Experimental investigation of gas kick effects on dynamic drilling parameters
Bibliographic record
Abstract
Blowout incidents not only lead to fatalities but also cause loss of assets, expensive clean-up, costly incident investigations and reports, and negative impact on the environment. The 2010 Macondo blowout accident in the Gulf of Mexico was an eye-opener for many oil and gas operators and oilfield service companies; thus, making early kick detection technology research one of the top industry agendas. However, only limited progress has been made in detection technologies that focus on downhole parameters due to the complexity of offshore drilling operations that is increasingly shifting towards the deepwater. Therefore, the current paper experimentally explores downhole drilling parameters for kick indication during drilling. The study utilizes a fully instrumented laboratory scale drilling rig coupled with an air injection and surface monitoring systems. This study observed a sudden jump in bottomhole pressure, increased volume of the return fluid, decreased density of the return fluid, reduced rate of penetration, and increased rotary speed as indicators of kick. The most significant new finding, which is also validated with field reports, is the dampening effects of the drilling vibrations due to kick. Frequency analysis of the axial bit–rock displacements/vibrations confirms the changes of frequencies due to kick induction during drilling. Coupling this important finding with dynamic drilling models, the response of the drilling system at surface (e.g. standpipe, choke pressures, etc.) indicating this change can be predicted.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".