Solid Polymer Prevents Gas Migration into Cement Slurry in Subzero Conditions
Bibliographic record
Abstract
Abstract Cementing formations with medium to high gas migration severity in cold conditions is considered a very challenging operation for major operators in Turkmenistan. The liquid gas migration agents used in Turkmenistan for the above mentioned severities incur additional operational and technical challenges, particularly during the winter months as the ambient temperature falls below zero. In addition to the climatic challenges, Turkmenistan drilling activity often encounters the additional challenges of high pressures and narrow pore / frac windows. This paper focuses on case studies to analyze the application of both liquid and solid gas migration prevention additives compared with conventional slurry systems in overcoming challenges encountered while cementing across gas bearing formations. To address such challenges, a solid polymer gas migration prevention additive has been field tested successfully in environments such as Canada, USA and Russia having similar conditions as Turkmenistan. A description is then given of a successful method of combining a special solid polymer based gas migration prevention system with good cementing practices, in order to not only achieve the primary objective of the cementing operation, but also yield improved logistics in terms of storage, handling, and no associated mix fluid aging or detrimental effect of thawing. Field case histories are presented, illustrating the versatility of the system in solving gas migration problems in seasonal climate changes. Above all, solid gas migration prevention polymers are considered the best cost effective solution compared to the liquid gas migration agents of the same family which is considered as the key value for commercial oil and gas production.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".