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Cited by 573

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Global Distribution of Methane Hydrate in Ocean Sediment

Klauda, Jeffery B., Sandler, Stanley I.·2005·Energy & Fuels
DOI 10.1021/ef049798o
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Abstract

In this paper, we present an equilibrium thermodynamic model to accurately predict the maximum depth of hydrate stability in the seafloor, including the effects of water salinity, hydrate confinement in pores, and the distribution of pore sizes in natural sediments. This model uses sediment type, geothermal gradient, and seafloor depth as input to predict the thickness of the hydrate zone. Using this hydrate model and a mass-transfer description for hydrate formation, we have also developed a predictive method for the occurrence of methane hydrates in the ocean. Based on this information, a prediction for the distribution of methane hydrate in ocean sediment is presented on a 1° latitude by 1° longitude (1° × 1°) global grid. From this detailed prediction, we estimate that there is a total volume of 1.2 × 10 17 m 3 of methane gas (expanded to atmospheric conditions), or, equivalently, 74 400 Gt of CH 4 in ocean hydrates, which is 3 orders of magnitude larger than worldwide conventional natural gas reserves. Of this number, we estimate that 4.4 × 10 16 m 3 of methane expanded to standard temperature and pressure (STP) exists on the continental margins.

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Crossref · 10.1021/ef049798o

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Metadata

Authors
Klauda, J. B., & Sandler, S. I.
Venue
Energy & Fuels
Publisher
American Chemical Society (ACS)
Published
2005-02-03
Volume
19
Issue
2
Pages
459-470
ISSN
  • 0887-0624 (ISSN-L)
  • 1520-5029
DOI
10.1021/ef049798o
OpenAlex
W2092960018
Cited by
573
References
23
FWCI
10.04
Type
journal article
Language
en
Open access
closed

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.