Qualification of Scale Inhibitors for Conventional and Novel Scale Squeeze Applications to Deepwater Subsea Wells: Laboratory Testing to Field Application
Bibliographic record
Abstract
Abstract Control of inorganic scale such as sulfate and carbonate has been possible via inhibitor chemical treatment or modification of the fluids types (i.e. desulfation of injection fluid). The deployment of scale inhibitor treatments via continual injection downhole and to topside process equipment is a common practice as it batch chemical treatments to production wells via the squeeze process. The paper will outline the inhibitor selection process and field results for three oilfields (1) a conventional squeeze chemical selection study with coreflood and chemical placement evaluation for an application within two south American offshore fields and (2) non conventional batch chemical application for a West African offshore oilfield where scale inhibitor was applied within the fluids used to simulate the well prior to frac pack operation so providing initial scale inhibitor placement prior to initial water breakthrough. This non convention batch treatment eliminated a squeeze to these subsea wells and allowed uninterrupted production of oil until 850,000 bbls to 2,100,000 bbls of produced water was produced from the wells. The delay in applying the first squeeze to these wells allowed the water cut to increase above the relatively low water cut values (<10% BS&W) associated with slow clean up post an aqueous squeeze treatment. This paper will present with the aid of field data from both fields how the challenges of managing scale forced innovation in terms of when to apply scale inhibitor to deepwater subsea wells within the fields. The paper will demonstrate that scale management while being complex can be controlled and treatment programs optimised with the use of varied monitoring methods and the varied skill of the scale management team members.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".