Dynamic capillary pressure analysis of tight sandstone based on digital rock model

In recent studies, dynamic capillary pressure has shown significant impacts on the flow behaviors in porous media under transient flow condition. However, the effect of dynamic capillary pressure effect on tight sandstone is still not very clear. Since lattice Boltzmann method (LBM) is a very promising and widely used method in analyzing flow behaviors, therefore, a two-phase D3Q27 LBM model is adopted in this paper to simulate the flow behaviors and analyze the dynamic capillary pressure effect in tight sandstone. Moreover, a new pore segmentation method for tight sandstone base on U-net deep learning model is implemented in this study to improve the pore boundary qualities of pore space, which is crucial for two-phase LBM simulation of tight sandstone. A total of 3800 3D sub-volume data sets extracted from computed tomography data of 19 tight sandstone samples are selected as ground truth data to train the network and segment the pore space afterward. The simulation results based on the segmented digital rock model, show that nonwetting phase fluid prefer the path with lower dynamic capillary pressure in the seepage process before breaking through the porous model. Furthermore, the increase of injection rate causes the saturation changes more quickly, injection rate also shows apparent positive correlation relationship with capillary pressure, which implies that dynamic capillary pressure effect also exists in tight sandstone, and LBM based two-phase flow simulation could be used to quantitatively analyze such effect in tight sandstone.Cited as: Cao, Y., Tang, M., Zhang, Q., Tang, J., Lu, S. Dynamic capillary pressure analysis of tight sandstone based on digital rock model. Capillarity, 2020, 3(2): 28-35, doi: 10.46690/capi.2020.02.0

The Research on Complex Lithology Identification Based on Well Logs: A Case Study of Lower 1st Member of the Shahejie Formation in Raoyang Sag

Lithology identification is the basis for sweet spot evaluation, prediction, and precise exploratory deployment and has important guiding significance for areas with low exploration degrees. The lithology of the shale strata, which are composed of fine-grained sediments, is complex and varies regularly in the vertical direction. Identifying complex lithology is a typical nonlinear classification problem, and intelligent algorithms can effectively solve this problem, but different algorithms have advantages and disadvantages. Compared were the three typical algorithms of Fisher discriminant analysis, BP neural network, and classification and regression decision tree (C&RT) on the identification of seven lithologies of shale strata in the lower 1st member of the Shahejie Formation (Es1L) of Raoyang sag. Fisher discriminant analysis method is linear discriminant, the recognition effect is poor, the accuracy is 52.4%; the accuracy of the BP neural network to identify lithology is 82.3%, but it belongs to the black box and can not be visualized; C&RT can accurately identify the complex lithology of Es1L, the accuracy of this method is 85.7%, and it can effectively identify the interlayer and thin interlayer in shale strata

Nanomaterial integrated 3D printing for biomedical applications

3D printing technology, otherwise known as additive manufacturing, has provided a promising tool for manufacturing customized biomaterials for tissue engineering and regenerative medicine applications. A vast variety of biomaterials including metals, ceramics, polymers, and composites are currently being used as base materials in 3D printing. In recent years, nanomaterials have been incorporated into 3D printing polymers to fabricate innovative, versatile, multifunctional hybrid materials that can be used in many different applications within the biomedical field. This review focuses on recent advances in novel hybrid biomaterials composed of nanomaterials and 3D printing technologies for biomedical applications. Various nanomaterials including metal-based nanomaterials, metal-organic frameworks, upconversion nanoparticles, and lipid-based nanoparticles used for 3D printing are presented, with a summary of the mechanisms, functional properties, advantages, disadvantages, and applications in biomedical 3D printing. To finish, this review offers a perspective and discusses the challenges facing the further development of nanomaterials in biomedical 3D printing.</p