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Experimental Study on Sand Detection and Monitoring Using Distributed Acoustic Sensing for Multiphase Flow in Horizontal Pipes

地质学 岩土工程 流速 流量(数学) 泥浆 环境科学 机械 环境工程 物理
作者
Rishikesh Shetty,Jyotsna Sharma,Mayank Tyagi
出处
期刊:Spe Journal [Society of Petroleum Engineers]
卷期号:29 (02): 1045-1060 被引量:10
标识
DOI:10.2118/218005-pa
摘要

Summary Excessive sand production in wellbores can cause significant damage to the downhole and surface equipment, which could reduce well productivity and cause loss of integrity. Conventional surface sand detectors provide a delayed indication of the onset of downhole sanding events. Additionally, they do not provide definitive information about the zones in the reservoir contributing to sanding. Distributed fiber-optic sensors can address these gaps by enabling real-time downhole monitoring simultaneously across the entire length of the installed fiber in the wellbore. In this study, we investigate the application of optical fiber-based distributed acoustic sensing (DAS) for real-time monitoring of sand migration patterns, detecting sand ingress location, and estimating sand slip velocity. The sand slip velocity is the difference in the flow velocity of sand particles and fluid velocity in slurry flow caused by drag forces. DAS was acquired on a horizontal experimental surface flow loop at different flow rates and sand ingress conditions for a fixed sand slurry concentration and sand particle size. DAS data were analyzed in the frequency domain using spectrums and spectrograms to investigate the frequency fingerprint of the flowing sand that enabled visualization of sand migration patterns in multiphase flow. Sand flow velocities were estimated using the DAS frequency band energy (FBE) profiles and compared with analytical models of sand transport. A reasonable comparison was observed for all six experimental data sets analyzed in this study. Comparison of results from 28 gal/min and 32 gal/min trials showed higher sand slip velocity at higher flow rates. Different sand ingress locations were detected using DAS spectrum analysis. The results demonstrate the successful application of DAS for in-situ sand monitoring and flow characterization that can enable targeted sand management and remediation.
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