Automated MPD and an Engineered Solution: Case Histories from Western Canada
Bibliographic record
Abstract
Abstract Managed pressure drilling allows operators to address key technical risks such as narrow pore pressure and fracture gradient windows, influx management, sensitive wellbore stability environments and navigation of steep and unknown pore pressure ramps. In these applications, MPD may be the determining factor of delivering or not delivering the well. Historically in North America land, operators have executed simplified forms of equipment-centric MPD in well-known basins. However, as unconventional development expands and new basins open up, subsurface pressure regimes present challenges that require a more intense engineered approach to MPD drilling, at least until the field is known. Applying engineering support alone does not guarantee success, though. MPD systems are designed to offer different degrees of pressure management precision and control. Depending on the complexity of the reservoir and geometry of the well design, an MPD solution appropriate for the application is also required. The authors discuss the foundation of MPD automation using as an example, two systems with different degrees of automation control. Each was used for two distinct MPD projects in Western Canada. After determining the technical requirements of each basin, the appropriate MPD system was selected. In both instances, intensive drilling engineering design work was required to ensure the selected system was utilized successfully to drill these difficult wells. Each case unearthed unique lessons that allowed the drilling team to collectively evolve a step-change approach to MPD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".