drilling optimization in two successive steps. First, the uncertainty is propagated into drilling simulations, and risks associated to pressure control, cuttings transport, and drillstring stability are evaluated. Second, parameter optimization within the allowed window, can provide a strategy to obtain the best performance possible with respects to acceptable risk levels. As a result, both quantitative risk analysis and drilling optimization under several constraints are available to the drilling engineer as a decision support tool for well planning.
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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".