Research on Low Density Cementing Technology of Coalbed Methane Well
Bibliographic record
Abstract
The pressure of the coalbed methane (CBM) reservoir is relatively low, there is serious loss circulation, andlow mechanical strength of the coal, which seriously influence the cementing quality. In this paper, according to the characteristics of coal in southern Qinshui basin, an ultra-low density cement slurry system and a foamed cement slurry system are designed and developed to reduce the density of cement slurry by adding lightening admixture and foaming agent through the experimental method. An appropriate bridging type preflush system is also researched and developed. Considering the drilling conditions, the operation technologies are put forward.The ultra-low density cement slurry system has good rheological performance, small API filter loss (≤ 45 mL/ (30 min·6.9 MPa)), excellent sedimentation stability, suitable thickening time and higher early strength. Foamed cement slurry systemhas good rheological performance, excellent sedimentation stability andsuitable thickening time, and can satisfy the compressive strength requirements for the cementing of CBM wells. The fluid loss of the bridging type preflush system is small under the medium or high pressure, withgood rheological performance,andexcellent plugging ability. The ultra-low density cement slurry system and foamed cement slurry system were applied in 14 and 11 CBM wells respectively, and good results have been achieved.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".