@article{eage:/content/journals/10.3997/1365-2397.fb2022007, author = "Tellier, Nicolas and Ollivrin, Gilles and Laroche, Stéphane and Donval, Christophe", title = "Mastering the Highest Vibroseis Productivity While Preserving Seismic Data Quality", journal= "First Break", year = "2022", volume = "40", number = "1", pages = "81-86", doi = "https://doi.org/10.3997/1365-2397.fb2022007", url = "https://www.earthdoc.org/content/journals/10.3997/1365-2397.fb2022007", publisher = "European Association of Geoscientists & Engineers", issn = "1365-2397", type = "Journal Article", abstract = "Abstract Increasing the productivity of seismic acquisition projects has been a key goal for contractors and operators for decades now. It remains topical, mainly in respect of efforts to increase a given project’s trace density for a cost in line with the resulting reservoir quality uplift. The Middle East and North Africa have traditionally pioneered the development and introduction of advanced productivity techniques, given the presence of large hydrocarbon deposits located beneath open terrain with limited anthropogenic activity. After the successful introduction of several high-productivity methods in the region, two of them – DS4 and Unconstrained Vibrators – have won recognition and are now standard on most projects. While the level of productivity these methods enable is unprecedented, they still show some scope for improvement: the productivity of DS4 is not the highest achievable, whereas the aggressive blending associated with unconstrained vibrators acquisitions can affect the overall imaging quality. In this paper, we introduce a new high-productivity methodology, at the confluence of the two aforementioned methods while addressing their limitations. xDSS makes it possible to reach the ultra-high productivity enabled by unconstrained vibrators, while preserving the blended acquisition golden rules ‘randomness in time and space’ and ‘sparseness in the frequency – wavenumber domain’. The automated observance of these two rules makes it possible to get as close as possible to the maximum achievable source productivity, while delivering to the processors a deblending-friendly dataset.", }