日本地球惑星科学連合2018年大会

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[JJ] ポスター発表

セッション記号 S (固体地球科学) » S-CG 固体地球科学複合領域・一般

[S-CG63] 地球惑星科学におけるレオロジーと破壊・摩擦の物理

2018年5月20日(日) 15:30 〜 17:00 ポスター会場 (幕張メッセ国際展示場 7ホール)

コンビーナ:桑野 修(国立研究開発法人 海洋研究開発機構)、清水 以知子(東京大学大学院理学系研究科地球惑星科学専攻)、石橋 秀巳(静岡大学理学部地球科学専攻、共同)、田阪 美樹(島根大学)

[SCG63-P06] Permeability benchmark using 3D numerical modelling

*藤田 和果奈1Eichheimer Philipp2Thielmann Marcel2Popov Anton3Golabek Gregor2Kaus Boris3 (1.東北大学大学院理学研究科、2.バイロイト大学バイエルン地球科学研究所、3.ヨハネス・グーテンベルク大学マインツ地球科学研究所)

キーワード:デジタルロック、浸透率、砂岩、数値計算

Migration of geological fluids is an important process controlling chemical transport and mechanical properties in the Earth’s interior (e.g. melt segregation from partially molten region; segregation of dehydrated fluid from a subducting slab).

Geological fluids percolate upwards due to their buoyancy and permeability is a key factor that controls the flow rate. Therefore the effective permeability depends on the microscopic pore fluid connectivity.

Here we calculate permeability numerically for high resolution CT scans of rocks. For this purpose we use the 3D thermomechanical code LaMEM (Lithospheric and Mantle Evolution Model) (Kaus et al., 2016). We compute the flow velocities and then plug them into Darcy’s law. For benchmarking, we calculate fluid flow through single and multiple pipes using LaMEM. We compare the resulting permeabilities using different grid resolutions against the analytical solution for Hagen-Poiseuille flow.

In a next step we computed the permeability of Fontainbleau sandstone digitalized by Andra et al., 2012. We obtained 2171mD which is higher than 1100mD from laboratory measurement (Keehm 2003). However, our result is almost consistent with numerical results by Andra et al., 2012 which are 1503mD for Lattice-Boltzmann method and 1914mD for Explicit Jump method respectively.


(This work was supported by the JSPS Japanese-German Graduate Externship)



Andra et al,(2013). Digital rock physics benchmarks-Part I: Imaging and segmentation Computers and Geoscience, 50 25-32

Andra et al,(2013). Digital rock physics benchmarks-part II: Computing effective properties Computers and Geoscience, 50 33-43

Kaus, B et al, (2016). Forward and inverse modeling of lithospheric deformation on geological timescales. In: Binder, K., Mller, M., Kremer, M., Schnurpfeil, A. (Eds.), NIC Proceedings. Vol. 48. pp. 299–307.

Keehm, Y., (2003). Computational Rock Physics: Transport Properties in Porous Media and Applications. Ph.D. Dissertation, Stanford University. 135 pp.