Reverse Circulation of Primary Cementing Jobs—Evaluation and Case History
Bibliographic record
Abstract
Proposal Conventional means of primary cement placement pump the cementing fluids down the casing and well returns are taken from the annulus. This is the most common way of cement placement for the industry and has been used for more than 80 years. Much less commonly used by the industry, but recently gaining in use is the Reverse Circulation of Cement (RCC) technique. When using the RCC technique, the cementing fluids are pumped into the annulus of the well and returns are taken through the casing. The recent acceptance of the RCC technique is mainly driven by economics and state-of-the-art technology bringing an alternative technique. Benefits of the RCC technique can include lowering bottom-hole placement pressure, reducing cement retarder concentration, lowering the time for cement placement, and increasing location safety. The main drawback to the technique is determining when uncontaminated cement is at and around the casing shoe. This paper discusses the benefits and shortcomings of the RCC technique in relation to fluid friction, cement slurry design, location safety, and zonal isolation. The paper illustrates, through a case history, how RCC technique's strengths are obtained while shortcomings are minimized. Field data from a recent job using the RCC technique on a 3100-m gas well in Alberta, Canada, as well as lessons learned from the job, are presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 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".