Cloud computing for large-scale controlled-source electromagnetic inversions, Barents Sea, Norway
Karen Engell Savoretti, Stefan Dümmong, Berit Ensted Danielsen, Jan Oven Hansen, Mark Austin Read, Hans Rune Bue and Torgeir Wiik
Journal name: First Break
Issue: Vol 36, No 10, October 2018 pp. 69 - 74
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Price: € 30
Since the late 1990s, the continuous development and application of the Controlled-Source Electromagnetic (CSEM) technology have vastly improved its usage in exploration (e.g., Constable, 2010; Løseth et al., 2015; Zach et al., 2009). CSEM inversion results are the main input for interpretation of resistivity data, especially potential fluid anomalies. Complementary to other geophysical methods, e.g. Amplitude versus Offset (AVO), it can therefore be a valuable tool in hydrocarbon exploration. Yet, the unconstrained nature of the subsurface and non-unique¬ness of inversions is a tough predicament. For example, several features in the subsurface could cause high resistivity, such as abundant limestone, salt, basalt, mature source rocks or hydro¬carbon-filled sandstones (Baltar and Barker, 2015). As a result of enhanced data quality, data processing and inversion capabilities, together with improved workflows and accumulated experience, CSEM interpretation has become increasingly more robust (Buland et al., 2011; Karman et al., 2013; Løseth et al., 2014). The 3D Gauss-Newton (GN) inversion algorithm is a more robust method compared to other inversion types. GN thus provides a new, powerful methodology for generating marine CSEM resistivity images. Nonetheless, it has formerly been com¬putationally exhaustive to apply. With the Hoop area, Barents Sea, recently being nominated for the Barents Sea exploration licensing round, a tight deadline was given for the CSEM inversion of an expansive 11,000 km2, over 130 blocks (refer to Figure 1). Adopt¬ing the flexible and agile cloud system into the CSEM workflow, it has improved the 3D GN work efficiency and provided overall consistent results when compared with other CSEM inversion schemes. This paper will illustrate how a systemic workflow of CSEM 3D GN inversions together with efficient cloud computing, could lead to a more consistent interpretation of this data.