Survey Design, Processing Workflows, and Operational Best Practice for Distributed Acoustic Sensing in Geoscience Applications

Distributed Acoustic Sensing (DAS) has rapidly evolved from an experimental sensing approach into a practical operational technology for seismic imaging and reservoir monitoring in geoscience applications due to its ability to transform fibre optic infrastructure into dense arrays of seismic receivers. DAS systems enable high-resolution measurements in boreholes, trenches, and subsea environments, supporting applications such as vertical seismic profiling (VSP), 3D and 4D reservoir monitoring, and near-surface imaging. As DAS deployments continue to scale, the efficient management of large distributed datasets and the automation of acquisition and processing workflows have become increasingly important for reliable field operations and timely geophysical interpretation.

This presentation provides a practical overview of operational best practices for DAS-based geoscience surveys, focusing on automated processing workflows, real-time quality control, and scalable acquisition architectures. The discussion covers the end-to-end operational workflow from survey planning and equipment preparation through acquisition, data handling, processing, visualization, and delivery. Workflows for both active and passive acoustic monitoring are presented, including continuous recording, triggered acquisition, automated event and shot extraction, and real-time operational QC.

Particular emphasis is placed on the DAS interrogators, auxiliary acquisition systems, networking infrastructure, and real-time processing environments to support efficient field deployment and remote operations. Practical quality control methodologies are also discussed, including automated monitoring of acquisition health, timing synchronization, data integrity, signal consistency, and operational performance throughout survey execution.

In addition, the presentation explores the growing role of edge-based processing and intelligent data handling within DAS acquisition systems. Real-time visualization, data reduction workflows, and remote operational support are discussed as emerging requirements for modern fibre optic sensing deployments, particularly where large data volumes and several distrusted systems are involved.

By consolidating operational experience from a range of DAS deployments, this work demonstrates how robust acquisition design, automated processing workflows, and scalable operational architectures can improve acquisition reliability and maximize the value of fibre optic sensing technologies for geoscience applications.