Numerical Studies of Gas Hydrate Formation and Decomposition in a Geological Reservoir
Bibliographic record
Abstract
Numerical modeling of gas hydrates can provide an integrated understanding of the various process mechanisms controlling methane (CH4) production from hydrates and carbon dioxide (CO2) sequestration as a gas hydrate in geologic reservoirs. This work describes a new unified kinetic model which, when coupled with a compositional thermal reservoir simulator, can simulate the dynamics of CH4 and CO2 hydrate formation and decomposition in a geological formation. The kinetic model contains two mass transfer equations: one equation converts gas and water into hydrate and the other equation decomposes hydrate into gas and water. The model structure and parameters were investigated in comparison with a previously published model. The proposed kinetic model was evaluated in two case studies. Case 1 considers a single well within a natural hydrate reservoir for studying the kinetics of CH4 and CO2 hydrate decomposition and formation. A close agreement was achieved between the present numerical simulations and results reported by Hong and Pooladi-Darvish (2003, “A Numerical Study on Gas Production From Formations Containing Gas Hydrates,” Petroleum Society’s Canadian International Petroleum Conference, Calgary, AB, Jun. 10–12, Paper No. 2003-060). Case 2 considers multiple wells within a natural hydrate reservoir for studying the unified kinetic model to demonstrate the feasibility of CO2 sequestration in a natural hydrate reservoir with potential enhancement of CH4 recovery. The model will be applied in future field-scale simulations to predict the dynamics of gas hydrate formation and decomposition processes in actual geological reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".