A novel velocity band energy workflow for fiber-optic DAS interpretation and multiphase flow characterization.

Distributed fiber-optic sensing continues to gain widespread adoption in the energy industry because of the numerous benefits it offers for real-time surface and subsurface monitoring of pipelines, wellbores, reservoirs, and storage infrastructure. In this study, we introduce a novel workflow to analyze optical fiber-based distributed acoustic sensor (DAS) data, which takes into account the speed of sound for a certain phase to filter the acoustic energy or signal contributed by that phase. This information is then utilized for the characterization of multiphase flow.

Well-scale demonstration of distributed pressure sensing using fiber-optic DAS and DTS.

In this study, we used data from optical fiber-based Distributed Acoustic Sensor (DAS) and Distributed Temperature Sensor (DTS) to estimate pressure along the fiber. A machine learning workflow was developed and demonstrated using experimental datasets from gas-water flow tests conducted in a 5163-ft deep well instrumented with DAS, DTS, and four downhole pressure gauges. The workflow is successfully demonstrated on two experimental datasets, corresponding to different gas injection volumes, backpressure, injection methods, and water circulation rates.