Evaluating Human-Machine Interaction for Automated Drilling Systems
Bibliographic record
Abstract
Abstract The efficient utilization of automation systems necessitates a clear understanding of the interaction of the human operator, the automation system and any automated routines being run. If automated routines perform actions not desirable to the human operator, time is lost as the routine is interrupted and human control re-engaged. In addition, automatic handoff back to the human operator, both due to human intervention and due to exist conditions or anomalies must also be managed. Activity data from rigs across North America is analyzed to understand automation process utilization and interrupt timing. Realtime and historic data is tagged, either automatically, semi-automatically using machine learning, or manually, to create a minute-by-minute timeline of rig operations. Operations are then classified both by operation – steering, reaming, making hole, etc. – and well plan to understand how operational demands change automation system utilization. This results in a new set of metrics which can be used to precisely quantify the performance metrics of both the human and automated drilling systems. Performance of the automation system is found to be a strong function of hole deviation with the system outperforming during simple operations and in the vertical hole, but with reduced performance while in the curve and horizontal, due to high interruption of certain tasks. It is found that standard performance metrics, such as slip to slip or weight to weight are affected by standard practices and if these are used to grade system performance, these practices must be account for. This paper presents a detailed investigation of the interaction of the driller with an automated drilling automation system and lays out the utilization of the automation system as a function of rig operations and well path. It is specially noted that standard performance metrics must consider standard practices which may differ between operations.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".