A Statistical Evaluation of Cement Placement Techniques by use of Cement Bond Index
Bibliographic record
Abstract
Abstract Cement placement plays an important role in the primary cementing process. There are several best practices in place which are believed to have significant impact on the quality of the overall cement job. Previous investigations suggest that a combination of multiple placement techniques, such as density and rheology gradient coupled with proper displacement rates, pipe rotation or reciprocation, conditioning of drilling fluid prior to cement job, pipe centralization, and bottom plugs, improves the chances of a successful cement job. However, there is little quantitative analysis available to demonstrate the importance of each technique independently in the field. In the past 15 years, operations in offshore Atlantic Canada have cemented 140-mm and 178-mm liners in 216-mm openhole sections in two different reservoirs. The cementing designs for the liners are similar, especially in terms of flow rate, centralization (type or placement), and spacer train, but differ in pipe rotation, mud conditioning, and bottom plugs. Once the cementation process is executed, it is evaluated by an ultrasonic imaging tool, which measures the acoustic impedance to calculate the cement bond index. The average of the cement bond index for the entire liner is then used to quantify the quality of the cement job for each well. The average cement bond index obtained from 53 wells was used to evaluate various cement placement techniques. The average cement bond index is proportional to the amount of cement bonded to the pipe and is inferred to be proportional to factors related to mud removal and cement placement. Factors that affect mud removal, such as mud conditioning, annular velocity, pipe movement, wellbore characteristics, and the presence of a bottom plug, are investigated. Statistical analysis of the cement bond index indicates that some of these cement placement techniques affect mud removal significantly more than others. A comprehensive analysis of these results and an assessment of potential benefits are presented in this study. The results of the study were used to improve the cement job design.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".