Modeling of Ice Formation in Gas Hydrate Reservoirs
Bibliographic record
Abstract
Summary A number of numerical simulation studies of gas hydrate reservoirs have indicated that the pressure reduction method known as depressurization is a promising technique to produce gas from hydrate reservoirs. In some cases, severe ice formation has been observed, leading to plugging and termination of gas production. Some researchers have suggested that if the flowing bottomhole pressure is not lowered beyond the equilibrium pressure corresponding to the freezing point of water, then ice formation may be avoided. This argument is based on the premise that the lowest temperature would occur at the wellbore. If temperature can be controlled to above zero by controlling the bottomhole pressure, then freezing should not occur. The objective of this work is to explore under what conditions ice particles form. Various cooling mechanism (cooling because of decomposition, gas expansion, etc) are studied in detail. For this purpose, a 3D mathematical model for gas production from hydrate reservoirs is introduced which incorporates energy balance, fluid flow and kinetics of the hydrate decomposition along with the ability to predict the formation of ice particles. This model is developed by modifying the GPRS (General Purpose Reservoir Simulator) platform to account for a number of mechanisms including hydrate decomposition and ice formation. GPRS is an object oriented reservoir simulator code developed at Stanford University. We will then apply this simulator to model large-scale hydrate decomposition process in porous media, and demonstrate the effect of ice formation on gas production behavior. Through some case studies we investigate the conditions in which ice forms and becomes an issue. The learning for these studied are then used to suggest practical ways of avoiding ice formation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".