Monte Carlo Analysis of Advanced Spline Curves for Wellbore Trajectory Uncertainty Calculations
Bibliographic record
Abstract
Abstract The wellbore position uncertainty model is an important part of well design and operational considerations particularly for multi-well pads. The true uncertainty ellipsoids are dependent on the wellbore trajectory calculation method as much as it is on the quality of the survey measurements. Using the advanced spline curve method, more accurate uncertainty ellipsoids can be generated enabling engineers to optimize wellbore position for drilling, geological, reservoir, and production engineering applications. To demonstrate that the advanced spline curve method models the true wellbore position more accurately than minimum curvature, trajectory calculations of a well surveyed with high resolution continuous gyroscope measurements are compared with the same wellbore after down sampling. The error of the two methods with the down sampled survey are compared to the base case. With the advanced spline curve method established, the uncertainty ellipsoid is calculated for each survey station for a set of wells on a multi-well pad. A Monte Carlo simulation using an industry standard error model for the survey measurements generated the uncertainty ellipsoids. The confidence interval, agreement of distribution, and collision risk of both calculation methods are evaluated and compared to a high-resolution survey. This paper presents a Monte Carlo analysis of the wellbore position error model generated by the advanced spline curve method to that of the error model generated by the minimum curvature method. The error induced by the calculation method is reduced loosening the constraint of collision risk on wellbore design. Improved knowledge of the wellbore position benefits all engineering aspects for the life of the well.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".