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Preparation and property evaluation of a temperature-resistant Zr-crosslinked fracturing fluid
Ming Zhou,Jinfeng Zhang,Zhonghua Zuo,Mao Liao,Peng’ao Peng 한국공업화학회 2021 Journal of Industrial and Engineering Chemistry Vol.96 No.-
Compared with the traditional water-based fracturingfluid thickener guanidine gum, the price ofsynthetic polymer is relatively low, and the temperature resistance, shear resistance and viscoelasticityof the formed fracturingfluid are better. Therefore, the fracturingfluid formed by the crosslinking ofHPAM and organic metal crosslinker will become one of the hot spots in the research of water-basedfracturingfluid. In this paper, zirconium oxychloride, acetic acid and triethanolamine were used as rawmaterials to prepare organic Zr-crosslinked agent. The polymer gel was formed by crosslinking theorganic Zr-crosslinked agent with the acrylamide copolymer KY-5S solution, and the viscosity of the gelwith different concentrations was investigated. The performance of the fracturingfluid was evaluatedthrough laboratory experiments. The results showed that the viscosity of the fracturingfluid remainedabove 100 mPa s at the shear rate of 170 s 1 and after 2 h of shear at 140 C. When the temperature was20 C, the shear stress was 0.1 Pa, and the frequency f varied from 0.1 to 10 Hz, the elastic modulus offracturingfluid was at least 11.6 Pa, and the viscous modulus was at least 8.54 Pa. At the temperature of90 C, when the addition of glue breaker was 0.01 wt%, the longest breaking time was 3 h, and themaximum viscosity of breaking liquid was 4.05 mPa s. The damage rate of core permeability of gluebreaker was about 13%. When the temperature was 25 C and the shear rate was 0–170 s-1, the viscosity offracturingfluid remained above 200 mPa s.
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PointMS: Semantic Segmentation for Point Cloud Based on Multi-scale Directional Convolution
Hui Chen,Wanlou Chen,Yipeng Zuo,Peng Xu,Zhonghua Hao 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.10
In the field of point cloud scene segmentation with deep learning, the ability of the network to extract spatial structure information limits the performance of semantic segmentation. This work proposes a novel framework named PointMS, which handles the semantic segmentation of point cloud scene, to solve the problem of missing local feature information due to the lack of spatial structure information on the training stage. The structure of framework utilizes spatial structure information of point cloud and balances the extraction of global feature and subtle feature when processing point cloud data. Firstly, a multi-scale combination module (SIFT-MS) is used to extract local features of different scales for enhancing the perception of local structure information at each point. Secondly, the process of feature transmission often leads to the loss of information, so a feature supplement module (FSM) is proposed to complete the information lost after feature transformation through the effective combination of global feature and subtle feature. This module integrates the features of different locations to supplement the information lost in feature conversion. The experimental results demonstrate that the proposed framework is efficient for semantic segmentation of S3DIS dataset. SIFT-MS module and FSM module can effectively improve the performance of the semantic segmentation model of point cloud.
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