Prior to any structural attribute analysis on 3D seismic data it is usually necessary to undertake noise cancellation to remove the noise and enhance data quality. We present the results of applying noise cancellation techniques to 3D seismic data and show how that can improve our imaging and analysis of faults. Furthermore we look at automated methods for improving the mapping of major and minor faults using a unique fault workflow. To be effective volumetric noise cancellation must attenuate noise whilst minimizing the loss of information in order to maintain what is genuine geology. To do this we systematically apply edge preserving methods for attenuating both random noise and high spatial frequency coherent noise that may be masking our true representation of faults. Seismic attributes such as Semblance can then be applied to identify discontinuities within the data which may correlate to structural faults. These types of discontinuity attributes are very sensitive to noise in the data, so running the algorithms on noise attenuated data can have a significant impact on the quality of the result and the scale of the faults that can be interpreted. Structurally oriented and adaptive filters significantly enhance the reflector continuity and improve lateral and vertical imaging of faults by attenuating higher frequency noise whilst preserving edges. We will present examples of the effect of noise cancellation on the complete fault workflow from initial attribute analysis to semi-automated fault extraction and analysis, and show the improvements in the fault interpretation with the application of noise attenuation.

3Current horizon interpretation techniques are based primarily on the use of seismic reflectivity. While there have been robust algorithms developed to work with seismic data, there are limitations and trade-offs with each of these approaches.In this presentation we investigate the merits of interpretation based on the use of a colour blend comprised of phase, amplitude and fault datasets. The colour blend used in this workflow is a Hue-Saturation-Value (HSV) blend. In this blend the hue/colour is controlled by the instantaneous phase, the saturation is controlled by the amplitude, and the value/blackness is controlled by a fault detect volume.Combining these three datasets provides a greater level of explicit information when interpreting an event. In standard cases this information can be inferred by the interpreter using secondary attribute volumes, or an autotracking algorithm performing extra calculations in the background. However, both of these approaches add extra overhead to the work being performed, reducing efficiency. Explicitly encoding phase, amplitude and fault information allows: Reduced incidence of cycle skipping  The ability to pick on a particular phase angle  Honouring of faults in autotracking  Increased visual information in manual interpretation These points will be reviewed through the interpretation of a number of 3D seismic datasets, with varying data quality and covering a range of geological settings.

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Given the recent increase of seismic data quality owing to improvements in seismic acquisition and processing, it is surprising to realise that the oil and gas industry is still using standard desktop screens with 256 colour resolution software displays, and for most of the seismic representations, using only three types of colour bars (peak-trough, grey scale or rainbow) for human interpretation, comprehension and decision making processes. Knowing that these displays show 0.000006% of the details captured in 32 bit resolution data, it is a wonder: is the oil and gas industry using the available data to its maximum potential to decrease the risk of drilling dry wells? Astronomy and medical imaging tackled these issues long ago and inspired by them, the oil and gas industry is able to use a 24 bit colour space for representing seismic data in a more appealing way. These innovative seismic data representations are called colour blends and are created using sources such as frequency decomposition products, angle stacks, edge attributes, 4D vintages or any other seismic attributes colour-coded with primary colours. Colour blends have not yet become mainstream due to availability of the tools. The cognitive cybernetics approach allows a more balanced input between data driven processes, interpreter skills and guidance, and has recently been made available for use with colour blends—a breakthrough in interpretation. This extended abstract shows recent advances in these two techniques and how they benefit to the geological and geophysical work based on a case study from the Australian and New Zealand sector.