The Evaluation of Enhanced (Carbonate/Sulfate) Scale-Dissolver Treatments for Near-Wellbore Stimulation in Subsea Production Wells, Gulf of Mexico
Bibliographic record
Abstract
Abstract Dissolver technology has been developed and applied with varying degrees of success over the past few years to clean carbonate and the more challenging sulphate/sulphide scales from production tubing and process equipment. The intention of this proposed application was to select a chemical that could be applied into the near wellbore region of the reservoir to recover scale induced damage while not creating any secondary precipitation or negative wettability effects. The impact of dissolver chemical formulation on the reservoir formation's wettability and the potential for mobilization of fines (both reservoir silicate and scale) has not been outlined before. In this paper reservoir condition coreflood studies are outlined where scale is formed within the cores to allow assessment of sulphate dissolver performance. This will form a critical part of the technical evaluation of the dissolver formulation suitability for field application. Results from these coreflood studies will be compared to those generated for the same chemicals in conventional static dissolver studies. The practical concerns about deployment methods within subsea production wells are also emphasized. Specific reference will be made to the impact of metallurgy on chemical selection and the need to reduce corrosion risk within developments which utilize both carbon steel and super duplex requiring the development of higher pH non acid carbonate dissolvers. This paper shows that simple dissolver tests can give misleading performance information as they only show dissolution rates, impact of concentration of active chemical or the increased dissolution possible with solvents. The use of pre scaled reservoir cores to assess the impact of dissolver performance and the potential for secondary formation damage allows the most effective dissolver with minimum formation damage potential to be selected for field application.
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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 | 0.000 |
| 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) | 0.001 | 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".