Use of Cement As Lost Circulation Material - Field Case Studies
Bibliographic record
Abstract
Abstract Cement is one of the most common lost circulation materials (LCMs). Various types of cement have been used as LCMs in the past. Recent developments in cement technology and the understanding of lost circulation have produced custom-designed applications utilizing effective cement types and compositions. Applications also vary depending on the drilling fluid type and its properties. Custom-designed applications include thixotropic and ultrathixotropic cement slurries; slurries containing cello flakes, mica, and CaCO3 for mechanical bridging; unique spacers and surfactant packages; and foamed cement for controlling loss. Selection of proper cement type and injection procedure calls for specific information such as formation properties, wellbore conditions, and thief zone characteristics. Laboratory experiments are recommended in this process. Field observations are also critical in making the final decision for selecting the optimum treatment fluid train and application strategy. This paper discusses the process designs and application of various cement types as LCMs. Solutions to four problematic field cases are provided. The conditions that require cement as an LCM and the criteria for selecting the best cement compositions are outlined along with optimal strategies.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".