A Random Method for Calculation of Hoisting Drag
Bibliographic record
Abstract
Abstract In reality, downhole conditions are highly unpredictable due to many uncertain and inconsistent factors, such as the uncertainty of the friction and contact between drillstring and bore-hole. As friction and contact are crucial components in torque and drag calculation, it is meaningful and practical to consider their uncertainty. This paper presents a random method for calculation of hoisting drag. Firstly, the finite element method (FEM) is used for hoisting drag calculation of a directional drilling well using Adanoy’s method in the deterministic case. Then two strategies are taken to model the random component in the downhole. The first strategy considers the randomness of the downhole friction. Instead of being a deterministic value, the friction coefficient is considered as Gaussian. The second strategy considers the randomness of contact between drillstring and wellbore. As a result, the drillstring is no longer continuously contacting with the wellbore in the curved section of well profile, which can help avoid overestimating torque and drag. Parametric studies on both strategies are conducted. Monte Carlo (MC) simulation is employed for statistical analysis. The probability density distributions and mean values of drag will be studied. The methodology can be extended into torque or drag calculation in lowering, ream in and ream out drilling conditions. Results from this paper indicate that surface hoisting drag is nearly Gaussian when the friction coefficient is Gaussian. The contact loss leads to considerable reduction in the surface hoisting drag when contact uncertainty is considered. The work of this paper will help estimate the range of surface drag and torque, which allows the well planner to develop a risk assessment for a challenging well trajectory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".