Bibliographic record
Abstract
Cementing quality can’t meet the requirements in deep and ultra-deep wells cementing because of many factors such as long open hole section, multiple pressure system, high temperature and high pressure. In order to solve the problem of cementing in deep and ultra-deep wells, retarder and fluid loss additive were studied, then the cement slurry system for deep and ultra-deep wells were developed and the performance was evaluated. The results show that the cement slurry has steady performance for high temperature, adjustable thickening time, less fluid loss, good settlement stability, and high compressive strength, all of which can meet the requirement of cementing in deep and ultra-deep wells. Pilot tests of this cement system were conducted in more than 30 wells in Shengli, Dagang, Liaohe, Jiangsu, Jilin and Offshore oilfield, cementing qualities of these wells were qualified, which indicates that comprehensive performance of this kind of cement slurry system can meet the technical requirements for deep and ultra-deep wells cementing, which provides the references for the deep and ultra-deep wells cementing in all of the word. Key words : Deep and ultra-deep wells; Cementing; Fluid loss additive; Retarder; Thickening time
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".