Kick Risk Forecasting and Evaluating During Drilling Based on Autoregressive Integrated Moving Average Model
Bibliographic record
Abstract
Timely forecasting of the kick risk after a well kick can reduce the waiting time after well shut-in and provide more time for well killing operations. At present, the multiphase flow model is used to simulate and forecast the pit gain and casing pressure. Due to the complexity of downhole conditions, calculation of the multiphase flow model is difficult. In this paper, the time series analysis method is used to excavate the information contained in the time-varying data of pit gain and casing pressure. A forecasting model based on a time series analysis method of pit gain and casing pressure is established to forecast the pit gain and casing pressure after a kick. To divide the kick risk level and achieve the forecasting of the kick risk before and after well shut-in, kick risk analysis plates based on pit gain and casing pressure are established. Three pit gain cases and one casing pressure case are studied, and a comparison between measured data and predicted data shows that the proposed method has high prediction accuracy and repeatability.
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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".