Machine Learning for Automated Sand Transport Monitoring in a Pipeline Using Distributed Acoustic Sensor Data

Uncontrolled sand production presents a substantial challenge to wellbore and pipeline integrity and efficiency of hydrocarbon production operations, often leading to equipment damage and compromised productivity. Traditional sand detection methods on the surface alert operators to sanding issues, but they are often a lagging indicator of downhole sanding events and do not provide precise identification of the problematic reservoir zones. Addressing this limitation, this study harnesses a combination of efficient signal processing and machine learning (ML) to analyze data from optical-fiber-based distributed acoustic sensors (DASs), thus serving as the first instance (to the authors’ knowledge) of an automated and real-time approach to monitoring sand migration patterns and velocity estimation along a pipeline. The DAS data acquired from an experimental flow loop were analyzed using the developed algorithms, and the performance was evaluated for different flow speeds and sand ingress scenarios. The model training only required roughly 25% of the total data, and the remaining data were used to demonstrate the generalizability of the proposed ML models, through blind testing. Analysis of eight distinct experimental datasets provided a credible approximation of sand velocities, corroborating previous studies and theoretical expectations. Using the best-performing trained models, sand detection accuracies attained an average of 93.4% on blind testing data, along with sand velocity estimates with an average error of 10.1% from analytical results. The results from this study validate the use of DAS combined with ML for autonomous sand monitoring and flow characterization, both for boosting well performance and concurrently mitigating environmental hazards.