The Impact of Organic Acid on Scale Inhibitor/Corrosion Inhibitor Interaction, a Case Study from West Africa
Bibliographic record
Abstract
Abstract A development in deepwater off Western Africa has been experiencing incompatibility between the incumbent scale inhibitor and a newly deployed corrosion inhibitor. Initial laboratory testing to determine the amount of scale inhibitor necessary to overcome the interference from the corrosion inhibitor indicated that the effects of the corrosion inhibitor could not be overcome by increasing the dose of the scale inhibitor. Testing of other scale inhibitors indicated that there was not a suitable scale inhibitor that could inhibit scale formation in the presence of the corrosion inhibitor. That initial work was performed using synthetic brines that contained no acetate/acetic acid buffer to help control pH of the system. Further work evaluating the effect of buffering upon scale inhibitor performance employed brine containing an acetate/acetic acid buffer. Once this buffer solution was employed in static bottle testing, it was observed that an alternative scale inhibitor previously tested now worked in the presence of the corrosion inhibitor. While it is known that corrosion inhibitors can impact scale inhibitor performance the impact of organic acid on this interaction has not been published. Since the field where the chemicals were to be applied is known to contain organic acids (upwards of 2,000 ppm), the testing which included the acetate/acetic acid buffer is viewed as a more representative test for the field than the testing that did not contain any organic acids. A field trial of the alternative scale inhibitor and the corrosion inhibitor has been under taken. The paper will present the results from two field trials, the laboratory evaluation to understand the mechanism of interaction and selection of the alternative inhibitor chemical. This paper will shed light on the challenging subject of scale inhibitor and corrosion inhibitor interaction and review the most appropriate test methods/conditions to select non interfering chemicals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".