2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)最新文献

Acoustic Sensors to Measure Speed of Oil Flow in Downhole Pipes 声学传感器测量井下油管中油流速度

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-09-16 DOI: 10.1109/ICIEA.2019.8833893

N. Vahabi, Mohammed A. Alabdullah, D. Selviah

{"title":"Acoustic Sensors to Measure Speed of Oil Flow in Downhole Pipes","authors":"N. Vahabi, Mohammed A. Alabdullah, D. Selviah","doi":"10.1109/ICIEA.2019.8833893","DOIUrl":"https://doi.org/10.1109/ICIEA.2019.8833893","url":null,"abstract":"This study was conducted to estimate the downhole speed of flow in oil wells and determined the flow direction by analyzing acoustic data recorded by fibre optic distributed acoustic sensors. The signals generated from acoustic data are in the time versus distance domain that are then normalized and differentiated with respect to distance. A 2D Fast Fourier Transform is used to convert time to frequency and distance to wave-number for subsequent calculation. A Gamma correction function was employed to enhance an intensity of the signal in the frequency wevenumber domain. Also, decaying function was successfully applied to enhance the signals with a very low frequencies. We developed a novel method called integration along the radius in polar coordinate to measure the speed of sound and calculating the speed of oil flow. We compared the performance of our method with a Radon transform and proved our method outperforms an existing methods in both processing time and accuracy. The data sets used in this study are recorded from real oil and gas pipes which means there is no controlled environment and there are lots of noisy signals as a result of unpredicted events under the sea. The result of this study is applicable in Oil and Gas production energy industry, Hydraulic fracturing and shale gas extraction energy industry, Borehole water supply industry, Gas pipeline transportation energy industry and Carbon Dioxide Sequestration industry.","PeriodicalId":311302,"journal":{"name":"2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128417474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Fault diagnosis of wind turbine bearing using synchrosqueezing wavelet transform and order analysis 基于同步压缩小波变换和阶数分析的风电轴承故障诊断

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8833879

Jun Guo, Xiaoxian Wang, C. Zhai, Jiahao Niu, Siliang Lu

引用次数: 4

Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning 基于深度学习的海上钻井作业井涌检测与井涌规模估计

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8833850

A. K. Fjetland, Jing Zhou, Darshana Abeyrathna, Jan Einar Gravdal

{"title":"Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning","authors":"A. K. Fjetland, Jing Zhou, Darshana Abeyrathna, Jan Einar Gravdal","doi":"10.1109/ICIEA.2019.8833850","DOIUrl":"https://doi.org/10.1109/ICIEA.2019.8833850","url":null,"abstract":"An uncontrolled or unobserved influx or kick during drilling has the potential to induce a well blowout, one of the most harmful incidences during drilling both in regards to economic and environmental cost. Since kicks during drilling are serious risks, it is important to improve kick and loss detection performance and capabilities and to develop automatic flux detection methodology. There are clear patterns during a influx incident. However, due to complex processes and sparse instrumentation it is difficult to predict the behaviour of kicks or losses based on sensor data combined with physical models alone. Emerging technologies within Deep Learning are however quite adapt at picking up on, and quantifying, subtle patterns in time series given enough data. In this paper, a new model is developed using Long Short-Term Memory (LSTM), a Recurrent Deep Neural Network, for kick detection and influx size estimation during drilling operations. The proposed detection methodology is based on simulated drilling dataand involves detecting and quantifying the influx of fluids between fractured formations and the well bore. The results show that the proposed methods are effective both to detect and estimate the influx size during drilling operations, so that corrective actions can be taken before any major problem occurs.","PeriodicalId":311302,"journal":{"name":"2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125113579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Research on binocular clear image hazing algorithm based on depth map 基于深度图的双目清晰图像模糊算法研究

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8833996

Wenjun Song, Lijun Yun, Zaiqing Chen, Zhixia Ye

引用次数: 0

Battery system selection in DC microgrids for residential applications: an Australian case study 住宅用直流微电网的电池系统选择:澳大利亚案例研究

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834035

U.G.K. Mulleriyawage, W. Shen, Cungang Hu

引用次数: 6

Energy efficiency analysis of a liquefied natural gas and electric power combined transmission system 液化天然气与电力联合传输系统的能源效率分析

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834234

Chengfeng Zhu, Hongbo Tan, Yanzhong Li, Yang Nie, Jiamin Shi

引用次数: 9

Analysis and Design of a Novel Transformer-less-Inverter in Grid-Connected Photovoltaic System 光伏并网系统中一种新型无变压器逆变器的分析与设计

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834150

Manisha Verma, S. Gangavarapu, A. Rathore

引用次数: 3

Modeling and Control Strategy for Zero Voltage Switching Condition in Series-Series Compensated Inductive Power Transfer System 串联补偿电感输电系统零电压切换工况建模与控制策略

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8833898

W. Wenbin, Xin Jianbo, Fan Ruixiang, L. Qiong, Meng Tianqi, Wang Ziyi, Z. Ao

引用次数: 1

Design and analysis of a novel tip-tilt stage with high precision for space applications 一种新型高精度空间倾斜台的设计与分析

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834366

Jun Jiang, Qiang Zhang, Jikui Liu, Yuanzi Zhou, Wen Wen, Wei Liu, Xuepeng Liu

引用次数: 0

Enhance cross-subject MEG decoding by combining Riemannian Geometry feature extraction and multi-task learning 结合黎曼几何特征提取和多任务学习，增强跨学科脑磁图解码能力

2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2019-06-19 DOI: 10.1109/ICIEA.2019.8834085

Yalin Liu, Tianyou Yu, Yuanqing Li

引用次数: 2