Distributed Acoustic Sensing (DAS) is gaining attention as a potential next-generation technology for subsurface monitoring in the oil and gas industry. Through a collaboration between Aker BP and Shearwater, supported by funding from the Research Council of Norway (RCN), researchers are working to advance DAS from a proof-of-concept technology into a practical solution for large-scale 4D seismic monitoring.<\/p>\n\n\n\n
The project, supported by NOK 20 million in public funding, aims to evaluate how fiber-optic sensing technology can complement existing seismic monitoring methods and improve the efficiency of subsurface data acquisition.<\/p>\n\n\n\n
Conventional reservoir monitoring commonly relies on technologies such as Ocean Bottom Nodes (OBN) and towed streamer seismic surveys. These methods provide high-quality subsurface information but can involve significant operational complexity, time requirements, and costs.<\/p>\n\n\n\n
DAS offers an alternative approach by transforming existing fiber-optic cables into dense seismic sensing arrays. This capability allows operators to collect seismic information across large areas without requiring extensive deployment of traditional sensing equipment.<\/p>\n\n\n\n
By utilizing existing fiber infrastructure, DAS has the potential to reduce acquisition time, lower operational costs, and decrease the environmental impact associated with seismic surveys.<\/p>\n\n\n\n
The Aker BP and Shearwater project focuses on developing DAS into a reliable field-ready monitoring technology. Over a four-year development period, the project will investigate several key areas, including:<\/p>\n\n\n\n
These efforts aim to improve the ability of operators to obtain high-value subsurface insights while reducing the cost and complexity of seismic monitoring.<\/p>\n\n\n\n
The development of DAS is not intended to completely replace existing technologies such as OBN. Instead, DAS can provide additional flexibility by complementing conventional monitoring approaches.<\/p>\n\n\n\n
The combination of DAS and OBN could enable more frequent seismic surveys, faster data acquisition, and improved understanding of reservoir changes over time. This approach may be particularly valuable for applications such as mature oil fields, carbon capture and storage (CCS), and geothermal projects, where continuous and cost-effective monitoring is important.<\/p>\n\n\n\n
A key component of the project is the integration of advanced computing and machine learning technologies. The developed workflows will be incorporated into Shearwater\u2019s geophysical processing platforms, including advanced seismic imaging tools and machine learning frameworks.<\/p>\n\n\n\n
AI-based methods will be used to analyze subsurface uncertainty and improve interpretation of complex seismic datasets. Combined with GPU-powered computing, these technologies are expected to support faster processing and scalable monitoring solutions.<\/p>\n\n\n\n
Beyond improving efficiency, DAS-based monitoring could contribute to reducing the environmental footprint of offshore seismic operations. By decreasing the need for large-scale survey campaigns and enabling more frequent data collection, DAS has the potential to reduce emissions and operational risks.<\/p>\n\n\n\n
The project represents a broader industry movement toward smarter, more sustainable subsurface monitoring methods by combining fiber-optic sensing, artificial intelligence, and high-performance computing.<\/p>\n\n\n\n
By 2029, Aker BP and Shearwater aim to demonstrate an integrated DAS monitoring workflow capable of supporting oil and gas operations, CCS projects, and geothermal developments with improved efficiency, reliability, and scalability.<\/p>\n\n\n\n