Control of Bottomhole Pressure in Managed Pressure Drilling using IMC Controller
Bibliographic record
Abstract
Abstract In this paper we present a simple proportional integral (PI) with internal model controller (IMC) tuning for kick/loss rejection for managed pressure drilling (MPD) process. Arctic regions are very fragile and hence precaution must be taken to avoid any blow out. MPD is a promising technology for well control. Several complex controllers are available in the literature for controlling MPD but design and implementation of such controllers require the knowledge of advanced controls. It would be beneficial to check the suitability of simple controllers for the control of MPD. We propose a switching PI controller with IMC tuning. In order to synthesis the controller the process between the bottomhole pressure and choke opening was identified. It was observed that the process was highly nonlinear for lower choke openings and hence two separate cases of choke openings were considered for controller synthesis. The synthesized controller was used to reject a kick of 100 lpm and it was observed that performance of the controller was reasonable. We also show that the controller will be able to track the setpoint robustly at different choke openings. The proposed controller requires minimal human interference and it is very simple to implement. We demonstrate that complex advanced controllers will be beneficial from a technical point of view and to meet tough conditions, nevertheless simple controllers can also deliver a reasonable performance.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".