Interactive analysis of OBN data

Ocean bottom node (OBN) surveys, which offer clear imaging advantages, are steadily supplementing (and sometimes replacing) surveys acquired with conventional streamers. This is driven by their ability to record a wider and richer azimuth of high quality data, over complex and challenging subsurface regimes. As this technique also is becoming cost-effective, we see that several large energy companies are now trending towards more OBN surveys, and "repeat OBN" for time-lapse projects. One challenge, however, of this type of acquisition, is that their subsurface geometry and bin size are, in general, irregular in shape and fold; to a much higher degree than conventional streamer seismic or Ocean Bottom Cable (OBC). With today’s best available technology, this means that the raw data needs to be regularized before visualization and processing, which leads to high storage overheads and irreversible loss of data and a less accurate subsurface analysis. Big energy companies are therefore seeking out a solution that can enable both more accurate and cost-effective analysis of OBN survey datasets, and efficient data handling for newer hybrid surveys that combine OBN and streamers. The goal of the project is hence to develop innovative approaches that eliminate the need for data regularization before processing and visualization. This is expected to result in significantly improved resource delineation and extraction, as well as enhanced decision-making processes for energy companies. Increased use of full-azimuth seismic will also benefit all industries relying on geophysical data. The project is a collaboration between Sharp Reflections and Aker BP, with research support from the German Fraunhofer Institute.

The project seeks to address challenges associated with irregular seismic data, especially that acquired through the use of OBN surveys, which have become increasingly prolific in seismic acquisition and display an especially high degree of irregularity through the development of innovative approaches that eliminate the need for data regularisation before processing and visualisation, substantially increasing accuracy in subsurface analysis. Increased accuracy in subsurface analysis will enable O&G companies to extract more resources from existing mature fields, as well as increasing accuracy in new well placements, effectively reducing risk and CO2 emissions associated with oil and gas exploration and extraction. The main objectives of this project include the development of robust algorithms and processing techniques that can handle irregular and incomplete prestack seismic trace data. This involves data pre-processing, noise removal, data interpolation, and the design of algorithms for processing and visualisation of irregular seismic data natively. Another key aspect of the project is the exploration of adaptive resolution and binning techniques for irregular seismic data. The goal being to enable dynamic adjustments of data resolution and binning in order to optimize data representation and analysis. This will involve researching data organization methods, development of algorithms, and representative selection techniques within each bin. The project also emphasizes the visualization of irregular data, seeking to develop advanced visualization techniques and tools that can effectively represent and interpret irregular seismic data without the need for regularisation. The focus being to preserve the inherent complexities of the data and enabling interactive exploration and navigation of irregular datasets.