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用于纳米流体驱油监测的光纤布拉格光栅反演算法

Inversion Algorithm of Fiber Bragg Grating for Nanofluid Flooding Monitoring.

作者信息

Yahya Noorhana, Nyuk Chai Mui, Ismail Ahmad Fauzi, Hussain Nazabat, Rostami Amir, Ismail Atef, Ganeson Menaka, Ali Abdullah Musa

机构信息

Department of Fundamental and Applied Science, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia.

Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia.

出版信息

Sensors (Basel). 2020 Feb 13;20(4):1014. doi: 10.3390/s20041014.

DOI:10.3390/s20041014
PMID:32069956
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7070743/
Abstract

In the current study, we developed an adaptive algorithm that can predict oil mobilization in a porous medium on the basis of optical data. Associated mechanisms based on tuning the electromagnetic response of magnetic and dielectric nanoparticles are also discussed. This technique is a promising method in rational magnetophoresis toward fluid mobility via fiber Bragg grating (FBG). The obtained wavelength shift due to FeO injection was 75% higher than that of dielectric materials. This use of FBG magneto-optic sensors could be a remarkable breakthrough for fluid-flow tracking in oil reservoirs. Our computational algorithm, based on piecewise linear polynomials, was evaluated with an analytical technique for homogeneous cases and achieved 99.45% accuracy. Theoretical values obtained via coupled-mode theory agreed with our FBG experiment data of at a level of 95.23% accuracy.

摘要

在当前的研究中，我们开发了一种自适应算法，该算法可以基于光学数据预测多孔介质中的油运移情况。同时还讨论了基于调节磁性和介电纳米颗粒电磁响应的相关机制。这项技术是通过光纤布拉格光栅（FBG）实现合理磁泳以控制流体流动性的一种很有前景的方法。由于注入FeO而获得的波长偏移比介电材料的高75%。这种FBG磁光传感器的应用对于油藏中的流体流动追踪可能是一个重大突破。我们基于分段线性多项式的计算算法，通过解析技术对均匀情况进行了评估，准确率达到了99.45%。通过耦合模理论获得的理论值与我们FBG实验数据的吻合度达到了95.23%的准确率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/aa8fe03e18aa/sensors-20-01014-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/af1f7b49fb5b/sensors-20-01014-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/b3f4ac406241/sensors-20-01014-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/7097b58731a0/sensors-20-01014-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/1dc7999bb759/sensors-20-01014-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/81b4e145c95d/sensors-20-01014-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/d27e4d615e4c/sensors-20-01014-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/101c03d7c8a6/sensors-20-01014-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/e6b04c2cafaa/sensors-20-01014-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/aa8fe03e18aa/sensors-20-01014-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/af1f7b49fb5b/sensors-20-01014-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/b3f4ac406241/sensors-20-01014-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/7097b58731a0/sensors-20-01014-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/1dc7999bb759/sensors-20-01014-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/81b4e145c95d/sensors-20-01014-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/d27e4d615e4c/sensors-20-01014-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/101c03d7c8a6/sensors-20-01014-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/e6b04c2cafaa/sensors-20-01014-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c93/7070743/aa8fe03e18aa/sensors-20-01014-g009.jpg

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