Seismic volume-based analyses are here defined as processes that are implemented on the seismic volume in its entirety in order to improve the imaging of faults planes and discontinuities in seismic reflectors or to understand the signal in and around faults in the data before seismic interpretation methods are applied. Analyses of the seismic volume were
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incorporated into Papers I and II. Here the steps in the seismic volume analysis portion of the fault analysis workflow will be defined (Fig. 3a).
3.1.1 Data conditioning
Data conditioning was only incorporated in Paper II to increase the signal to noise ratio in areas that were affected by the presence of shallow gas in seismic volume ST15M04. The workflow was aimed at attenuating noise in the seismic volume while also normalizing amplitudes and was applied in two steps (Gilani & Gómez-Martínez 2013). In areas where the amplitudes were identified as low due to the presence of shallow gas, an aggressive noise cancellation and amplitude scaling were applied. In areas with good signal, a more passive noise attenuation with no amplitude scaling was applied. This data conditioning workflow was run in the software, Geoteric™. For more information on data conditioning see Paper II, Section 3.1 and/or Gilani and Gómez-Martínez (2013).
3.1.2 Attribute study
Fault enhancing attributes were applied on both ST15M01 and ST15M04 in Papers I and II to aid in the imaging of faults in the seismic data. The three most important fault enhancing attributes in this thesis were tensor, semblance and dip (see e.g. Botter et al. 2016b). Tensor is defined as a measurement of the local reflector orientation which is generated using a locally oriented symmetric tensor (Bakker 2002). Semblance (similar to coherency or variance) is defined as a measurement of reflector discontinuity and measures lateral changes to reflectors in the seismic volume (Marfurt et al. 1998). Dip is the measurement of the inclination of the seismic reflector with respect to the horizontal and is commonly applied to image stratigraphic or structural edges in the seismic volume (Barnes 2000; Marfurt 2006). These structure enhancing attributes were all generated in Geoteric™ using specific window sizes suitable for large faults. The envelope attribute (a measurement of amplitude strength;
Bakker 2002) was applied in Paper I to analyse the changing amplitude
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strength with increasing incidence angle. More specific information can be found for all attributes in Papers I and II.
Attributes were also combined to generate multi-attribute blends (e.g.
Purves & Basford 2011; Iacopini et al. 2012; Botter et al. 2016a). The attributes were equally weighted in a single CMY combined volume and a single colour: cyan, magenta and yellow (CMY) were assigned to tensor, semblance and dip respectively. Anywhere where all colours in the blend overlap is defined as black in the colour bar and these areas were assumed to represent faults in their fullest extent relative to what is visible in each individual attribute. Since none of the seismic volumes used in this thesis imaged a fault plane the attributes measurement of the discontinuity fault imaging.
3.1.3 Fault facies classification
Unsupervised seismic fault facies were only classified in Paper II in order to further understand the seismic signal coming from faults. The term unsupervised means that they are not facies in the classical sense of being calibrated to rock types and lithological properties, but are based on a seismic signal classification (Iacopini et al. 2012). Fault facies were classified by applying a fault enhancement filter on a greyscale volume of the CMY colour blend volume (tensor, semblance and dip). Fault enhancement is a Gaussian filter that detects and enhances edges whilst also suppressing noise in the data volume (Chopra & Marfurt 2007). The highest values in the fault enhancement volume were subdivided by value into four unsupervised seismic fault facies representing specific seismic attribute responses. Fault facies were then analysed using opacity filtering (e.g. Iacopini & Butler 2011) and by cross-plotting the data according to attribute and fault facies. This was done in order to understand the relationship between the seismic signal and fault enhancement fault facies. For specific attribute parameters refer to Paper II.
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3.1.4 Amplitude study
The seismic amplitudes surrounding faults were analysed in Paper II in order to determine if there is a relationship between fault related folding observed in the seismic reflectors near faults and the amplitude magnitude of these reflectors. To complete this analysis structural modelling was used to subdivide chosen areas into grid cells. An RMS amplitude volume of the seismic was calculated using predetermined window sizes to match the grid cell dimensions. The RMS values were re-sampled back into the grid cells of interest. These data could then be cross plotted by distance to fault and magnitude of RMS amplitude in order to determine if a relationship between folding and amplitude could be established. For more information on the parameters used in this aspect of the fault analysis workflow, please refer to Paper II.
3.1.5 AVO attribute stack analysis
The blending of parameters from near, mid and far offset volumes into a single colour blend is termed AVO colour blends (Gomez 2015). Each attribute blend combines three equally scaled attributes of the near (red), mid (green) and far (blue) to create an RGB blend. In Paper I AVO attribute colour blends were made using the tensor attribute volumes of partially stacked and azimuthally separated data (Section 3.1.2) in order to understand how the imaging of faults differed with incidence angle and azimuth in the Snøhvit case study. The results are best viewed on a time slice and more information can be found in Paper I.
3.1.6 Incidence angle and azimuthal separation analysis
Incidence angle and azimuthal separation were aspects of processing that were applied to the Snøhvit case study in Paper I. The data were processed into east (E, only containing data from receivers located to the east of the source) and west (W, only containing data from receivers
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located to the west of the source) azimuth volumes. The data were also separated into near, mid and far incidence angle stacks. To analyse the effect of both incidence angle and azimuthal separation on the imaging of faults seismic attributes were run on both the E and W partial and full stacks and then compared. The reason for conducting this analysis was to see if the faults were imaged differently in the E versus W azimuth partial stack data. The results of these comparisons were used to speculate what might be causing the imaging difference, with respect to survey geometry. The use of seismic attributes in the analysis of E and W azimuth partial stack data was performed on the case study data and the results were compared with the 2D forward modelled reflection seismic data. Since the forward modelled data was 2D, the attributes could not be run on them, but a comparison of image quality was still possible.
3.1.7 Frequency study
An analysis of seismic frequency was conducted on both the case study and forward modelled data in Paper I. The analysis was applied to investigate the frequency content of the backscattered signal from within and around fault planes. In the case study, frequency decomposition blends were generated by equally scaling low, medium and high frequencies in an RGB frequency focused colour blend. In the modelled seismic data, an experiment was designed specifically to understand frequency (Paper I, section 2.2.2). To analyse the frequency content of faults in the modelled data, amplitude spectra were extracted from the middle of the fault, adjacent to the fault and ~800m from the fault on its HW side. The results of both the case study and the modelled experiment were compared. The details of these analysis can be found in Paper I.