The Impact of Pre-flush Additives on Scale Squeeze Treatments - Application of Modelling and New Test Methodologies to Understand Mechanisms of Retention and Optimise Field Treatment Designs
Bibliographic record
Abstract
Abstract One of the most common methods to prevent scale deposition in the near wellbore area is through the application of squeeze treatments which conventionally consist of pre-flush, main treatment, overflush, shut-in and back production stages. The use of additives such as polyamino acids and polyquaternary amines has often been successfully applied as part of the pre-flush stage of squeeze treatments to improve treatment lifetimes (Chen et al., 2006, Vazquez et al., 2011, Heat et al., 2012). However, although this technology has been successful applied in the field, there is still a lack of understanding of the prevalent retention mechanisms with different scale inhibitors and also a suitable test methodology and modelling techniques to optimize field treatment designs and lifetimes. A new sand pack methodology which provides a better simulation of field squeeze treatments than traditional corefloods has been designed to provide a better understanding of the scale inhibitor retention mechanisms when polyquaternary amines are applied in pre-flush treatments. This has enabled improved treatment modelling and the impact of these additives to be understood in field treatments. The performance of the polyquaternary amine is dependent upon scale inhibitor chemistry and the mechanisms of retention are addressed for both polymeric and phosphonate scale inhibitors. The adsorption isotherms were derived and compared in the absence/presence of the polyquaternary amine using specialized software, and applied to predict squeeze lifetime in field scenarios. This paper provides an understanding on the effects of polyquaternary amines on squeeze lifetime for both phosphonate and polymeric scale inhibitors supported by the application of a newly developed test methodology and computer modelling techniques. In addition, the combination of laboratory and computer modelling data coupled with field experience and a better understanding of the retention mechanisms involved now provides the ability to improve and optimize field squeeze treatment designs with polyquaternary amine pre-flush additives.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".