Thermal properties of highly saturated methane hydrate-bearing sediments recovered from the Krishna–Godavari Basin

Accepted Manuscript Thermal properties of highly saturated methane hydrate-bearing sediments recovered from the Krishna–Godavari Basin Michihiro Muraoka, Michika Ohtake, Naoko Susuki, Hiromitsu Morita, Motoi Oshima, Yoshitaka Yamamoto PII:

S0264-8172(18)30443-4

DOI:

https://doi.org/10.1016/j.marpetgeo.2018.10.037

Reference:

JMPG 3549

To appear in:

Marine and Petroleum Geology

Received Date: 23 February 2018 Revised Date:

15 October 2018

Accepted Date: 22 October 2018

Please cite this article as: Muraoka, M., Ohtake, M., Susuki, N., Morita, H., Oshima, M., Yamamoto, Y., Thermal properties of highly saturated methane hydrate-bearing sediments recovered from the Krishna–Godavari Basin, Marine and Petroleum Geology (2018), doi: https://doi.org/10.1016/ j.marpetgeo.2018.10.037. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Thermal properties of highly saturated methane hydrate-bearing sediments recovered from the

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Krishna–Godavari Basin

3 Michihiro Muraoka a,*, Michika Ohtake a, Naoko Susuki a, Hiromitsu Morita a, Motoi Oshima

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b

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,Yoshitaka Yamamoto a

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a

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Technology (AIST), Onogawa, Tsukuba, Ibaraki 305-8569, Japan.

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b

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Research Institute of Energy Frontier, National Institute of Advanced Industrial Science and

Methane Hydrate Production Technology Research Group, Research Institute of Energy Frontier,

National Institute of Advanced Industrial Science and Technology (AIST), Sapporo, 2-17-2-1

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Tsukisamu-Higashi, Toyohira-Ku, Sapporo 062-8517, Japan

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E-mail: [email protected]; Fax: + 81 29 861 8765; TEL: +81 29 861 2841

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Highlights

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Thermal properties of sediments from the Krishna–Godavari Basin were measured.

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Highly saturated methane hydrate-bearing sediments thermal constants were reported.

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The measurements were performed using the single-sided TPS method.

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The distribution model is valid from the low to high MH saturation range.

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The measured thermal conductivity is independent of the MH saturation.

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Abstract

We measured the thermal constants (including conductivity, specific heat, and thermal

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diffusivity) of methane hydrate (MH)–bearing sediment samples recovered in 2015 from the

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Krishna–Godavari Basin, in India. These samples were recovered through the National Gas Hydrate

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Program Expedition 02 (NGHP-02). The measurements were performed using the single-sided

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Transient Plane Source (TPS) method. To investigate the influence of the sediment composition on

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the thermal properties of MH-bearing sediments, the thermal constants of MH-bearing sediments

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were measured at 5° C and 10 MPa over a porosity (ϕ) range of 41 % ≤ ϕ ≤ 51 % and MH saturation

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(Sh) range of 17 % ≤ Sh ≤ 74 %. In addition, density and mineral compositional measurements of the

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dry sediment grain samples were conducted.

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The measured thermal conductivity slightly decreases with increasing ϕ and is

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independent of Sh. The measured specific heat increased with increasing ϕ, whereas it

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decreased with increasing Sh. The measured thermal diffusivity decreased with

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increasing ϕ, whereas it increased with increasing Sh. Various models were used to

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estimate the thermal constants to examine the applicability of these models to natural

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MH-bearing sediments. The distribution model (using a geometric mean model) is valid

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from the low to high MH saturations; however, the thermal properties of clay-rich layers

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are not likely reproducible by the distribution model from the mineral compositions.

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This indicates that the low value of the observed thermal conductivity in the clay-rich

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samples is likely a product of the sediment’s small grain size.

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Keywords: methane hydrate, hot disk, transient plane source, Krishna–Godavari Basin, thermal

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properties, mineral composition

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1. Introduction

Methane gas hydrates in suboceanic sediments and permafrost regions are of significant interest

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due to their use as future energy resources, their potential as geo-hazards (Kvenvolden, 1999), their

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role in the global carbon cycle (Kvenvolden, 2002), and their potential to affect climate change

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(Hatzikiriakos, 1993). In March 2013, the first offshore gas production test on methane hydrate

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(MH)–bearing sediments in the Nankai Trough area, Japan, was conducted using a depressurization

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method (Yamamoto, 2015). From 12 May 2017 to 13 June 2017, the second set of offshore gas

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production tests were conducted in the same area.

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Depressurization-induced gas production requires heat from surrounding sediment because MH

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dissociation is an endothermic reaction; therefore, the thermal properties of MH-bearing sediments

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are imperative to develop gas production technologies. There are only a few published articles

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reporting the thermal conductivity of MH-bearing sediments, and most of them used artificially

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synthesized MH-bearing sediments in the lab (Huang et al., 2005; Muraoka et al., 2015; Waite et al.,

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2002; Yang et al., 2016, 2015). Waite et al. (2002) reported that the in situ thermal properties of the

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MH-bearing sediments are dependent on the in situ pore pressure and temperature conditions.

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Henninges et al. (2005) indirectly estimated the thermal conductivity of gas hydrate-bearing

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sediments from geothermal data and petrophysical models: they suggested that the geometric mean

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model, which gives the apparent thermal conductivity for a random distribution in a two-component

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system (Woodside and Messmer, 1961), is suitable for estimating the thermal conductivity of gas

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hydrate-bearing sediments. Muraoka et al. (2014) measured MH-bearing sediment cores recovered

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in 2004 from the Nankai trough area, and, assuming a simple thermophysical model, concluded that

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the distribution model (i.e., geometric mean model, Woodside and Messmer, 1961) is suitable for

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estimating the thermal conductivity of hydrate-bearing sediments comprised of sediment grain, water,

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and MH.

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The reported saturations of MH in the cores recovered from the Nankai trough were low; the

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maximum was only 15%. The reason for the low MH saturations is that the technology for

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recovering cores was incomplete at the time. In 2004, MH-bearing sediment cores were recovered

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from the Nankai Trough via a pressure–temperature core sampler (PTCS) (Fujii et al., 2008). These

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samples were rapidly depressurized on the ship and were frozen using liquid nitrogen to prevent MH

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dissociation. Jin et al. (2014) documented fractures formed during liquid nitrogen questing operation.

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In our sampling, we selected fracture-less sediment pieces. Pressure core technology is important

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because if MH dissociates, the core sediment structure is disturbed. Over the last decade, several

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types of pressure coring tools and recovered core transfer systems have been developed (Yamamoto

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et al., 2014; Schultheiss, 2009) The “Pressure Coring Tool with Ball valve” (PCBT) pressure core

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system can be used in situ to maintain the core sediments of samples. Pressure cores used in this

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study were transported to the laboratory under gas hydrate stable pressures and temperatures (near in

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situ conditions) to avoid the dissociation of the methane gas hydrate. The pressure core handling and

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processing techniques applied were developed by the National Institute of Advanced Industrial

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Science and Technology (AIST) (Yoneda et al. 2015a, b; Jin et al., 2016). Yoneda et al. (2015 b)

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reported the hydrate saturation in cores tested in this study to be up to 79% in the cores recovered

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with the PCBT pressure core system.

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In this paper, we report the measurement results of the thermal properties of MH-bearing

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sediment cores recovered from the eastern coast of India. The India National Gas Hydrate Program

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Expedition 02 (NGHP-02) was conducted from 3 March 2015 to 28 July 2015 off the eastern coast

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of India using the deep water drilling vessel Chikyu (Kumar et al., 2016; Collett et al., this volume;

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Kumar et al., this volume). The goal of this expedition was to explore highly saturated MH-bearing

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sediment reservoirs that would become targets for future production tests. The NGHP-02

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pre-expedition drill site review included more than 80 sites in various Indian offshore basins. Of

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these, 25 sites from the Krishna–Godavari basin were selected as candidate test sites for NGHP-02.

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The location map of these wells is shown in Figure 2 in Kumar et al. (2016). The water depth of the

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wells ranged from 1,519 m to 2,815 m, with subseafloor depths ranging from 239 m to 567 m below

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the sea floor. The cores used herein were recovered in 2015 by a PCBT pressure core system during

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the NGHP-02 expedition, as mentioned above (Collett et al., this volume; Kumar et al., this volume).

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The NGHP-02 recovered pressure cores exhibited a wide range of MH saturation. We selected the

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cores with a wide range of low to high P-wave velocities, which is generally associated to the range

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of MH saturation (Oshima et al., this volume). These cores were measured using the hot-disk

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transient method, which enabled us to simultaneously measure the sample’s thermal conductivity,

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specific heat, and thermal diffusivity (Gustafsson, 1991; Tanikawa et al., 2016). MH-saturated cores

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are rare, and the number of samples has been limited. Therefore, we used the single-sided hot-disk

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method, where only a half-sized sample is required, compared to the normal hot-disk method. The

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validity of the results was checked in this study in comparison with the thermal properties estimated

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from the mineral composition modeling.

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2. Methods

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2.1 General core information and sample preparation

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The samples used for the thermal conductivity measurements were MH-bearing sediment

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pressure-core samples recovered during NGHP-02. Recovered pressure cores were transported to the

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laboratory under hydrate stability pressure-temperature conditions to avoid dissociation of gas

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hydrates. Pressure-core samples were analyzed with Pressure-core Nondestructive Analysis Tools

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(PNATs) (Yoneda et al., 2015a; Jin et al., 2016) and subsampled with a cutting tool (Yoneda et al.,

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2015a). The core processing and handling techniques used in this study were also used for dynamic

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behavior/permeability analysis in other studies (Yoneda et al., this issue a,b,c, Oshima et al., this

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issue).

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Figure 1 shows all core processing steps and thermal property measurements conducted in

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this study. The laboratory apparatus and procedures used to prepare the cores for analysis are

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described elsewhere (Yoneda et al., (2015b). The core samples were initially stored in a pressure

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chamber at 10 MPa and 5° C, and the water in the chamber was purged with CH4 gas. The pressure

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in the chamber was maintained at 10 MPa during this procedure. The chamber was cooled to −100°

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C by blowing liquid nitrogen vapor onto the surface of the storage chamber. After cooling, the

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pressure in the chamber was reduced to atmospheric, and the core was immediately dipped in liquid

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nitrogen. The cores were cut into a cylindrical shape with a band saw and lathe while under a flow of

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liquid nitrogen. The cylindrical shape core sample was used for the thermal properties measurement,

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while the adjoining segments were used for measuring the saturation of MH, Sh, and the porosity, ϕ.

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Representative photographs of core samples after cutting are shown in Figures

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2a and 2b. Eight test samples were prepared (Core Nos 1–8). Sample Nos. 4 and 5 were

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cut from the upper and lower portions of Cores NGHP-02-17C-09P, and Nos. 7 and 8,

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respectively, were cut from the upper and lower portions of Core NGHP-02-23C-09P.

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The diameter and length of the samples were approximately 30 and 15 mm (10.6 cm3 in

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volume), respectively.

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2.2 Experimental system for hot-disk transient method

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A schematic diagram of the experimental device used to evaluate the thermal conductivity,

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specific heat, and thermal diffusivity of the prepared natural core samples is shown in Figure 3. The

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experimental device consists of a high-pressure vessel, a device to evaluate the thermal constants

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(thermal conductivity, specific heat, and thermal diffusivity) by the hot-disk transient method, a

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temperature and pressure control unit, a methane gas cylinder, and a syringe pump for applying an

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overburden stress. A hot-disk thermal probe (C5465, produced by Hot disk AB Co., sensor radius

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3.189 mm) and a hot-disk thermophysical property analyzer (TPS-2500S, Hot disk AB Co.) were

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used to measure the thermophysical properties.

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The hot-disk transient method enables simultaneous measurements of the thermal conductivity,

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thermal diffusivity, and specific heat. The hydrate dissociation in the sample pores was prevented by

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the liquid nitrogen, and the core sample was maintained in a metal holder inside the high-pressure

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vessel. The inner diameter of the metal holder is 31 mm, appropriate for holding core samples within 8

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ca. 28 to 30 mm of diameter.

The sample was brought into close contact with a disk-shaped probe, upon constant current, and

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a constant heat was applied to analyze the thermal characteristics from the temperature rise of the

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sensor. The measurement was performed by applying heat to the sphere within a radius of 7 mm

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from the center of the spiral pattern at the tip of the probe. The nickel in the sensor has a double

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helical structure, and the temperature change is captured by the electric resistance of the sensor.

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When the hot-disk sensor heats the sample at constant power, its electrical resistance increases as a

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function of time. The average temperature rise of the hot disk, ∆Tave, is given by

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=

where +1 ]

, 1



%

%

&'

2'

− $ +( × "# $ # ( exp , / 0 4

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$(

3 2

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=[ 10



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∆

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Here, W0 is the average power supplied to the hot-disk sensor during the measurement; r is

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the radius of the hot-disk probe; λ is the thermal conductivity of the sample; τ is the dimensionless

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time (τ = 456/ ), where α is the thermal diffusivity and t is the time from the start of the power

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supply to the sensor probe; m is the number of concentric rings in the TPS probe; and I0 is the

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modified Bessel function (Gustafsson, 1991).

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We used a single-sided configuration for the hot-disk transient measurements. A

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single-sided configuration assumes a perfect mirroring of the symmetry boundary, which is the plane

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of the sensor. We assumed a doubled output for the heating power when analyzing the data. The

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single-sided TPS method has been used to investigate the thermal and physical properties of various

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materials, such as MH (Rosenbaum et al., 2007), high conducting ceramic bars (Gustavsson, 2006),

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and deep marine sediment cores (Tanikawa et al., 2016).

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2.3 Thermal property measurement procedure

The hot-disk method for measuring thermophysical properties developed by Gustafsson is

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based on the Transient Plane Source (TPS) method (Gustafsson, 1991). A photograph of the sample

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holder for the single-sided TPS used here is shown in Figure 4a. The inner diameter of the sample

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holder is 31 mm, the height is 38 mm, and the slit for inserting the sensor probe has a 13 mm

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opening on the bottom of the core holder. The portion below the slit is integrally molded, and a heat

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insulator (made of Styrofoam) is embedded in the groove of the holder metal part. Figure 4a shows

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the method for inserting the sensor probe into the slit and the state of the installation inside the

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measuring vessel. The core sample was installed in the holder while cooling the interior of the vessel

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with liquid nitrogen, followed by rapid pressurization of the vessel with methane gas to prevent the

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dissociation of MH as shown in Figure 4b. The inside of the high-pressure vessel was pressurized to

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10 MPa with methane gas, and the thermal conductivity, specific heat, and thermal diffusivity at a

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temperature of 5.0°C.

Regarding the pressure of the overburden stress in the apparatus, the pressure level kept

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the sensor probe attached firmly to the sample. Note that the effective pressure at the core is

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approximately zero under these conditions. After completion of the measurement, the core sample

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was dissociated under reduced pressure conditions inside the high-pressure vessel at 5.0° C, and the

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thermophysical properties of the core samples were measured after the dissociation at 0.1 MPa and

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5.0° C.

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The analysis software TPS 2500 was used with the attached software Ver. 6 to measure the

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thermophysical properties. The experimental setting for measuring the thermophysical properties

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included the sensor output, P = 50 mW, and the measurement time, t = 20 s. Under these conditions,

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the temperature rise of the sample due to the applied heat pulse was 2.0–3.5°C. The measurement

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conditions of the methane gas under atmospheric pressure (10 MPa) and T = 5.0°C, which is MH

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stable condition according to the MH phase diagram (Sloan and Koh, 2008) about seawater,

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containied 3.5wt % salinity. We estimated the errors for thermal conductivity, thermal diffusivity,

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and specific heat measurements using the single-sided TPS method to be ± 5.4 %, ± 13 %, and

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± 9.5 %, respectively. These values were estimated as follows. The errors of the double-sided

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hot-disk method as provided by the supplier were ± 2 %, ± 5 %, and ± 7 % for thermal conductivity,

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thermal diffusivity, and specific heat, respectively. In a preliminary experiment, we compared the

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values obtained using the double- (normal) and single-sided TPS methods. We also measured the

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thermal constants of Pyrex glass. The errors of the single-sided method with respect to the

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double-sided method were ± 5 %, ± 12 %, and ± 6.4 % in terms of thermal conductivity, thermal

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diffusivity, and specific heat, respectively. Finally, we estimated the errors of the single-sided TPS

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method as ± 5.4 %, ± 13 %, and ± 9.5 % in terms of thermal conductivity, thermal diffusivity, and

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specific heat, respectively.

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2.4 Model for thermal properties

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The following models are applied to calculate the apparent thermal conductivity of the

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composite media (Goto and Matsubayashi, 2009; Woodside and Messmer, 1961). In this study, we

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assumed that the MH-bearing sediment cores consisted of sediment grain, sea water, and MH. The

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formula of the series model (Eq. (3)) applies to the heat flux perpendicular to the structure in a

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layered medium. This model gives the minimum physically possible scale value, which is given by

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the harmonic mean.

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=

89 : 9

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+

8< : <

3

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The formula of the parallel model (Eq. (4)) applies to the heat flux parallel to the structure in a

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layered medium. This model gives the maximum physically possible scale value and is given by the

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weighted arithmetic mean. 12

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= 89 :

9

+ 1−:

;

+ 8< :

<

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The distribution model (Eq. (5)) applies to the random distribution of the components and is

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derived empirically, not theoretically. This model can reproduce the thermal conductivity of marine

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sediment (Goto and Matsubayashi, 2009) and hydrate-bearing sediments in low-hydrate-saturation

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regions (Muraoka et al., 2014). This model gives the intermediate value between the series and

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parallel models, which apply for heat flowing perpendicular and parallel to the structure in a layered

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medium, respectively. =

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>? @



;

@

<

>A @

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, 5

where λ, λh, λs, and λw are the apparent thermal conductivities of the sample, the hydrate crystal, the

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sediment grains, and the seawater, respectively. The sediment sample has porosity, :, with the

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hydrate and seawater saturation in the pore spaces in the sediment grains represented by Sh and Sw,

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respectively, where 8C + 8D = 1.

The specific heat, ρCp, is closely approximated from the arithmetic mean formula (Waite et

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al., 2009; Woodside and Messmer, 1961): EFG = 8C : EC FGC + 1 − : EH FGH + 8D : ED FGD , 6

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where ρh, ρs, and ρw are the densities of the MH, sediment grains, and seawater, respectively, and Cph,

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Cps, and Cpw are the specific heats of the same fractions.

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The thermal diffusivity of this three-component system was calculated using Eqs. (3)–(5)

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and the formula: 5 =

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KLM

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2.5 Thermal property models determined from the mineral composition

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The grain density and specific heat of materials are scalar quantities. The grain density and

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specific heat of an aggregate composed of minerals of N-components are calculated using the

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arithmetic mean formulas:

8 S

= # QR ET U# QR = 1V 8 R'

9 S

R'

S

= # QR FXT U# QR = 1V, 9 R'

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R'

where wj, ρj, and Cpj represent the weight fraction, density, and specific heat, respectively, of the j-th

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mineral.

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The thermal conductivity of an N-mineral component aggregate can be estimated using the

distribution model: P O

S

=Z R'

S [\ R U# ]R R'

= 1V 10

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where λj and Vj represent the thermal conductivity and the volume fraction, respectively, of the j-th

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mineral. This empirical formula has been widely used to calculate the sediment thermal conductivity

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as a function of the mineral composition (Goto and Matsubayashi, 2009; Kinoshita, 1994).

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2.6 Thermal property of the sediment grain measurement procedure

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The core samples for measuring grain densities were dried at 45° C in a vacuumed

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chamber for more than one week following the thermal property measurements. The grain density of

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the dry sediment was measured using a dry-type automatic pycnometer (AccuPyc 1330,

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SHIMADZU), and the value of the grain density was calculated from the average of three

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measurements. Sediment samples for measuring mineral compositions were dried again at 45° C in a

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vacuumed chamber for more than one week. The median grain diameter, d50, and the ratio of clay,

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silt, and sand were determined via a laser diffraction scattering method, using a particle size

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distribution analyzer (Micro trac MT3000II, Nikkiso CO., Japan) for each sample.

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Mineralogical analyses were performed by employing powder x-ray diffraction (PXRD)

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(Smart Lab, Rigaku Co., Japan) using CuKα radiation. The details of the measurement procedures

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for grain densities and mineral components as

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this issue). The specific heat, Cps, was measured via differential scanning calorimetry with a thermal

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analyzer (Customized DSC-204, Netzsch Co., Germany) after the core samples had been dried. To

well as grain sizes are shown in Oshima et al. (in

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estimate the thermal conductivity of the dry sediment grains in the natural MH-bearing sediment, the

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dry sediment grain was mixed with water and vibrated for uniform packing. The thermal

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conductivity, λ, was measured at 5° C. The observed thermal conductivities, λs, of the sediment

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grains were estimated using the following distribution formula: = exp

ln − : ln 1−:

<

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For this calculation, we used λw = 0.571 W m−1 K−1 (NIST Chemistry WebBook,

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Gaithersburg, MD). We used λs as the observed thermal conductivity. The density of the sediment

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grain, ρs’, the specific heat, Cps’, and λs’ were estimated using Eqs. (8), (9), and (10), respectively.

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2.7 Experimental procedures for determining the gas hydrate saturation

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The segments around thermal property measurement samples were placed in the

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high-pressure chamber cooled with liquid nitrogen, and a vacuum was established using a pump.

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Then the MH was dissociated at room temperature, 20° C. The amount of MH in the core, NMH, was

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quantified by measuring the pressure change ∆P in the chamber and using the ideal gas state

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equation.

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(VSW2-31-4-500KPa(abs)WS-H500KPAR2, VALCOM). The accuracy of this gauge is ± 3.5 kPa.

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The chamber volume is Vchamber = 44.78 cm3. After MH dissociation, the core sample was dried at

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45° C for one week. Weight of water, Wwater, weight of the sediment grain, Wsand and grain density,

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gauge

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ρsand, were obtained from the dried core samples. NMH as: abc =

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where V is free space in chamber, R is gas constant, 8414.4598 kPa cm3 K-1 mol-1 andT is 293 K

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∆d ] 12 e

(20°C). NMH contains measured error δP is calculated from abc =

∆d ± gd ] 13 e

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The volume of water is Vwater = Wwater / ρwater. ρwater is 1.000 g cm-3 (Sharqawy et al., 2010).

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The volume of sand is Vsand = Wsand / ρsand. The volume of MH is VMH = WMH / ρMH. ρMH is 0.913 g

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cm-3. The dissociated water from MH in the core is V’water. The volume of core Vcore is:

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Vcore = (Vwater – V’water) + Vsand + VMH

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The MH saturation of core SMH is calculated from

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8bc = [

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The porosity and saturation of the MH were determined using the weight and density of each

component.

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3. Results

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3.1 Sediment samples indexes and measured thermal properties

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Table 1 shows the sample depth below the seafloor, porosity, saturation of MH, median

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diameter, d50, of the sediment grains, and ratio of clay (<3.9 µm), silt (3.9−63 µm), and sand (>63

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µm) in each sediment sample. All samples contained silt-size grains, with concentrations ranging

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from 43% to 86 %. The median diameter, d50, of the MH-bearing sediment in the table is smaller

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than the values in the Nankai Trough area (Ito et al., 2015; Muraoka et al., 2014). Figure 5 shows the

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grain-size particle diameters for each core sample. Figures 5a and 5b show the grain size cumulative

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curve and the frequency plot, respectively. Table 2 shows the relative ratio of the mineral

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compositions of dry sediment grains in each core sample (Yoneda et al., this issue a). Table 3 shows

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the indexes and thermal properties of the major sediment constituent minerals. The thermal

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conductivities of quartz and pyrite are considerably higher than those of the other sediment

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constituent minerals. Table 4 shows the thermal property data of the sediment grains and the thermal

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properties calculated from the mineral composition data (Table 2).

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The error while estimating the specific heat is generated by the standard deviation obtained

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from DSC measurements. Based on the mineral composition, the measured and estimated thermal

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conductivities of the sediment grain are observed to be in agreement, except in case of sample No. 6.

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The observed Cps exhibited a tendency to be higher than Cps’.

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3.2 Thermal properties of the MH-bearing sediment samples

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Figures 6a and 6b display the thermal conductivity, λ, as a function of ϕ, and Sh, of the

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MH-bearing sediment samples, respectively. The measured thermal conductivity of the MH-bearing

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samples is represented by the open diamonds. The measured thermal conductivity of the samples

2

after dissociation MH is represented by the open circles. As observed in Figure 6a, there is a slight

3

decrease in thermal conductivity λ with increasing porosity ϕ. By analyzing the data in Figure 6b, the

4

thermal conductivity is observed to be almost independent of Sh. The thermal conductivity after MH

5

dissociation decreased compared to that before MH dissociation.

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Figures 7a and 7b display the measured specific heat, ρCp, as a function of ϕ, and Sh, of the

7

MH-bearing sediment samples, respectively. As seen in Figures 7a and 7b, the specific heat

8

increased with increasing ϕ. Conversely, the specific heat decreased with increasing Sh. This

9

tendency is discussed in Section 4. As seen in Figure 7b, the specific heat after MH dissociation

10

decreased compared to that before MH dissociation for Sh < ~0.43. Around Sh = 0.43, the ρCp after

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MH dissociation equaled the ρCp prior to MH dissociation. The specific heat after MH dissociation

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in Sh > ~0.43 increased compared to that before MH dissociation.

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Figures 8a and 8b display the measured thermal diffusivity, α, as a function of ϕ and Sh of

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the hydrate-bearing sediment core samples, respectively. As seen in Figure 8a, the measured thermal

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diffusivity decreased with increasing ϕ. Conversely, the measured thermal diffusivity increased with

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increasing Sh. As seen in Figure 8b, the thermal diffusivity after MH dissociation decreased

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compared to that before MH dissociation.

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4. Discussion

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4.1 The validity of single-sided TPS methods for MH-bearing sediment cores

The thermal properties of the test samples were measured via a single-sided TPS method.

4

In the present study, we compared the measured thermal properties obtained by the double (normal)

5

and single-sided TPS method. We measured the thermal constants of Pyrex glass. The λ from double-

6

and single-sided TPS methods were 1.12 and 1.17 W m-1 K-1, respectively. The repeatability of the λ

7

measurement was well established. The measured ρCp from the double- and single-sided TPS

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methods were 1.66 and 1.55 M J m-3 K-1, respectively.

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The repeatability of λ for the test samples Nos. 4 and 5 and Nos. 7 and 8, which come from

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the same cores, confirms repeatability of these measurements. Conversely, the repeatability of ρCp

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and α of the test samples Nos. 4 and 5 and Nos. 7 and 8, which come from the same cores, are

12

slightly different. The repeatability of the measured thermal conductivities of the test samples given

13

from the same cores, as depicted in Figure 6, was shown to be high. In addition, the measured λs of

14

sediment grain and the estimated λs’ were consistent, except for sample No. 6. Therefore,

15

single-sided TPS methods are robust for thermal conductivity measurements. However, the number

16

of test samples with values of the measured specific heat that are consistent with the values from the

17

most robust estimation is less than half the total as seen in Figure 7. Therefore, a larger sample data

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set is required to reach a scientifically sound conclusion about the repeatability of the single-sided

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TPS method.

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4.2 Model-derived thermal property estimates

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Figure 6 demonstrated the thermal conductivities calculated using Eqs. (3)–(5). For these

5

calculations, we used λh = 0.619 (Waite et al., 2007) and λw = 0.575 W m−1 K−1 (3.5% salinity)

6

(Sharqawy et al., 2010). For λs, we used the observed thermal conductivities shown in Table 4. The

7

values calculated using the distribution model agreed best with the measured values of λ.

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The specific heat values estimated by the arithmetic mean formula are shown in Figure 7.

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The specific heat ρCp is closely approximated by the arithmetic mean formula (Woodside and

10

Messmer, 1961; Waite et al., 2009). For this calculation, ρh = 925.2 kg m−3, Cph = 2191 J kg−1 K−1

11

(Waite, 2007), ρw = 1031 kg m−3, and Cpw = 4110 J kg−1 K−1 (3.5% salinity) (Sharqawy et al., 2010).

12

For ρs and Cps, we used the observed values shown in Table 4.

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Figure 8 shows the model-derived thermal diffusivities for Eqs. (3)–(5). The thermal

14

diffusivity was calculated from the formula 5 = /EFG. For this calculation, λ was taken from the

15

corresponding model (see Figure 6), and the value for ρCp was calculated from the arithmetic mean

16

(see Figure 7). The values calculated using the distribution model agreed best with the measured

17

values of α; the calculated λ values agreed in a similar manner.

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4.3 Trend of the thermal property measurement results

Figure 6a reveals that the thermal conductivity slightly decreases with increasing porosity.

3

Such an observation can be rationalized in terms of the significantly larger thermal conductivity of

4

the sediment grain as compared to that of the other components (i.e., water and MH). Figure 6b

5

shows that the thermal conductivity is independent of the MH saturation. This may result from the

6

thermal conductivity of water and MH being nearly the same.

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Figure 7a shows that the specific heat increased with increasing porosity. The specific heat

8

of water and MH are 4.15 (NIST Chemistry WebBook) and 1.99 MJ m-3 K-1 (Waite et al., 2007),

9

respectively. Thus, this result can be attributed to the increasing volume fraction of both water and

10

MH. Figure 7b shows that the specific heat decreased with increasing MH saturation. This is likely a

11

result of lower water saturations relative to the increasing GH saturations.

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Figure 8a shows that thermal diffusivity decreases with increasing porosity. Figures 6 and

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7 show a nearly constant trend in thermal conductivity and increasing specific heat with porosity.

14

Figure 8b shows that the thermal diffusivity increases with increasing MH saturation. The latter can

15

be attributed to the measured constant thermal conductivity and the decreasing specific heat with

16

increasing Sh as shown in figure 6 and 7.

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However, we infer that the measured porosity range is quite narrow in this study. Furthermore, a

larger data set is required to derive statistically defendable thermal property estimates.

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4.4 Influence of clay sediment on the thermal properties Table 4 indicates that the observed λs is much lower than λs’ measured for sample No. 6,

4

which has a higher clay grain size content than the other samples. This indicates that the lower value

5

of λ is likely to be affected by the small grain size. One suggestion is that the small grains may

6

influence the thermal conductivities of the sediment matrix, as depicted by the theoretical study of

7

Batchelor and O’Brien (1977) and by the phenomenological model of Every et al. (1992). Batchelor

8

and O’brien (1977) numerically solved an integral equation describing the distribution of

9

temperature over the surface of a particle near the contact point, finding that the interparticle

10

conductive heat transfer was proportional to the particle radius and inversely proportional to the

11

intercontact distance. Every et al. (1992) developed a theoretical model to achieve a

12

phenomenological model that relates the interface resistance with the dispersed particle size in the

13

matrix. According to this model, the thermal conductivity of the composite is reduced by the thermal

14

boundary resistance, in which the particle radius is less than a specific radius called the Kapitza

15

radius.

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Mineral compositional analysis indicates that the distribution model cannot reproduce the

17

thermal conductivities of clay-rich sediments. Additional studies are required to examine the effect

18

of small grain sizes on the thermal conductivities and to develop a better modeling technology for

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the MH-bearing sediments. The observed Cps exhibited a tendency to be higher than Cps’. The

2

aforementioned tendency is likely to be linked to the unqualified compounds, such as organic

3

compounds.

4

4.5 The effects of gas released from MH on the thermal properties

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Figures 6 and 8 show that thermal conductivities and diffusivities decreased after MH

7

dissociation. If MH dissociates, it releases gas and water, and the resulting sediment thermal

8

conductivities are low. This situation should occur when the saturation of gas increases due to MH

9

dissociation. As seen in Figure 7b, the specific heat after MH dissociation decreased compared to

10

that before MH dissociation for Sh < 43 %. This may occur if the specific heat of the formed gas is

11

sufficiently low. When Sh > 43%, the specific heat after MH dissociation increased compared to that

12

before MH dissociation, likely because the specific heat of the formed water (4.15 MJ m-3 K-1) is

13

nearly double that of MH (1.99 MJ m-3 K-1).

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4.6 Thermal property modeling of in situ MH-bearing sediments

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The distribution model of Eq.5 is valid for a wide range of MH saturations. The arithmetic

17

mean model for specific heat should be robust. Therefore, we can accurately estimate the thermal

18

properties (λ, ρCp, α) of an MH-bearing sediment from the set of measured or assumed thermal

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conductivities for the sediment grains, porosity, and hydrate saturation conditions in a given

2

sediment layer. We can accurately estimate the thermal properties of MH-bearing sediments of sand

3

or silt layers from the mineral composition of the sediment using the method of Henninge et al.

4

(2005). However, thermal property estimates for clay-rich layers are not likely to be reproduced by

5

the distribution model; therefore, further studies are required to determine the relation between the

6

thermal properties, mineral composition, and small grain sizes in MH-bearing sediments high in clay

7

content.

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5. Conclusions

We measured the thermal constants of MH-bearing sediment samples recovered in 2015

11

from the Krishna–Godavari Basin in India. These samples were recovered during the NGHP-02

12

expedition. Measurements were performed using a single-sided TPS method. Then, density and

13

mineral composition measurements for the dry sediment grain were conducted.

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To investigate the influence of the sediment composition on the thermal properties, the

15

thermal constants of the MH-bearing sediments were measured at 5° C and 10 MPa over a porosity

16

range of 41 % ≤ : ≤ 51 % and an MH saturation range of 17 % ≤ Sh ≤ 74 %.

17

The measured thermal conductivity slightly decreased with increasing ϕ and was

18

independent of Sh. The thermal conductivity after MH dissociation decreased compared to that

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before MH dissociation. The measured specific heat increased with increasing ϕ, whereas it

2

decreased with increasing Sh. The specific heat after MH dissociation decreased, as compared to the

3

specific heat before MH dissociation in the low Sh range. In a high Sh range, the specific heat after

4

MH dissociation increased, as compared to that before MH dissociation. The measured thermal

5

diffusivity decreased with increasing ϕ, whereas it increased with increasing Sh. The thermal

6

diffusivity after MH dissociation decreased compared to that before MH dissociation.

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The repeatability of the measured thermal conductivities of test samples given from the

8

same cores was good. In addition, the measured thermal conductivity of the sediment grain and the

9

estimated thermal conductivity based on the mineral compositional analysis were consistent, except

10

for one sample (Sample No.6). Therefore, we consider the single-sided TPS method employed in the

11

present study to be robust for thermal conductivity measurements. However, the number of test

12

samples with single-sided TPS measured specific heat values in agreement with the values from the

13

DSC method was less than half of the total. To reach an unequivocal conclusion, a larger data set

14

would be required. Various models were used to estimate the thermal constants to examine the

15

applicability of these models to MH-bearing sediments. The distribution model appears to be valid

16

over a wide range, from low to high MH saturations. The arithmetic mean model for the specific

17

heat calculations also appears to be robust. We can estimate accurate thermal properties of

18

MH-bearing sediments of sand or silt layers using the method based on the mineral composition.

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However, the clay layers are not likely to be reproduced by the distribution model. This indicates

2

that the lower value of the observed λ is likely a product of the smaller grain sizes.

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Acknowledgments

This study was conducted as part of the activity of the Research Consortium for Methane Hydrate

6

Resources in Japan [MH21 Research Consortium] as planned by the Ministry of Economy, Trade,

7

and Industry (METI), Japan and India National Gas Hydrate Project Expedition 02 as planned by the

8

Ministry of Petroleum & Natural Gas within the Government of India, Directorate General of

9

Hydrocarbons (DGH). We would like to express our sincere thanks for their support. We are also

10

grateful to Dr. T. Collett of U.S. Geological Survey (USGS) for his leadership contributions to the

11

India National Gas Hydrate Project Expedition 02. We extend special thanks to the science team of

12

the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Oil and Natural Gas

13

Corporation Limited (ONGC), USGS, The Japan Oil, Gas and Metals National Corporation

14

(JOGMEC) and the GeoTEK for technical support and fruitful discussions during the pressure

15

coring, as well as Drs. N. Tenma, J. Nagao, Y. Jin, J. Yoneda, Y. Konno, M. Kida, N. Katou, and Ms.

16

S. Izumi of AIST for their support during the onshore pressure core analysis.

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Yoneda, J., Oshima, M., Kida, M., Kato, A., Konno, Y., Jin, Y., Tenma, N., (this issue b),

15

Consolidation and hardening behavior of hydrate-bearing pressure core sediments under high

16

confining pressure based on natural core sample from NGHP-02. Mar. Pet. Geol.

17 18

Yoneda, J., Oshima, M., Kida, M., Kato, A., Konno, Y., Jin, Y., Jang, J., Waite, W., Kumar, P., Tenma

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N., (this issue c), Permeability variation and anisotropy of hydrate-bearing pressure core sediment

2

from India's National Gas Hydrate Program ExpeditionNGHP-02. Mar. Pet. Geol.

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Figure captions

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Figure 1. The entire procedure of the MH-bearing sediment core treatment for the thermal property

measurement study. This figure is arranged from Figure 1 in Yoneda et al (2015b).

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Figure 2. Test samples after cutting the sediment core recovered from the Krishna–Godavari basin in

11

India: (a) No. 4 cut from NGHP-02-17C-09P 286.57-286.61 mbsf and (b) No. 6 cut from

12

NGHP-02-19C-04P 311.84-311.90 mbsf.

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Figure 3. A schematic diagram of the experimental device used to evaluate the thermophysical

15

properties of the natural core samples.

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Figure 4. (a) Inserting the sensor probe into the slit and the state of the installation inside the

18

measuring vessel. (b) The natural core sample was installed while cooling the interior of the vessel

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with liquid nitrogen.

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Figure 5. The particle diameter distribution for the test samples of the MH-bearing sediment cores: (a)

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cumulative grain size curve of the samples and (b) grain-size frequency plot of the samples.

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Figure 6. Thermal conductivity, λ, as a function of the porosity, ϕ, and MH saturation, Sh, of

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MH-bearing sediment samples and the estimate of the thermal conductivity obtained from each

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thermal conductivity model. (a) The thermal conductivity, λ, as a function of the porosity, :. (b) The

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thermal conductivity, λ, as a function of the MH saturation, Sh. The open diamonds (◊), open circles

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(○), diagonal crosses (×), open squares (□), and open triangles (∆) indicate the measured results, the

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measured results after dissociation, the series model, the parallel model, and the distribution model,

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respectively, for the thermal conductivity. The denotation dash indicates that the value is after MH

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dissociation.

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Figure 7. The specific heat, ρCp, of the MH-bearing sediment samples and the calculated results from

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the arithmetic mean formula. (a) The specific heat, ρCp, as a function of the porosity, :. (b) The

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specific heat, ρCp, as a function of the MH saturation, Sh. The open diamonds (◊), open circles (○),

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and open squares (□) represent the measured results, the measured results after dissociation, and the

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calculated results from the arithmetic mean formula, respectively. The denotation dash indicates that

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the value is after MH dissociation.

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Figure 8. Thermal diffusivity, α, of the MH-bearing sediment samples and the estimates generated

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using the thermal conductivity models. (a) The thermal diffusivity, α, as a function of the porosity, :.

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(b) The thermal diffusivity, α, as a function of the MH saturation, Sh. The open diamonds (◊), open

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circles (○), crosses (×), open squares (□), and open triangles (∆) indicate the measured results, the

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measured results after dissociation, the series model, the parallel model, and the distribution model,

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respectively. The denotation dash indicates that the value is after MH dissociation.

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Table captions

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Table 1. The sample depth below seafloor, porosity, saturation of MH, the median diameter, d50, of

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the sediment grains, and the ratio of clay, silt, and sand in each sediment sample.

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Table 2. The relative weight ratio of the mineral compositions of dry sediment grain in each core

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sample.

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Table 3. Indexes and thermal properties of the major sediment constituent minerals.

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a

Nasser, M. S., and James, A. E. (2006); b Robie, R. A., and Hemingway, B. S. (1991);c Michot, A.,

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Smith, D. S., Degot, S., and Gault, C. (2008); f

g

d

Johnson and Olhoeft (1984);

e

Goto, S., and

h

i

3

Matsubayashi, O. (2009);

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Waples and Waples 2004; j Robie et al., 1978;k Skauge et al.,1983; and l Brigaud, F. and G. Vasseur

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1989.

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Horai(1971); Drury et al., 1984; Horai, K. and G. Simmons,1969;

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Table 4. Thermal property data of sediment grains and the calculated thermal properties from the

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mineral composition data.

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Table 1.

Depth

Saturation

Median

of MH

Diameter d50

Porosity

Clay

Silt

Sand

Sample mbsf

%

%

µm

No. No.1

273.98

45

68±1

35.1

5.1

71.3

23.6

16B-03P (274.33-274.37)

No.2

274.33

41

73±1

71.3

2.9

42.8

54.3

16B-07P (287.26-287.34)

No.3

287.26

45

74±2

29.1

5.3

85.9

8.8

17C-09P (287.07-287.11) upper

No.4 286.57

50

32±8

No.5

19C-04P (311.84-311.90)

No.6

23C-09P (281.81-281.87) upper

No.7

311.84

23C-09P (281.81-281.87) lower

No.8

2

43±2

47

17±3

12.0

69.7

18.3

23.0

11.8

69.7

18.5

18.2

22.7

58.6

18.7

18.0

18.1

61.5

20.4

19.8

13.6

74.5

11.9

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22.2

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17C-09P (287.07-287.11) lower

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16B-03P (273.98-274.03)

281.81

4

Vol %

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Table 2. Weight fraction wt% Sample Kaolinite

Chlorite

Quartz

Orthoclase

Plagioclase

Calcite

Pyrite

mica

Smecite

Siderite

Dolomite

Hornblende

No.1

3.6

3.6

46.0

10.6

12.2

8.4

0.6

4.9

5.2

0.5

3.0

1.3

No.2

1.7

2.8

56.9

8.7

13.0

6.1

1.1

2.4

1.6

0.6

1.5

3.5

No.3

4.4

5.6

47.4

5.8

9.3

9.6

0.6

6.0

7.3

0.6

1.4

2.0

No.4,5

4.5

5.2

55.5

5.3

8.5

8.4

1.3

4.2

2.1

0.5

2.1

2.4

No.6

2.2

1.7

48.0

4.4

7.9

27.1

3.0

1.8

2.1

0.3

1.1

0.4

No.7,8

4.2

6.3

51.8

7.0

10.6

9.0

1.0

4.9

2.0

0.7

1.7

0.7

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Table 3.

Specific heat

kg m−3

J kg−1 K−1

W m−1 K−1

Kaolinite

2600a

907b

0.34c

Chlorite

2800d

818e

Quartz

2648d

741g

Orthoclase

2570d

707e

Plagioclase

2606f

730i

Calcite

2710d

820g

Pyrite

5011d

518j

Mica

3000i

770i

Smecite

2608d

Siderite

3944i

Dolomite

2840i

Hornblende

3080i

5

2.32h 2.32f

3.59h

19.21h 2.34f

795g

1.88l

740i

3.004f

870i

5.506f

710i

3.075f

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7.69h

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5.15f

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Thermal conductivity

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Table 4. Observed

Mineral composition

Specific heat Cps

Thermal conductivity λs

ρs'

Cps'

λs'

kg cm−3

J kg−1 K−1

W m−1 K−1

g cm−3

J kg−1 K−1

W m−1 K−1

No.1

2710

767.0 ± 20.2

4.44 ± 0.41

2688

757.0

4.12

No.2

2690

775.5 ± 27.5

4.37 ± 0.36

2700

745.6

4.85

No.3

2700

760.5 ± 16.8

4.28 ± 0.43

2703

762.7

4.16

No.4,5

2700

794.0 ± 8.2

4.78 ± 0.43

No.6

2640

823.6 ± 14.5

3.27 ± 0.38

No.7,8

2710

756.5 ± 19.1

4.34 ± 0.38

2

SC

757.8

4.67

2744

761.3

4.75

2704

757.9

4.49

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2717

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Sample No.

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Density ρs

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