Modeling of CO2-Hydrate Formation in Geological Reservoirs by Injection of CO2 Gas
Bibliographic record
Abstract
Continuing concern about the impacts of atmospheric carbon dioxide (CO2) on the global climate system provides an impetus for the development of methods for long-term disposal of CO2 produced by industrial and other activities. Investigations of the CO2-hydrate properties indicate the feasibility of geologic sequestration CO2 as gas hydrate and the possibility of coincident CO2 sequestration/CH4 production from natural gas hydrate reservoirs. Numerical studies can provide an integrated understanding of the process mechanisms in predicting the potential and economic viability of CO2 gas sequestration, especially when utilizing realistic geological reservoir characteristics in the models. This study numerically investigates possible sequestration of CO2 as a stable gas hydrate in various reservoir geological formations. As such, this paper extends the applicability of a previously developed model to more realistic and relevant reservoir scenarios. A unified gas hydrate model coupled with a thermal reservoir simulator (CMG STARS) was applied to simulate CO2-hydrate formation in four reservoir geological formations. These reservoirs can be described as follows. The first reservoir (Reservoir I) is similar to tight gas reservoir with mean porosity 0.25 and mean absolute permeability 10mD. The second reservoir (Reservoir II) is similar to a conventional sandstone reservoir with mean porosity 0.25 and mean permeability 20mD. The third reservoir (Reservoir III) is similar to hydrate-free Mallik silt with mean porosity 0.30 and mean permeability 100mD. The fourth reservoir (Reservoir IV) is similar to hydrate-free Mallik sand with mean porosity 0.35 and mean permeability 1000mD. The Mallik gas hydrate bearing formation itself can be described as several layers of variable thickness with permeability variations from 1mDto1000mD, and is addressed as a separate part of this study. This paper describes numerical methodology, model input data selection, and reservoir simulation results, including an enhancement to model the effects of ice formation and decay. The numerical investigation shows that the gas hydrate model effectively captures the spatial and temporal dynamics of CO2-hydrate formation in geological reservoirs by injection of CO2 gas. Practical limitations to CO2-hydrate formation by gas injection are identified and potential improvements to the process are suggested.
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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 | 0.000 |
| 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".