Scale Inhibitor Squeeze Treatments Deployed From an FPSO in a Deepwater, Subsea Fields in the Campos Basin
Bibliographic record
Abstract
Summary This paper describes field experience and lessons learned from scale control operations in a deepwater subsea development in the Campos Basin, Brazil; specifically, from bullheading scale-inhibitor squeezes from the FPSO host, along the production flowlines, into four low-watercut, horizontal subsea wells, completed with sand control. The relatively small number of high-cost, highly productive wells, coupled with a very high barium-sulfate scaling tendency upon breakthrough of injection water, meant that not only was effective scale management critical to achieve high hydrocarbon recovery, but even wells at low water cuts were deemed to be at sufficient risk to require squeeze application. Use of conventional, water based squeezes have been known to cause significant damage to productivity in low-watercut wells, including those showing a fines-migration tendency, as was the case here. Hence, on the basis of risk mitigation, supported by an extensive program of laboratory testing, it was decided that for the initial treatments, only the mainflush would be water based, with a mutual-solvent preflush and marine-diesel overflush. Other key challenges associated with treating from the host included the remote location of the wells, the potential to form hydrates, the cleanliness of the lines along which the treatment would pass, the achievement of effective placement over a long producing interval, as well as the need to deploy the chemical package via a support vessel adjacent to the FPSO. All had to be managed because of the high cost and low availability of a deepwater rig that could deploy the treatments directly to the subsea wellheads. This paper will explore in detail the issues associated with inhibitor-squeeze deployment in deepwater, subsea fields, many of which are currently being developed in the Campos basin, Gulf of Mexico, and West Africa, and are a good example of best-practice sharing from another oil basin.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".