Study On The Application Of Pre-stack Noise Attenuation Techniques Aiming At Elastic Inversion

With the development of oil & gas exploration and exploitation, the geological conditions of seismic survey areas is be coming more and more complicated, and thus the demand to seismic data processing is becoming higher and higher. For instance, pre-stack time migration has become a routine processing and pre-stack processing has become more important. The pre-stack noise attenuation techniques are far away from meeting the requirements of elastic inversion; therefore, the ability to keep signals of a noise attenuation technique is more important.Noise attenuation is a very important step during the seismic data processing stage. It matters not just subsequent processes, but also data interpretation and reservoir lithology and liquid distribution discrimination and prediction.Aiming at the requirement of elastic parameter inversion to input seismic data, this paper conducted profound and detailed research on keeping signals during pre-stack noise attenuation, including noise attenuation technique and parameter selection, amplitude monitoring and quality control. Finally, based on the current processing software system and the pertinent techniques, presented a practical procedure for insuring and monitoring data quality during noise attenuation of seismic data from eastern China. With this procedure, noises in the data are well attenuated and reflection amplitude distortion is minimized during the processing and thus the non-geological response of the data is basically removed. Compared with the results of conventional processing, the S/N ratio is higher, lateral reflection amplitudes are more balanced and more reliable subtle events between main reflections emerged and so the subsurface geology is better rendered. According to the composite monitoring and evaluation using seismic data and geological information and modeling data, the relative amplitudes between different layers are reasonable. And what is more, elastic parameter inversion was conducted on the processed data and the inversion results gave better discrimination to the reservoir lithology and liquid distribution.