Improved Method for Underbalanced Perforating With Coiled Tubing in the South China Sea
Bibliographic record
Abstract
Abstract In Malaysia, coiled tubing (CT) conveyance is used to optimize underbalanced perforating, especially for rig-related operations. Well trajectory, temperatures and fluids can create uncertainties on both depth control, and the accuracy of hydrostatic cushion before firing the guns. The conventional method of correlating the CT on depth involves two CT runs the first to run a memory gamma ray (GR) and casing collar locator (CCL) and the second run for the actual perforation. The underbalanced condition calculated based on wellbore fluid displacement is often deemed insufficient to create effective cleanup of the perforations. This paper outlines a solution to these challenges. For a CT perforation campaign in the South China Sea, a CT string equipped with fiber optic cable inside was used, coupled with a bottomhole assembly capable of measuring both bottomhole temperature, internal and external CT pressure, and in addition casing collar locator. The primary objective of the job was to ensure that the perforation was performed with maximum under-balance but not exceeding a safe drawdown on the formation and risking collapse of the perforation tunnels. With 1,000 psi initial underbalance, to remove perforation damage the well would then remain balanced to avoid fluid invasion on the new perforations. The secondary objective was to avoid an additional CT run for correlation, thus saving rig time. The objectives were met and this new approach to coiled tubing operations was found to be effective. Not only was there significant saving of rig time, the wells performed superior to existing wells and were brought into production sooner than planned. This technology has elevated CT standard operation onto a higher level in Malaysia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".