Acoustic Sensors to Measure Speed of Oil Flow in Downhole Pipes

Vahabi, Nafiseh; Alabdullah, Mohammed H.; Selviah, David R.

doi:10.1109/iciea.2019.8833893

Cited by 3 publications

(1 citation statement)

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“…In last decade, the optical fibre sensors have been successfully developed and applied for flow measurement [5], [6], [7]. Optical fibre sensors for flow analysis in wells can do a single point measurement or multiple point measurements along the well.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks to Classify Oil, Water and Gas Wells Fluid Using Acoustic Signals

Vahabi

Selviah

2019

2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

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Identifying the fluid type and predicting the amount of each fluid in the fluid mixture within the well pipes are important for oil and gas production energy industry and borehole water supply. Therefore automating this process will be very valuable for the oil industry because it maximises the quality and quantity of extracted oil and reduces the cost. The current study contributes to our knowledge by addressing this important issue using machine learning algorithms. The presented paper investigates the classification algorithms that identify the fluid type in oil, water and gas pipes using acoustic signals. The datasets analysed in this study are collected from real oil, water and gas well pipes under the sea where there is no controlled environment and data contains lots of noisy signals due to unpredicted events under the sea. Data is recorded during 24 hours from Distributed Acoustic Sensors which is attached alongside the 3500 m of three well pipes: oil, water and gas. The acoustic dataset are in time-distance domain and are converted to frequency-wave number domain using 2D fast Fourier transform. The outcome of 2D fast Fourier transform is sampled and fed into Artificial Neural Networks and Conventional Neural Networks algorithms to classify each fluid type. Both algorithms are trained on three datasets (oil, gas and water) and tested on another dataset. The result of this study shows Artificial Neural Networks and Conventional Neural Networks algorithms classify the fluid type with the accuracy of 79.5% and 99.3% respectively when applied on the test dataset.

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Section: Introductionmentioning

confidence: 99%