MétaCan
Menu
Back to cohort
Record W2012444104 · doi:10.2118/2007-204

CO2 Hydrate Formation in Geological Reservoirs by Injection of CO2 Gas

2007· article· en· W2012444104 on OpenAlexaffabout
M. Uddin, Dennis Coombe

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsSocial Sciences and Humanities Research Council
Fundersnot available
KeywordsClathrate hydrateHydrateNatural gasMethanePetroleum engineeringPermafrostGeologyOverburdenShoreSaturation (graph theory)Carbon dioxidePermeability (electromagnetism)PorosityPorous mediumReservoir engineeringMineralogyPetroleumGeotechnical engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Gas hydrates are a significant resource of natural gas existing both on-shore buried under the permafrost and off-shore buried under oceanic and deep lake sediments. Recent investigations consider the possibility of sequestering carbon dioxide (CO2), a greenhouse gas (GHG), in gas hydrate reservoirs and at the same time recovering the methane (CH4) from the hydrates. Numerical studies can provide an integrated understanding of the process mechanisms in predicting the potential and economic viability of CH4 gas production and CO2 gas sequestration in a geological reservoir. This study numerically investigates possible sequestration of CO2 as a stable gas hydrate in reservoir geological formations. A unified gas hydrate model coupled with a thermal reservoir simulator (CMG STARS) was applied to simulate CO2 hydrate formation in three 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 10 mD. The second reservoir (reservoir II) is similar to a conventional sandstone reservoir with mean porosity 0.25 and mean permeability 20 mD. The third reservoir (reservoir III) is similar to hydrate-free Mallik silt with mean porosity 0.30 and mean permeability 100 mD. The fourth reservoir (reservoir IV) is similar to hydrate-free Mallik sand with mean porosity 0.35 and mean permeability 1000 mD. The Mallik gas hydrate bearing formation can be described as several layers of variable thickness with permeability varied from 1 mD to 1000 mD. 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. Introduction Background – Gas hydrates are ice-like solids composed of gas molecules and water. Gas hydrates form when relatively small guest molecules (such as carbon dioxide (CO2) and methane (CH4)) come into contact with water under low temperature, high-pressure conditions, both above and below the freezing point of water. Depending on the types of gas present, several crystal structures of gas hydrate are known to occur (such as Structure I, Structure II and Structure H), each with different physical and stability properties 1,2. In natural environments, the pressure-temperature conditions favouring gas hydrate formation could occur offshore in shallow depths below the ocean floor and onshore beneath the permafrost. Areas offshore of Canada's west coast and a number of onshore Arctic locations are know to contain some of the most concentrated CH4 hydrate deposits in the world. One large CH4 hydrate reserve is located in the Mallik field, Mackenzie Delta on the coast of the Beaufort Sea, in Canada's Northwest Territories. In addition, geological reservoirs with favorable pressure – temperature conditions for CO2 hydrate formation exist offshore of Canada's east and west coasts widespread in num

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.014
GPT teacher head0.225
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueCanadian International Petroleum ConferenceSame topicMethane Hydrates and Related PhenomenaFrench-language works237,207