Inversion algorithm of fiber bragg grating for nanofluid flooding monitoring
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 me...
| Main Authors: | Yahya, N., Nyuk, C.M., Ismail, A.F., Hussain, N., Rostami, A., Ismail, A., Ganeson, M., Ali, A.M. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.23398 / |
| Published: |
MDPI AG
2020
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079573251&doi=10.3390%2fs20041014&partnerID=40&md5=8c982f1091f7820b40474f888255aa91 http://eprints.utp.edu.my/23398/ |
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| Summary: |
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 Fe3O4 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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. |
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