A Deep Learning–Based Dual Latent Space Method for the Estimation of Physical Flow Properties From Fibre-Optic Measurements

Distributed fibre-optic sensing (DFOS) technologies have emerged as cost-effective high-resolution monitoring alternatives over conventional geophysical techniques. However, due to the large volume and noisy nature of the measurements, significant processing is required and expert, fit-for-purpose tools must be designed to interpret and utilize DFOS measurements, including temperature and acoustics. Deep learning techniques provide the flexibility and efficiency to process and utilize DFOS measurements to estimate subsurface energy resource properties. We propose a deep learning-based dual latent space method to process distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) measurements and estimate the injection point location and relative multiphase flow rates along a flow-loop equipped with a DFOS unit. The dual latent space method is composed of two identical convolutional U-Net AutoEncoders to compress and reconstruct the DAS and DTS data, respectively. The AutoEncoders are capable of determining an optimal latent representation of the DAS and DTS measurements, which are then combined and trained using one experimental trial and used to estimate the physical flow properties along five different test experimental trials. The predictions are obtained within 7 ms and with over 99.98% similarity and less than $3.68\times {10}^{-9}$ absolute error. The method is also shown to be robust to Gaussian noise and can be applied to different multiphase scenarios with a single pre-training procedure. The proposed method is therefore capable of fast and accurate estimation of physical flow properties at the laboratory scale and can potentially be used for rapid and accurate estimation in different laboratory or field subsurface energy resource applications.