Early Detection and Estimation of Kick in Managed Pressure Drilling
Bibliographic record
Abstract
Summary Drilling in the offshore environment involves high risk, mainly caused by uncertainties in the reservoir conditions. Unplanned events such as the influx of reservoir fluids (i.e., kick) can lead to catastrophic accidents. Therefore, mitigation of kick is extremely crucial to enhance the safety and efficiency of drilling. In the current study, an unscented-Kalman-filter (UKF)-based estimator is used to simultaneously estimate the bit-flow rate and kick in a managed-pressure-drilling (MPD) system. The proposed estimator uses sigma-point transformations to determine the true mean and covariance of the Gaussian random variable and captures the posterior mean and covariance accurately up to the third order (Taylor-series expansion) for any nonlinearity. In the proposed UKF formulation, hidden states and unknown inputs were concatenated to an augmented state vector. The magnitude of the kick is estimated using only available topside measurements. The applied method was validated by estimating the gas-kick magnitude in a laboratory-scale setup and data set from a field operation. The proposed estimation method was found robust for the MPD system under different noisy scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".