Evaluation and Field Optimisation of Kinetic Hydrate Inhibitors for Application Within MEG Recovery Units, Gas Condensate Field, Mediterranean Sea
Bibliographic record
Abstract
Abstract The most common chemical treatment for hydrate formation is the use of thermodynamic hydrate inhibitors (THI), which act to shift hydrate equilibrium conditions toward higher pressures and lower temperatures. On systems where a THI is needed on a continuous basis, regeneration systems have been developed to lower the cost. The most common regeneration systems are methanol recovery and MEG recoveryunits (MRU). While these recovery units can lower the cost of a THI treatment program, the large volume of THI needed for most operations remains a major problem for many operators. A new method to prevent hydrate formation that can significantly reduce the amount of THI needed is using a Kinetic Hydrate Inhibitor (KHI) in combination with a THI. This paper will discuss the laboratory evaluation of KHI chemicals, deployment of KHI within a MEGstream and optimization of a combination of MEG/KHI when applied to an offshore gas/condensate field in the Mediterranean Sea. The objective of the trial was to apply the selected KHI and reduce the MEG injection rate as far as the MEG injection system would allow. The operator imposed a 50% reduction in MEG rate as a target for a successful trial of the KHI chemical. During the trial it was possible to pass the initial target reduction of MEG and in fact a reduction in the MEG rate by 70% was recorded. During the trial unplanned shut downs (duration 3 days) occurred but even under these conditions the KHI was still effective. The lessons learned during the selection and field optimization will be presented along with the economic impact that the reduction in MEG has made to this operation. The implications of these findings will be outlined in terms of impact on Capex projects such as the footprint/capacity of the handling/storage and recovery units. The impact that KHI application can have during the OPEX phase where it will also be shown such as de-bottleneck existing systems where salt loading and/or higher than expected produced water volumes are experienced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".