Literature Review
Several methods exist that are used to identify and characterize fractures (Burnett, 2011). Seismic method is the most effective method of identifying aligned fracture sets. Aligned fracture sets render a rock seismically anisotropic; this implies that the velocities of seismic waves moving through the rock will depend on their direction of propagation (Verdon & Kendall, 2010). The way through which seismic anisotropy manifests itself includes azimuthal variation in reflection amplitudes (Hall & Kendall, 2000). Fromme (2011) investigated the scattering directivity of the A0 Lamb wave mode at through-thickness defects in aluminum plate from FE simulations (Zhou, Li, Mao, Mevel & Ou, 2013). There was a close correlation between the results. This paved way for further investigation of influence of relative defect size and depth, wavelength, incidence angle and plate thickness on fracture process. The findings from this investigation revealed that sensitivity for defect detection depends on the chosen investigation presented in the contribution (Fromme, 2011).
Engineers use two methods to extract fracture information from P-wave attributes. These are narrow-azimuth stacking and full-azimuth surface fitting. Full-azimuth surface fitting fits an elliptical surface to data from the available azimuths and off-sets by the technique of least-square fitting (Qian, Li, Main & University of Edinburgh, 2010). Narrow-azimuth stacking method divides the data into several narrow-azimuth volumes. Here, engineers choose 18, with 100 azimuthal bins. Xu, Li, Main & University of Edinburgh (2012) carried out a detailed analysis of a twenty-kilometer square 3D dataset, which was acquired from a physical modeling experiment. Results from the findings confirmed Azimuthal variations of P-wave attributes, which confirmed the presence of the numerical modeling results (Li, X., Liu, E., Liu, Y.-J., & Shen, 2003). The acquisition footprint influenced the results from the narrow-azimuth stacking method. In contrast, the results from the full-azimuth surface fitting agreed with the physical parameters.
The application of seismic data has been useful in identifying open, fluid-filled fractures in several reservoirs throughout the world (Roberts et al 2001). Smith and McGarrity (2001) argued that the SFD method had the ability to capture fracture information in a reservoir. This confidence has increased through using the 3D data technology in more than fifty projects across the world. Nikravesh, Aminzadeh, and Zadeh (2003) noted that SFD shows fractures of equal strike and intensity as indicated by other methods used to identify open fractures. Open fractures include fluid flow in the reservoir, mud-loss, borehole, production, stress and measurements (Chopra, Marfurt & Society of Exploration Geophysicists, 2007). These results imply that the SFD technique identifies the orientation and relative intensity of open and fluid-filled fractures (Miller, Bradford & Holliger, 2010). Engineers could do seismic fracture analysis using the existing pre-stack 3D seismic surveys with the help of the SFD technique. This is only possible if these surveys have sufficient azithumal coverage (Maloney, Davies, Imber & King, 2012).
The existing technologies can be used together with the measurements of anisotropy between two or more wells to identify the flow caused by fractures around the wells (American Society of Civil Engineers, 2008). The information collected can be turned into reservoir parameters, which describe the features of the fractured reservoir (American Society of Civil Engineers, 2008). This information can be imported to the reservoir simulator to assess the complex drainage patterns associated with the fractures and generate a reservoir development plan. Studies have consistently revealed that shear can occur above significant faults within the mechanical stratigraphy; this can lead to the creation of domino style fault arrays (International Symposium on Coastal Engineering Geology & Huang, 2013). Gravitationally driven extension can be separated into two major possibilities; part of the slope failure and a period of growth faulting due to sediment loading and gravitational processes (Xu, Li, Main & University of Edinburgh, 2012). The nature of basal detachment unit determines the structural styles of deltaic systems (Miller, Bradford & Holliger, 2010). However, geologists have not found out the exact factors that influence the activation of new detachments. Further research into the footwalls of basin systems is required to understand how 3D seismic data can be used to detect fractures.
References
Burnett, W. A. (2011). Multiazimuth velocity analysis using velocity-independent seismic imaging. Austin: University of Texas Publisher
Chopra, S., Marfurt, K. J., & Society of Exploration Geophysicists. (2007). Seismic attributes for prospect identification and reservoir characterization. Tulsa, OK: Society of Exploration Geophysicists.
Fromme, P. (2010). SHM of large structures using guided waves for crack detection. Review of Progress in Quantitative Nondestructive Evaluation, 30, 1507-1603. University College of London: Department of Mechanical Engineering.
Hall, S. A. & Kendall J. M. (2000). Constraining the Interpretation of AVOA for fracture characterisation. Anisotropy 2000: Fractures, Converted Waves and Case Studies. 107-144
International Symposium on Coastal Engineering Geology, & Huang, Y. (2013). New frontiers in engineering geology and the environment: Proceedings of the International Symposium on Coastal Engineering Geology, ISCEG-Shanghai 2012. Berlin: Springer.
Li, X., Liu, E., Liu, Y.-J., & Shen, F. (2003). Fracture detection using land 3D seismic data from the Yellow River delta, China. The Leading Edge, 22, 680-683.
Maloney, D., Davies, R., Imber, J. & King, S. (2012). Structure of the footwall of a listric fault system revealed by 3D seismic data from the Niger Delta. Journal of Research Basin, (24), 107-123.
Miller, R. D., Bradford, J. H., Holliger, K. (2010). Advances in near-surface seismology and ground-penetrating radar. Tulsa, OK: Society of Exploration Geophysicists.
American Society of Civil Engineers (2008). Sinkholes and the engineering and environmental impacts of karst: Proceedings of the eleventh multidisciplinary conference, September 22-26, Tallahassee, Florida. Reston, Va: American Society of Civil Engineers.
Nikravesh, M., Aminzadeh, F., & Zadeh, L. A. (2003). Soft computing and intelligent data analysis in oil exploration. Amsterdam: Elsevier.
Qian, Z., Li, X.-Y., Main, I., & University of Edinburgh. (2010). Analysis of seismic anisotropy in 3D multi-component seismic data. Austin: University of Texas.
Smith, R. L. & McGarrity, J. P. (2001). Craking the fractures from seismic anisotropy in an offshore reservoir: The Leading Edge, 20, 19-26.
Verdon P. J. and Kendall J. M. (2010). Detection of Multiple fracture sets using observations of shear-wave splitting in microseismic data. Geographical Prospecting, 59, 593-608.
Xu, Y., Li, X.-Y., Main, I., & University of Edinburgh. (2012). Analysis of P-wave seismic response for fracture detection: Modelling and case studies. Great Britain: University of Edinburgh.
Zhou, W., Li, H., Mao, C., Mevel, L., & Ou, J. (2013). Seismic Damage Detection for a Masonry Building Using Aftershock Monitoring Data. Advances In Structural Engineering, 16(4), 605-618.
