Measurements of Thermodynamic Hydrate Inhibitor Methanol Partitioning in Methane at High Pressures
Bibliographic record
Abstract
Methanol is one of the most common thermodynamic hydrate inhibitors (THIs) for offshore oil and gas production lines. Even with its widespread use, there are few data on methanol partitioning in the hydrocarbon phase at equilibrium due to the complexity of the measurements. Highly accurate data of methanol partitioning in hydrocarbons are crucial for various reasons. First, these data are needed to ensure that methanol is present in adequate amounts to be effective as a thermodynamic hydrate inhibitor. Methanol is effective at preventing hydrate formation only when it is present in the aqueous phase. Next, methanol portioning data are also needed to prevent the excessive injection of methanol in the flowline. Excess methanol in the flowline could cause corrosion to the pipeline and thus, reduce the integrity of the pipeline. Finally, the data are also needed to avoid large downstream processing issues. Specifically, oil/gas that is sent for processing needs to meet the downstream methanol specification. Contractors may be fined if the concentration of methanol is outside this specification. In this work, we performed measurements of the methanol portioning in the vapor methane phase using two different experimental apparatuses (high-pressure equilibrium flow and microfluidic). Each apparatus has its own advantages. The microfluidic system allows visual observation, and thus the first precipitation of methanol can be observed. However, the apparatus has a pressure limit of ∼11 MPa. On the other hand, the high-pressure equilibria flow system has a high-pressure limit (up to 34.5 MPa). This allows measurements to be performed at a high pressure, where currently there are limited reported values. In this work, methanol portioning measurements were conducted at various concentrations for methanol (90, 95, and 97 vol % methanol), a temperature of 40 °C, and pressures up to 34.5 MPa. In the microfluidic system, equilibrium compositions of methane, water, and methanol systems were measured at first liquid appearance using gas chromatography–mass spectrometry (GCMS). Results of these measurements showed that the compositions of methane, water, and methanol are consistent with the predicted values from simulation. Additionally, further analysis showed that in the vapor phase, the normalized methanol concentrations were consistent with the data reported by McGlashan and Williamson at both of the investigated experimental pressures. Similar investigations were also conducted in a high-pressure equilibrium flow system. Results of our measurements using the high-pressure equilibrium flow system showed that the mole fractions of water and methanol in the vapor phase were slightly higher than the predictions from simulation. However, the normalized methanol concentrations were consistent with the data reported by McGlashan and Williamson.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".