Seismic amplitude fidelity study for quantitative analysis and its relation to stack-based signal-to-noise ratio estimation using the SEAM Arid model, with application to field data from the Powder River Basin

Abstract

Through my research I develop and implement quantitative tools to assess the seismic amplitude fidelity for quantitative interpretation in several land acquisition designs, and establish a signal-to-noise ratio (SNR) threshold for adequate quantitative analysis in complex near-surface environments. The first part of the research uses data from the synthetic SEAM Arid model, which simulates typical desert environment near-surface features. I use this data from the model to develop the necessary assessment tools of evaluating amplitude fidelity for quantitative interpretation. I investigate the impact of near-surface features and acquisition geometries on amplitude information and how these factors relate to interpretation of structural and amplitude-dependent features. Additionally, I explore whether the stack-based SNR estimations associated with different seismic stacks can indicate the suitability of seismic volumes for quantitative interpretation. The first step of the project plan examined equalizing the amplitude range of the different seismic volumes to facilitate a one-to-one comparison. I then performed conventional seismic interpretation and attribute analysis to identify and map the structural and amplitude-dependent features, mainly targeting two shallow channel systems and two deep shale geobody accumulations. I also conducted quantitative amplitude analysis by calculating the standard deviation of the amplitudes relative to the reference seismic volume with the best overall SNR. This allowed me to evaluate the preservation of amplitude information and identify potential areas where the signal may be compromised. Furthermore, I created a quality factor metric for each seismic volume that assessed how well various seismic attributes can map the four subsurface targets compared to the reference seismic volume. Finally, I correlated the quantitative metrics, i.e., the quality factors and standard deviation of amplitudes, with the SNR volumes to determine whether the latter are indicative of seismic data suitability for robust quantitative seismic interpretation. By combining these methods and procedures, I was able to comprehensively evaluate the seismic data and provide insights into the effects of near-surface features and acquisition geometries on quantitative seismic interpretation. As a result, I provided quantitative metrics for optimizing seismic survey designs tailored to interpreter goals by setting a target SNR value that is sufficient for interpretation, and an acquisition geometry that is suitable for complex near-surface environments. I also determined that effective mapping of structural and amplitude-dependent features requires a minimum SNR of 6 dB, as SNR values below this threshold tend to obscure amplitude information. Moreover, I determined that receiver arrays are superior to single sensor receivers in producing high-quality seismic images and handling near-surface noise and scattering. I alsofound that areas with low standard deviation of amplitudes relative to the reference volume are associated with successful mapping of subsurface features and high SNR values. I also observed that the quality factor metric suggests the significance of dense acquisition designs for accurate mapping of amplitude-dependent features in shallow targets, whereas all tested acquisition geometries were proficient in mapping deeper targets due to high reflectivity. Finally, I observed a correlation between the stack-based SNR estimations and the quantitative metrics, demonstrating that stack-based SNR provides valuable insights into the suitability of seismic data for robust quantitative seismic interpretation. In the second part of the research, I implemented some of the developed assessment methods on Non-Uniform Optimal Sampling (NUOS) field data from the Powder River Basin. I tested three different seismic angle stacks of the same study area. I first estimated a cross-correlation-based SNR over four well locations and two depth levels. I then calculated the amplitude standard deviation of the different seismic stacks in relation to the synthetic seismograms at the four well locations. Finally, I evaluated the performance of the different seismic stacks in predicting the reservoirs’ sand presence and their porosities using the results from the P-impedance inversion volumes. As a result, I determined that the NUOS method has likely not compromised the amplitude fidelity for characterizing the main reservoirs, and that the high noise level in the eastern part of the survey was mainly due to active drilling and completion operations, not due to insufficient seismic sampling.