Case History: Automated Drilling Performance Measurement of Crews and Drilling Equipment
Bibliographic record
Abstract
Abstract This paper describes the development and implementation of a unique new capability to automatically measure the performance of drilling crews, drilling equipment and downtime. The methodology and supporting technology make it possible to carry out a detailed comparison of the performance of equipment and crews across a large number of installations. The system was tested on 8 drilling units (fixed and mobile) in the North Sea. When the data is analysed and plotted as a histogram (frequency against time taken for each task) the information provided gives: The management a process to select the best supplier based on the performance of its equipment when compared to the technical limit of that equipment.The crews the most consistent and efficient way of working (best practice).The management an automated measuring tool to set and follow up the targets for crew performance.A tool that makes ‘hidden down time’ visible, so appropriate actions can be taken to improve performance.A process to see if operations can be eliminated or considerably reduced in time (reaming, circulating, etc). When it comes to crew handling of the equipment the results show a performance improvement of 30%. There are potential time savings of between 40 to 60% in some individual drilling tasks such as slip to slip connection times and weight to weight connection times. This is achieved by performing each operation in a more efficient and consistent way. Optimizing a number of identified KPIs for drilling, including tripping and casing running times, shows a potential saving of between 8 to 15% of the total well construction time. Automated Drilling Performance Measurement (ADPM) has been proven in over 30 wells on 8 rigs in the North Sea. This paper also describes the implementation strategy and approach including the elements of the change management and training that are required to implement this new drilling performance process.
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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".