PVT Properties for Yemeni Reservoirs Using an Intelligent Approach
PVT empirical correlations and Artificial Intelligence (AI) techniques become the best alternative when laboratory PVT analysis is not ready or very expensive to obtain. The objective of this paper is to determine the most frequently used oil viscosity (μo), formation volume factor (βo), and gas s...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
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Institute of Electrical and Electronics Engineers Inc.
2021
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125091614&doi=10.1109%2fIEEECONF53624.2021.9668185&partnerID=40&md5=7fd2a30f9e29746e3f2619ccf3928557 http://eprints.utp.edu.my/29136/ |
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| Summary: | PVT empirical correlations and Artificial Intelligence (AI) techniques become the best alternative when laboratory PVT analysis is not ready or very expensive to obtain. The objective of this paper is to determine the most frequently used oil viscosity (μo), formation volume factor (βo), and gas solubility (Rs) PVT properties of Yemeni reservoirs using the bottom hole fluid samples from different wells such as Well-BSWS-1, Well-BSWS-2, Well-BSWS-3, and Well-BSWS-4. Both Fuzzy Logic (FL) technique and a set of statistical error analysis were used to validate and compare the performance and accuracy of the generated reservoir fluid properties correlations. A total of 200 data sets of different crude oils from Yemeni reservoirs were used. The accuracy of the new Fuzzy Logic (FL) was compared with existing real measured bottom hole fluid samples data sets. The graphical plots showed that the predicted oil viscosity, formation volume factor, and gas solubility Fuzzy Logic curves have excellent matching with the experimental curves. © 2021 IEEE. |
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