Recent years have seen many rapid developments in subsurface imaging, especially in velocity model building. This means that not only can many older data sets be reprocessed to a standard approaching that of modern data sets, as a result of advances in areas such as deghosting and designature, but even data sets acquired relatively recently can benefit from reprocessing. As technology continually evolves, there is often value in reprocessing seismic data multiple times, ensuring it remains a valuable asset.
Many thousands of square kilometres of seismic data around the world are suitable for reprocessing. Many of these data sets provide patchwork coverage, with different orientations and parameters, which would benefit from being combined and reprocessed as contiguous volumes. In many cases, they may be improved by infilling gaps with new acquisition. In more challenging areas, the data may be enhanced by over-shooting with new seismic acquired at a different azimuth, which can then be processed with the older data to deliver the benefits in illumination and multiple attenuation that multi-azimuth data provides.
The Cornerstone Evolution reprocessing project in the Central North Sea demonstrates the value achieved by reprocessing a large number of existing data sets in conjunction with newer acquisition. The Cornerstone data set consists of several phases of acquisition, covering over 35 000 km2 (Figure 1), built up over more than a decade. These surveys are not a random patchwork (like some reprocessing programmes), but rather were intentionally acquired in stages as a regular grid of multi-client projects, incorporating the latest advances in acquisition technologies as they were developed. The earlier surveys were all acquired with an approximate north-south orientation, while the most recent were acquired east-west, in some places overlying the previous surveys to provide dual-azimuth (DAZ) data.
Figure 1. Map showing the Cornerstone area, showing the areas of BroadSeis and dual-azimuth data.
The Central North Sea is a mature basin, yet still rich in opportunities for the discovery and development of new fields. There are many prospective intervals, with hydrocarbons encountered within three main sequences: Upper Jurassic sandstones, Cretaceous chalks (on the Norwegian side of the Central Graben) and Lower Tertiary submarine fan systems. Advances in technology have continued to allow new play models to be explored and new discoveries to be made. The development of broadband technology has enabled new stratigraphic traps and subtle structural closures to be delineated, and reservoir development and hydrocarbon recovery have been enhanced by more information about local facies variations and reservoir compartmentalisation. The higher frequencies in broadband data push the limits of amplitude tuning effects and help to resolve thin beds and pinch-outs that have previously been problematic to image. The low frequencies also play an important role by reducing sidelobe interference and helping in the interpretation of subtle facies transitions.
The Central North Sea suffers from a number of geophysical challenges, including shallow anomalies, heavy multiple contamination and sharp velocity contrasts, all of which may be resolved by modern processing techniques. Although the surveys that make up Cornerstone have already been recently reprocessed in depth (2015), the advances made in full-waveform inversion (FWI) for modelling velocity, visco-elasticity (Q) and anisotropy mean that considerable improvements can already be achieved by reprocessing again. The previous reprocessing started from archived pre-processed data, but the new Evolution project is reprocessing the data completely, starting from the field tapes, to gain the maximum advantage from improvements in signal processing such as 3D designature and deghosting. The project also benefits from advances in demultiple, especially the move from predictive to model-based techniques. Two areas of Cornerstone have been reprocessed as a priority, one of which is in the DAZ area and is the example discussed here.
Read the article online at: https://www.oilfieldtechnology.com/exploration/15032019/leveraging-legacy-data/
You might also like
The machine learning models that have been developed can assist geologists and geophysicists in reconstructing missing well logs, making lithology predictions, calculating shale content, and mapping potential undiscovered reservoir areas.