Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 170152, ’The Successful Application of Automated Managed-Pressure Drilling To Protect Caprock Integrity by Narrow-Margin Drilling in SAGD Wells,’ by Nadine Osayande, Elvin Mammadov, and Sheldon Sephton, Weatherford Canada, and Vincent Boucher, Suncor Energy, prepared for the 2014 SPE Heavy Oil Conference—Canada, Calgary, 10-12 June. The paper has not been peer reviewed. This paper presents a drilling solution through application of an automated managed-pressure-drilling (MPD) technique proved to identify and react to actual wellbore pressures and detect and control gains and losses while still having the ability to maintain a constant bottomhole pressure (BHP) while drilling through tight windows. The paper demonstrates the successful application of advanced automated MPD technologies on the Dover well close to Fort McMurray, Alberta, Canada. Introduction A well in the Dover field had multiple failures in the liner that resulted in excessive sand production, causing the well to be shut in. After reviewing the options of well repair or redrilling the horizontal section to install a new slotted liner, it was determined that redrilling was the best option. After the well-schematic analysis and in collaboration with the operator, the combination of a proprietary control system and MPD techniques was recommended along with a water-based mud (WBM) weight to drill the well and still be able to maintain the BHP required to overbalance the formation. The capability to detect microinfluxes/-losses while drilling, combined with an automated control system covering the drilling parameters [including the surface backpressure (SBP) and constant BHP], allowed the well to be drilled while maintaining the downhole pressure values as required. This approach additionally mitigated the challenges with weighted-WBM systems. For this particular well, reservoir temperature had cooled down to 145°C. If the well was not circulated for a period of time during drilling operation, mud temperature would increase, which would lead to the breakdown of the polymers and the loss of suspension of weighting material, effectively reducing the density to values near water density, thus creating an underbalanced situation. Redrilling a producer well in a shallow, overpressured, hot reservoir was a first for the operator in this area. Typically, during any completion workover, full fluid/mud losses occur, so the potential of these to occur during drilling operations was considered high.
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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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".