In this paper, we will share the recent work that was done to understand how bulk flow rates and fluid composition may be derived in single-phase and multi-phase flow by tracking the slopes (velocities) of coherent features detected using Distributed Acoustic Sensing (DAS). Both laboratory experiments and real field examples will be presented to demonstrate how velocity features can be detected and attributed to events such as slug flow or sound waves. Speed of Sound (SoS) analysis can in principle be used for determining changes in the fluid composition in multiphase flows, which provides opportunities to detect fluid interfaces and water or gas breakthrough. On the other hand, slowly moving features such as slugs or turbulent eddies can be used to derive bulk flow velocities, which may be used for injection or production profiling. The evaluation method directly derives velocities by Fourier transforming the raw DAS data in the temporal and spatial domains without applying any calibration steps. It can therefore be used to monitor flow in wells on a drive-by or continuous basis without a need for reference flow data.

This paper discusses the application of DAS for flow monitoring. While previous publications (Van der Horst et al (2013) focused on vertical and horizontal tight gas wells in North America, the focus here is on liquid producers and injectors in Brunei. Specifically, it was found that DAS has potential for zonal production and injection allocation across ICVs, monitoring interzonal inflow from the reservoir, monitoring artificial lift, tracking fluid transport through the well bore, detecting leaks, and monitoring wax build up or other types of deposition in the well.

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Within the Oil and Gas industry the use of Acoustics data for flow rate estimation is increasingly being explored. One technique is to consider the total spectral power of the signal within a specific frequency range, known as an FBE. The FBE, along with measured Flow rates, can then be used to build a simple regression model to estimate the flow rate. We collect acoustic data using Distributed Acoustic Sensing, DAS, and find that the recorded FBE generally contains some corrupted data and outliers. This may be due to well shut-in periods or other physical phenomena, or it may be due to issues in the DAS recording itself. These outliers can have a detrimental effect on the calibration of any predictive model and lead to biased flow predictions. We combat this by calibrating out model using Robust Regression techniques, such as Least Absolute Deviation, which are less influenced by outliers. Another practical concern is choosing the correct frequency band for the FBE. This can be done by evaluating the model performance on a training set, however we find that the signal quality within a band can diminish over time necessitating a change in the band used. Our challenge is to find a way to identify when a band is likely to be giving poor predictions. We do this by looking at the ratios between different FBE bands, we find that under normal conditions these are highly correlated, however for certain bands this correlation is lost over time. This can be used to determine when it is time to switch to use a different band. This paper contains the motivation and results of these techniques as they are applied to flow prediction in a gas producing well which has been part of a long-term flow monitoring project.

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