Bibliographic record
Abstract
Abstract Modelling of scale inhibitor squeeze treatments is routinely performed to assist with chemical selection and to optimise treatment design, many examples having been presented in the literature previously. However, the modelling techniques are not always used to best effect, due to lack of experience, time or a methodical procedure for calculating sensitivities. This paper presents a systematic approach to the use of squeeze models that makes use of laboratory data and field experience to assess, simply and effectively, the options for treatment design. Examples are presented that demonstrate the use of such models in aiding the selection of an appropriate inhibitor and the design of the first treatments as part of an integrated scale management philosophy. Very good accuracy in modelling the core flood is usually achieved. While the match between the model prediction and the first squeeze treatment is typically less accurate, history matching of the model parameters based on the first treatment is shown, by means of examples from two North Sea fields, to enable accurate predictions of numerous subsequent treatments in the same formation. The ability to accurately model treatments means that squeeze performance can be predicted with a high degree of confidence, and thus the treatment design may be optimised. This ability to accurately predicted treatment life is critical as wells mature, and the focus on cost per barrel of treated fluid becomes more critical. The most sensitive parameters are shown to be inhibitor type, inhibitor volume and overflush volume, and the paper discusses how they should be optimised to achieve the desired protection while striking a balance with chemical cost and deferred oil 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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".