Investigating the Effect of Carbon Dioxide Concentration on Hydrate Formation Risk from Water Alternating Gas (WAG) Changeover Operations
Bibliographic record
Abstract
Abstract Hydrate formation is a flow assurance challenge for offshore oil and gas operations with subsea pipelines, wells, and tiebacks. In Water-Alternating-Gas (WAG) operations, hydrates can form within the injection wells when switching from water-to-gas and vice versa. This study investigates hydrate formation in a WAG injection well under water-to-gas and gas-to-water changeover operations. Compositional changes, temperature, and required thermodynamic inhibitor are evaluated within the injector well where hydrate formation is likely. The simulation study is conducted on a representative offshore field at a seabed depth of 124 m and temperature of 3ºC. The dynamic multiphase flow simulator was used for the WAG simulation and fluid modeling. The subcooling is evaluated to detect potential hydrate formation. After determining the hydrate risk zones for water-to-gas and gas-to-water operations through detecting the regions with positive values of subcooling where the fluids can be exposed to hydrate formation, the effects of gas composition (CO2 content) change, and methanol injection on the subcooling profile are evaluated. Simulation results indicated a higher risk of hydrate formation after the start of water injection in gas-to-water during an offshore injection well changeover operation due to slower fluid displacement. In both cases, after starting the injection operation the subcooling is reduced significantly for the entire well. However, in the water-to-gas changeover, the sections of the well that had water and gas were outside the hydrate formation region after 1 hour of gas injection. For a water injection rate of 2,300 m3/day, 1 MSm3/d of gas was adequate to displace the entire water column in the well into the reservoir in the water-to-gas changeover operation. For gas-to-water changeover operation, full displacement of the gas occurred after 11 hours and 9 hours for the base natural gas case and the natural water with NG (CO2 44 wt%) case, respectively. Methanol slug injection (5 m3) at the end of the water injection inhibited hydrate formation for the entire length of the well. Fluid model simulations indicate that changing the CO2 composition (5-44 wt%) has a noticeable effect on the phase envelope and shifts the hydrate curve up to 2ºC. Few previous studies have investigated WAG changeover operations with the effect of CO2 and methanol concentrations on hydrate formation. One study found hydrate formation risk in water-to-gas operations based on onshore well with no attention to the impact of thermodynamic inhibitors and gas composition. This study investigates the hydrate formation risk, the impact of natural gas (NG) composition (CO2, 5-44 wt%), and the applicability of methanol in WAG changeover operations in an offshore well.
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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 | 0.001 | 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".