This Challenge is looking for ways to improve processing of Distributed Acoustic Sensing (DAS) data to monitor flow velocity along pipelines efficiently.
A flow monitoring system. Image credit: Wazoku Crowd
In recent years, there has been a growing interest in exploring DAS for monitoring oil and gas flow in surface and downhole pipelines, not the least because of DAS’s resistance to harsh environments.
However, there is an urgent need to develop physics-based and/or machine learning-assisted signal processing workflows for continuous real-time interpretation of DAS distributed measurements.
The Challenge
Real-time, non-intrusive pipeline surveillance allows continuous optimization of the fluid flow without frequent and labor-intensive performance tests; it can also potentially identify production anomalies (e.g., underperforming inlets, excessive local water production) hours before they are noticed downstream.
However, the efficiency of online surveillance crucially depends on sensitivity and reliability of a monitoring system as well as the accuracy and robustness of processing of the collected data.
DAS is a sensing system based on light and consisting of laser and optical cables. In a typical case, the laser sends light pulses into the optical cable and then analyzes naturally scattered light returning to the receiver.
The fiber itself is the sensing element enabling spatially continuous measurements comparable to those obtained by single-component (1C) accelerometers or geophones. This allows to detect acoustic signals passing through the cable over long distances with a spatial resolution of a few meters.
Submissions to this Challenge must be received by 11:59 PM (US Eastern Time) on January 28, 2024.
Source: Wazoku