Lee, Wei Yi (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Hamidi, Rosita (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Ghosh, Deva (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS) | Musa, Mohd Hafiz (Centre of Subsurface Seismic Imaging, CSI, Universiti Teknologi PETRONAS)
Noise is the unwanted energy in a seismic trace opposed to the signals corresponding to reflected energy from the subsurface features. Since it can overlap with the main signals' energy and conceal the geological information, noise attenuation is one of the most important steps in seismic data processing. The most common method is frequency filtering. However, due to its limitations on separating the noise from signals, this method usually results in hurting the signal. Hence, it is important to develop an alternative method that can attenuate the noise without affecting the signal. Filters based on time-frequency analysis of the data can have a better separation of the noise from signal as they maintain the time localization of events while presenting their frequency content simultaneously. One of the recent approaches to time-frequency analysis of signals is the Empirical Wavelet Transform (EWT) which provides adaptive wavelet filter bank for signal analysis. In this paper, a filter is designed based on EWT for random noise attenuation and is applied on both synthetic and real data.
Hamidi, Rosita (Centre of Seismic Imaging and Hydrocarbon Prediction, Universiti Teknologi Petronas) | Ghosh, Deva (Centre of Seismic Imaging and Hydrocarbon Prediction, Universiti Teknologi Petronas)
Fault and fracture study has a great importance in hydrocarbon prospect exploration and development. Consequently, there have been lots of efforts to analyze the existence and extent of faults in subsurface layers using different methods and tools available to geoscientists; among which the seismic attributes have been proven to be efficient in detecting areas affected by faults and fractures. Seismic attributes help interpreters to highlight details focusing on the geological features of interest in seismic data. However, there are some limitations in the performance of these tools, as the algorithms are dependent on the seismic survey parameters, quality of the data and its existing patterns, and geology of the study area. Consequently, new strategies and algorithms are needed to improve the information obtained from the calculated attributes.
In this study, fault and fracture damage zone analysis is done on three – dimensional seismic data from Sarawak basin in Malaysia. Commonly used seismic attributes to detect such features including variance, dip – magnitude, curvature, and gradient – magnitude are applied. Next, spectral analysis, as a tool to identify events with different frequency content is used which can detect the patterns related to faulting and fracturing of the subsurface layers. The proposed method in this work is to examine the attributes’ performance on spectrally decomposed seismic cubes to unmask the details present at different frequencies. Accordingly, the seismic attributes are applied on the selected cubes, and the color blended cubes of the outputs are evaluated. As the results show, the new strategy reveals more detailed information that already exist in seismic data but cannot be distinguished because they are concealed in the full band seismic cube. Comparing each pair of conventional vs. spectral assisted attributes shows enhancement of the results (more details and better resolution) in all evaluated seismic attributes with the proposed method.