Management of Cross-Flow Events in High pressure, Deep Disposal Wells Using MPD Techniques
Bibliographic record
Abstract
Abstract Drilling wells with Managed Pressure Drilling (MPD) technology allows Operators to drill and complete wells that would be otherwise un-drillable or uneconomical when done conventionally. The use of a mud density below the pore pressure can significantly reduce the likelihood of partial or complete loss of circulation especially when drilling through possible fracture or weak zones for Deep Disposal well projects. While drilling a Deep Disposal well, pore pressure was determined and confirmed through fingerprinting with the use of surface-applied pressure on connections. Drilling deeper into a different formation, total loss of circulation was experienced. The loss of mud in this scenario, without returns to the surface, can cause the fluid level to drop over time leading to a potential wellbore influx, that could result in the influx reaching surface. However, due to cross-flow between the high pressure zone and the loss zone, gas from the high pressure zone was not seen on surface. Managing these losses involved the successful placement of Loss Circulation Material (LCM) to re-instate circulation and the use of Managed Pressure Cementing procedures to set cement plugs to heal the loss. This paper delves into this unique instance of how MPD techniques were utilized to manage this cross-flow event in the Deep Disposal well in Alberta while highlighting the safety considerations employed when deciding between changing wellbore fluid to Brine or maintaining Invert as wellbore fluid.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".