Document Type
Article
Publication Date
6-1-2023
Abstract
The distributed acoustic sensor (DAS) is a promising technology for real-time monitoring of wellbores and other infrastructures. However, the desired signals are often overwhelmed by background and environmental noise inherent in field applications. We present a suite of computationally inexpensive techniques for the real-time extraction of the gas signatures from noisy DAS data acquired in a 5163 ft. deep wellbore. The techniques are implemented on three well-scale DAS datasets, each representing multiphase flow conditions with different gas injection volumes, fluid circulation rates, and injection methods. The proposed denoising techniques not only helped in optimizing the gas slug signature despite the high background noise, but also reduced the DAS data size without compromising the signal quality.
Publication Source (Journal or Book title)
Applied Optics
First Page
E51
Last Page
E61
Recommended Citation
Tabjula, J., & Sharma, J. (2023). Feature extraction techniques for noisy distributed acoustic sensor data acquired in a wellbore. Applied Optics, 62 (16), E51-E61. https://doi.org/10.1364/AO.483253