Apparent permeability model for shale oil with multiple mechanisms
Apparent permeability model for shale oil with multiple mechanisms
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多机制页岩油视渗透率模型
DOI：
10.1016/j.petrol.2019.01.038
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发表时间：
2019-04
影响因子：
--
通讯作者：
Xu Yajuan
中科院分区：
文献类型：
--
作者：
Feng Qihong;Xu Shiqian;Wang Sen;Li Yuyao;Gao Fangfang;Xu Yajuan
Accurate apparent permeability (AP) model for oil transport in shale must consider natural fractures and micro/nano-pores within organic matter and inorganic matrix. Furthermore, the effect of pore geometry, tortuosity, pore size distribution (PSD), the spatial distribution of different pores, total organic content (TOC), and liquid slip should also be taken into account. However, all of the aforementioned factors are not simultaneously considered in one AP model. In this work, we develop an AP model in 3D for shale oil to overcome this drawback. We first validate our model by comparing with the published data. Then sensitivity analysis for some parameters is investigated. After that, shale oil APs are used in a macro-scale reservoir simulation model. In this way, the effect of some micro-scale factors on production performance can be quantitatively evaluated. Notably, on the basis of embedded discrete fracture model (EDFM), our reservoir simulation model considers non-linear flow in shale matrix, complex hydraulic fracture geometry, and pressure-dependent permeability of hydraulic fractures. Results show that natural fractures contribute the most to AP, followed by organic pores and inorganic pores. In general, the AP in the horizontal direction is approximately twice that in the vertical direction in shale. Furthermore, the maximum error caused by the pore type on shale oil production is ∼31%, followed by tortuosity (∼25%), pore shape (∼13%) and slip effect (∼4.8%). In conclusion, it is necessary to simultaneously incorporate the influences of the aforementioned factors. Our stochastic permeability model can serve as an efficient tool to determine shale oil AP.
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影响因子：
4.6
作者：
Zuloaga-Molero P;Yu W;Xu Y;Sepehrnoori K;Li B
通讯作者：
Li B
DOI：
10.1016/j.ijheatfluidflow.2016.05.010
发表时间：
2016-12-01
影响因子：
2.6
作者：
Eshghinejadfard, Amir;Daroczy, Laszlo;Thevenin, Dominique
通讯作者：
Thevenin, Dominique
影响因子：
--
作者：
Jianlei Sun;E. Gamboa;D. Schechter;Zhenhua Rui
通讯作者：
Jianlei Sun;E. Gamboa;D. Schechter;Zhenhua Rui
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DOI：
10.1073/pnas.1612608114
发表时间：
2017-03-28
影响因子：
11.1
作者：
Wu, Keliu;Chen, Zhangxin;Dong, Xiaohu
通讯作者：
Dong, Xiaohu
影响因子：
--
作者：
Pengju Chen;S. Miska;Rui Ren;Mengjiao Yu;E. Ozbayoglu;N. Takach
通讯作者：
Pengju Chen;S. Miska;Rui Ren;Mengjiao Yu;E. Ozbayoglu;N. Takach