Evaluation of the Scaling Resistance of Different Coating and Material for Thermal Operations
Bibliographic record
Abstract
Abstract Several alloys and coating techniques have been used by industry for their anti-corrosion and anti-fouling properties in the industry. One of the major problems in thermal operation is related to silica and calcium carbonate scale. In this study, we intend to better understanding the relative scaling resistance performance of different coatings and alloys exposed to typical formation water in thermal operations. This paper provides a study on failed samples collected from various projects in Western Canada. Moreover, a review of research work on scaling properties of different materials in thermal applications will be presented. Different failed screens were collected from various projects in Western Canada. Thin section analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM) joined with energy dispersive X-ray spectroscopy (EDX) were performed on collected failed pipeline samples to determine the composition of the scale material. Obtained results revealed that the main scaling materials are silicates and carbonates. Chert, clays and carbonates act as cement to bind sand grains (mainly quartz). Later, a review was performed on an ongoing investigation regarding the materials and coatings for improving the anti-scaling properties. Bulk scaling tests, Atomic Force Microscopy (AFM), and in-situ field trials were used to investigate the anti-scaling properties of two RGL proprietary grade materials, proRC05, and proRS06, as well as electroless nickel (EN) coating. Carbon steel L80, carbon steel 4140 and EN30B alloy steel were used for comparison. The microstructural change of the material surface was studied using complementary techniques (e.g., XRD, SEM, and EDX). The tests have been performed under a complex chemical environment that represents the chemistry of the near screen condition in thermal operation, to assess the relative performance of different coatings. Among proRC05, proRS06, 4140 carbon steel and EN30B alloy steel, the anti-scaling performance follows the order of proRC05 > proRS06 > 4140 carbon steel > EN30B alloy steel. Comparison between EN-coated and uncoated samples shows that the EN-coated carbon steel L80 provides better anti-corrosion and fouling resistance performance with a small amount of iron oxides and silica foulants. Field trials of EN-coated technology have been also proven to be effective. This work provides a detailed review on recent attempts on evaluating the anti-scaling properties of various materials and coatings to minimize the silica and calcium carbonate scale. Furthermore, field trials were reviewed for evaluating the scaling and corrosion properties in thermal production. The results of this study will help engineers select material for projects in which silica and calcium carbonate scaling could be a significant issue.
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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 | 0.001 | 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".