Real Time Surface Data Driven WOB Estimation and Control
Bibliographic record
Abstract
Abstract In directionally drilled oil and gas wells, wellbore friction and tortuosity play an important role on how efficiently torque and weight-on-bit (WOB) is transferred from surface to the drill bit. Providing accurate and consistent true WOB in a real-time manner is crucial to maintaining optimum drilling efficiency. In this paper, we present a novel, automated system for estimating and controlling downhole WOB in real-time using surface data. The new system presented here automatically consumes drill-string and wellbore information while drilling to build a drill-string model with multiple sections, each with its own length, size, heading and wellbore friction factor. An automated algorithm captures and stores off-bottom hook-load data that meet certain rig-state conditions while monitoring multiple surface data streams. A numerical optimization method is then used to solve for the friction factor distribution with depth using selected off-bottom hookload data. The friction factors are put into a torque and drag model to estimate the downhole WOB. Numerical simulation shows that using the time history of surface data rather than a single hookload reading is key to ensuring a robust solution against noisy and missing data. An active control mode is also implemented in which the driller can directly control the downhole WOB through the system. The system was field tested on multiple horizontal wells drilled by the operator. Comparisons with downhole measurements show the system is capable of accurately predicting downhole WOB throughout the well including the lateral section. Solving the friction model using historical hookload data ensures the stability and robustness of the estimation. The distribution of friction factors with depth also help provides additional assessment of wellbore quality. An automated WOB control test was also conducted to demonstrate the system's ability to directly control downhole WOB by adjusting surface WOB according to the algorithm. The new system provides a novel way to use real time surface data to estimate and control downhole WOB
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".