RMS Seismic Attributes with RGB Color Blending Technique for Fault Interpretation

• Main Authors: Gritsadapong Leaungvongpaisan, Pisanu Wongpornchai
• Language: English
• Published: Science Faculty of Chiang Mai University 2020
• Subjects: Fault detection; RGB color blending; RMSseismic attribute
• Online Access: http://epg.science.cmu.ac.th/ejournal/dl.php?journal_id=7610; http://cmuir.cmu.ac.th/jspui/handle/6653943832/67356
• Institution: Chiang Mai University

For many generations, the use of seismic attributes has enabled the seismic interpreters to better understand the geological information in the subsurface. The objective of this study is to determine the fault and polygonal fault patterns to aid the structural interpretation inside the 3D Bonaventure seismic survey in Western Australia. In this study, three Root Mean Square (RMS) seismic attributes each with a different number of seismic samples are combined using the Red Green Blue (RGB) color blending technique to highlight the fault patterns using the Petrel platform. As the result, the fault images from the seismic time slices could be captured from the top to the bottom of seismic cube with the adjustment of the RGB color blending transformation function. The stretch and squeeze of RGB color blending functions provide a significant improvement on the fault delineation and visualization, especially at the bottom part of seismic cube, where the signal to noise ratio is usually low. The fault patterns from the RGB color blending technique are directly compared to the results from other structural seismic attributes such as variance, amplitude contrast and chaos at the same seismic time slice. The same fault trends, from the north to south direction, could be determined. The proposed method could be an alternative approach for fault interpretation in terms of t fault delineation and computation time. Finally, the ultimate goal of seismic attribute analysis is to improve the accuracy of seismic interpretation in order to reduce the uncertainty for hydrocarbon exploration and production.