HPHT Perforation Testing With Cesium Formate and Oil-Based Kill-Pills for North Sea
Bibliographic record
Abstract
Abstract A fluid with loss-control capability (kill pill) is used as the completion fluid during perforating to seal the formation immediately after perforating to maintain well control. This saves the time required to displace completion brine and the cost of losing the expensive brine into the formation. However, perforating with the kill fluid in the wellbore presents a great challenge in minimizing formation damage (Chang, Kageson-Loe, et al. 2004). For wells requiring to be perforated with a static overbalance pressure (SOB) and subsequently shut in with the kill fluid in place (to maintain well control while retrieving the gun and installing the completions system), a properly designed dynamic underbalance pressure (DUB) should be used to surge all perforation skin damage (caused by perforation debris and grain crushing) out of the perforation tunnel and let the wellbore pressure recover back to overbalance rapidly. This sequence allows building of a thin yet effective kill-pill filter cake against the clean perforation tunnel wall (Chang, Mathisen, et al. 2005). Too little DUB could leave the perforations with significant perforation skin remaining, strongly impairing the productivity. Too much DUB has the potential to cause perforation cavities to collapse, which will also strongly impair the productivity. Further, some kill fluids, such as a heavy brine formulated with cesium formate (CsFo) are significantly more costly than other kill fluids, such as an oil-based mud (OBM). Therefore, optimizing the perforating design and choosing the proper kill fluid are crucial tasks to ensure good well productivity (Chang, Kageson-Loe, et al. 2004). This paper discusses how one North Sea operator conducted large-scale perforation and formation damage qualification testing of two perforating kill-pill candidates for use in completing HPHT wells located in the Central North Sea. Test program design, results, and implications are discussed. To the authors’ knowledge, novel information presented includes 16-hour shut-in leak-off responses, flow initiation differential pressure, and return cleanup flow data for kill-pills placed within real perforations (perforations created using a real explosive shaped charge), quantitative and qualitative descriptions (with photos) of resulting CsFo and OBM kill-pill external filter cakes, dye flow visualizations of production flow path into perforations having kill-pill filter cakes, and quantification of single-shot total perforation skin decomposed to effective crushed zone permeability and thickness.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".