New Aspects of Torque-and-Drag Modeling in Extended-Reach Wells
Bibliographic record
Abstract
Abstract The length and inclination of wells have increased significantly the past decades. The record horizontal departure today is about 11 km from the platform. Because of this evolution the number of offshore platforms to drain a field has been significantly reduced. Excessive torque and drag can be critical limitations in extended-reach drilling (ERD). Torque and drag modeling is regarded as an invaluable process to assist in well planning and to predict and prevent drilling problems. It discusses how to use torque and drag calculations and measurements to plan long-reach well profiles, to execute drilling operations that minimize torque and drag effects, to monitor hole cleaning, and to plan jarring operations. Torque and drag models have proven to be useful in all three stages of an extended reach well: planning, drilling and post-analysis. During planning phase the models are used to optimize the trajectory design to minimize the torque, drag and contact forces between drillstring and borehole wall. Used together with monitoring of hole conditions during drilling, T&D models are particularly useful in diagnosing hole cleaning problems, impending differential sticking, and severe doglegs as well as determining the possibility of reciprocating casing during cementing operations. In post-analysis the models help to determine true causes of hole problems that previously were unexplained or attributed to other factors such as mud weight, mud chemistry or problem shales. In this study a general overview on most of the available literature on the subject is presented. Different models that have been developed for torque and drag predictions along with pros and cons of the models will be discussed and the validity will be checked by applying the model for one field case study from an ERD well in North Sea. The field case also demonstrates the challenges and importance of buoyancy effects and well path effects.
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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".