Managing Formation Damage Risks from Scale-Inhibitor Squeeze Treatments in Deepwater, Subsea Fields in the Campos Basin
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper describes field experience and lessons learned from bullhead deployed scale-control operations in a deepwater subsea development in the Campos Basin, Brazil; specifically, deploying such treatments 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 not only was effective downhole 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. Initial bullheaded scale treatments comprised mutual solvent preflush, a water-based mainflush, and diesel overflush;so-called "hybrid" treatments.As water production rates rose, so did the treatment volumes required.To improve the logistics of these treatments and to mitigate issues arising from poor injectivity of diesel in these wells, core studies were conducted to investigate the option of exchanging the overflush fluid from marine diesel to injection-quality seawater.This change also introduced the possibility of forming a gas-hydrates plug during shut-in, but this was managed using a thermodynamic hydrate inhibitor and by replacing the flowline contents to flashed crude during the shut-in period.The operational aspects and the response of the wells to the modified treatments will both be compared with those previously deployed, in particular, in terms of the injectivity of the wells during treatment and well clean-up rates and productivity afterwards. The core studies also highlighted a formation-damage mechanism caused by incompatibility between mutual solvent and the produced oil, which required modification of the treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it