The Impact of Thermodynamic Hydrate Inhibitors (MEG and Methanol) on Scale Dissolver Performance
Bibliographic record
Abstract
Abstract The use of dissolvers to remove scale deposits formed during water production is a relatively common practice for both onshore and offshore hydrocarbon extraction. Acids are commonly used for carbonate deposits, whilst chelant chemistries are deployed for dissolution of sulphate scales. The development of subsea fields presents challenges of deployment of such treatments down flow/test lines, with accurate volumes and movement of solids. In addition, there is the risk of hydrate formation during shut-in of the production wells, which generally means that thermodynamic hydrate inhibitors are applied ahead of the dissolver pill and possibly during the reflow of the spent dissolver pill while the well is heating up to normal operating conditions. This study investigates the impact of two common thermodynamic hydrate inhibitors (methanol and monoethylene glycol or MEG) on the performance of two dissolver chemistries, one organic acid and one chelant. Tests were conducted at 50 °C with no methanol or MEG present, when methanol or MEG was present in the dissolver solution prior to reaction with scale (applied ahead of a dissolver pill), and finally with methanol or MEG applied to the reacted dissolver (simulating thermodynamic hydrate protection during reflow of the well). The results from the study show an unexpected and significant reduction in dissolver performance with methanol or MEG added to the dissolver solution before, and also after reaction with the scale samples. When the hydrate inhibitor was added to the mix at the beginning of the test, the amount of scale dissolved was reduced in every test, with methanol having the biggest effect. The results were similar when the solvent was added to the reacting dissolver when the test was underway. Distinctive differences between MEG and methanol on the performance of organic acid dissolver were observed. Furthermore, different trends in dissolution performance became apparent with the chelant dissolver. The findings from this study clearly show the need for careful design of subsea dissolver applications to prevent reduced chemical performance in the presence of thermodynamic hydrate inhibitors such as methanol and MEG.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".