Search

Source-over-spread acquisition design is a recent development in marine seismic acquisition, aiming to improve imaging of the shallow subsurface. Contrary to conventional towed-streamer acquisition, source-over-spread acquisition records the direct wave below the source. We propose an inversion algorithm that takes advantage of the recordings of the direct wave in source-over-spread acquired seismic data to estimate notional source signatures for each air gun. The obtained notional source signatures from the inversion can be used to estimate directional far-field signatures, similar to the approach of notional source signatures estimated from measurements in near-field hydrophones. The forward modeling of the algorithm is based upon a physical modeling of the air bubble created by each air gun in the source array. A damped Gauss-Newton approach is used as a local search algorithm to minimize the difference between the recorded direct wave and the modeled direct wave from the forward modeling. The inversion is first carried out for a low-frequency part of the data, before higher frequencies are included in the inversion. Typical inversion parameters are empirical damping factors for the bubble oscillations and firing time delay for each air gun. Variations in streamer depth are taken into account and a constant sea surface reflection coefficient is also estimated as a by-product of the inversion. The algorithm is tested on shallow and deepwater data sets acquired with CGGs acquisition design called TopSeis. The notional source signatures are used in a designature flow and compared with designature results with notional source signatures from near-field hydrophone measurements. The results indicate that utilizing the direct wave in source-over-spread seismic can provide more accurate estimation of the strongest part of the bubble for deepwater data sets. To improve bubble estimation further, one may add more empirical factors to describe the oscillations of the bubble in the forward operator. It was furthermore observed that for shallow water data sets, subsurface reflections contaminated the bubble train of the direct wave. This affected the stability of the inversion, even when the water bottom reflection was included in the forward modeling.

3D exploration seismic data is increasingly being utilized for geohazard studies in the offshore oil and gas industry. An important criterion for the success of this approach is that the seismic data contain the broadest frequency bandwith possible. This study aims to display the potential of old vintage 3D exploration data in geohazard studies using targeted broadband processing. The processing workflow delivers a n near surface volume suitable for geohazard assessment. A Central North Sea data set, acquired in 1991 by Geco-Prakla using a dual source/dual streamer-configuration, was processed using best practice commercial processing technology to image a shallow gas hazard. Raw shot gathers displayed decent quality, but suffered from seismic ghosts, low frequent linear noise trends and multiples. The data was source designatured using a custom signature deconvolution successfully recovering frequencies in 80-100 Hz range. A K-filter followed by a Radon filter was applied, effectively mitigating the main linear noise content. Receiver deghosting was perfomed using least squares linear Radon equations showing enhanced imaging, but limited improvement in the frequency spectrum arguably due to natural notch diversity in the acquisition stage. Demultiple was performed using Tau-p gap deconvolution, before binning and regularization was applied. Migration was applied using Kirchoff time algorithm. The work demonstrates the use of 3D seismic exploration data in a shallow gas geohazard study. Using targeted processing we were able to define the extent and magnitude of the shallow gas hazard, effecetively illustrating its value and potential in proactive risk assessment for the offshore hydrocarbon exploration and production operation.