CASE Study: Enhancing CBL Quality through Emphasizing on Cementing Best Practice and Expanding Agent System
Bibliographic record
Abstract
Abstract Zonal isolation is an important thing to acquire good cement integrity. One of many ways to evaluate this is by running Cement Bond Log (CBL). Well condition prior, during, and after cement placement contribute high impact on the result of CBL. Aside from that, temperature and pressure changes also give significant outcome to CBL result. In this paper, some improvements wereapplied and the logging results showed significant impact on zonal isolation compare to previous well. Objectives of this paper include improvement applied on case studies to obtain good zonal isolation and no remedial cementing required, emphasizing on applied cementing best practice recommendation, and introducing expanding agent in order to recover micro annulus. Well integrity is a vital element to have long well life cycle. The paper describes the enhancementfrom poor zone isolation in previous well to be better in next well and this became one of best practices of cementing design and execution for Operator. Improvement in mud removal was done by adding spacer volume with high concentration of turbulent spacer. Cement slurry had expanding agent in the system and smart retarder which provided better compressive strength. Improvements in cement system were seen in faster compressive strength build up and ability to recover micro annulus. Linear expansion result from expanding test is 80 μm in 7 days, which is sufficientto cover micro annulus that has happened before in previous well. The designed slurry was also supported by using more centralizers in execution. Quality check was done by measuring rheology of drilling mud and spacer. Cementing job was executed with no issues and following job program. Evaluation of cement job supported with the playback pressure data. Result of CBL result showed that good bonding was achieved on upper and lower side of interest zone.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".