A Case Study: First Field Application of Fully Automated Kick Detection and Control by MPD System in Western Canada
Bibliographic record
Abstract
Abstract Detection and control of gas kicks in OBM/SBM while drilling through narrow pore - fracture windows has always been a challenge due to gas solubility and mud compressibility. Continuous closed loop monitoring of the well and automated early kick detection and control helps to keep the influx volume at a minimum before it reaches the "well control" threshold margin in the kick tolerance matrix. This paper presents a case study and detailed analysis of the event through advanced simulations to examine the benefits of automated influx detection and control using MPD system in comparison to conventional well control method. In the case study, an automated MPD system successfully detected and controlled a gas influx in OBM while drilling onshore Western Canada. The analysis employed dynamic well control simulations to regenerate the event and a close match with the field data was achieved. A sensitivity analysis was then conducted to study the effect of total response time on pressures at the surface and at the casing shoe during the application of conventional Driller’s method of well control. The findings from the study demonstrate how automated early kick detection and control minimizes influx volume and increases operational safety. The implementation of MPD system with such capabilities significantly reduces NPT by enabling influx circulation at full rate and eliminating the need for flow-check, BOP closure and operational delays inherent in conventional well control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".