The Challenge of Modelling and Deploying Divertion for Subsea Scale Squeeze Application
Bibliographic record
Abstract
Abstract Due to the increased cost of scale management in subsea compared to platform or onshore fields, and because of the more limited opportunities for interventions, it is becoming increasingly important to carry out a risk analysis process for scale management as early as possible in the field development plan. A critical part of this process is to evaluate methods of chemical deployment for reservoirs where near wellbore scale has been identified as a significant risk to production – often leading to consideration of the scale squeeze process. This paper discusses how scale squeeze treatment deployment options can be modelled and demonstrates the comparison of mechanical and chemical diversion (particulate or viscosified fluids) with simple rate diversion. In subsea heterogeneous wells diversion via bullhead deployable treatments can be more cost effective than deployment via a rig and coil tubing, provided the treatment distribution is as effective. The ability to model the application process is critical in the economic assessment of coil/rig vs. fix facility deployment in deepwater fields. The paper will outline the process of chemical selection, reservoir/near wellbore modeling and field application for solid, viscosified divertors or deployment options where high pump rates are utilized to achieved better chemical placement. Field treatments where this process has been utilized (North Sea, Brazil and West Africa) will be presented along with the results of these treatments. Practical issues related to overcoming the challenges of subsea flow line cleaning and the effective rates required to achieve diversion are discussed, as are monitoring methods following such treatments to ensure effective placement has been achieved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".