Unplanned Tortuosity Index: Separating Directional Drilling Performance from Planned Well Geometry
Bibliographic record
Abstract
Abstract Wellbore tortuosity is an important metric of wellbore quality; however, it is not always an appropriate reflection of directional drilling performance. Drilling planned tortuous features will increase wellbore tortuosity, but this in itself says nothing about directional drilling performance. Not only is there a need for a metric of wellbore tortuosity, it isalso necessary to have a metric of "unplanned" wellbore tortuosity. The former provides information about wellbore quality, whereas the latteris reflective of directional drilling performance. The directional drilling literature contains metrics for both wellbore and unplanned tortuosity; however, they are largely unique and difficult to relate to one another. It is desirable for the planned and unplanned tortuosity metrics to be relatable. This would allow operators to not only quantify overall wellbore tortuosity in real time, but also to understand how much of that tortuosity is unplanned and possibly avoidable. This, then, opens up avenues for directional drilling performance improvement. In this paper, a new metric, the "Unplanned" Tortuosity Index is developed on the basis of an existing metric of wellbore tortuosity. This is done by systematically removing the effects of intended tortuous features from the wellbore tortuosity analysis, retaining only those tortuous features in the wellbore trajectory that are unplanned. The unplanned tortuosity index is then tested on two distinct sets of survey data from actual wells drilled. The results are compared between the sets and with the wellbore tortuosity metric from which the new index was derived. It is shown that thenewly developed unplanned tortuosity index canhelp operators and directional drilling companies discern their directional drilling performance, especially forwell paths with multiple, planned tortuous features.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".