Prediction of Reservoir-Kick Effect and Its Management in the Managed-Pressure-Drilling Operation
Bibliographic record
Abstract
Summary The sudden influx of reservoir fluids (i.e., reservoir kick) into the drilling annulus is one of the common abnormal events encountered in drilling operations. A kick can lead to a blowout, causing loss of lives, assets, and damage to the environment. This study presents a framework for real-time kick monitoring and management in managed-pressure-drilling (MPD) operation. The proposed framework consists of three distinct steps: the unscented Kalman filter (UKF) is used to detect and estimate the kick's severity; the estimated kick size and optimal control theory are used to calculate the time to mitigate the kick in the best-case scenario; and on the basis of the total predicted influx and pressure rise, the monitoring system generates a warning and activates the mitigation strategy. Thus, the proposed method can estimate, monitor, and manage kick in real time, enhancing the safety and efficiency of the MPD operation. The developed method was validated and demonstrated using a simulated MPD system, a pilot-scale experimental setup, and field data collected from an MPD operation in western Canada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".