Using Pre-Stack Seismic Data to Predict the West African Deepwater Reservoir

Abstract

The deepwater area has become a hot region of exploration and production for its huge potential on oil and gas resource. As a result of the special sedimentary environment and short of wells, the direct application of regular post-seismic data and regular seismic predict technology in these areas cannot meets the need of production and development. In order to improve the situation, we make the optimization and improvement from the aspects of information and technology. Usually, as the post-stack seismic section is stacked section, it has the advantage of improving the signal to noise ratio and so as to the seismic data quality. While the disadvantage is that, the post-stack seismic section destroys the real underground amplitude relationship. With the exploration and development targets shift from structural reservoir to lithologic reservoir, geophysicists began to maintenance pre-stack seismic data. Different offset and gathers all contain different geological significance. Pre-stack near-offset data in actual seismic data is the closest to zero-offset seismic data, theoretically has a higher frequency and resolution. We use nearly offset pre-stack angle gathers seismic data for interpretion, and with the routine post-stack data for comparison. In the study we extract sensitive seismic attributes by using pre-stack gathers near offset angle. Compared with the routine post-stack attribute extraction, pre-stack seismic near-offset data extraction displays better, measures the sand thickness more accurate, makes the well-seismic calibration better matching, greatly improves the prediction accuracy of sand body distribution range. We made an improvement on the seismic technology by extract instantaneous amplitude slice. It shows that the effect of the instantaneous amplitude slice extraction on pre-stack data is better than the general strata slice. Pre-stack instantaneous amplitude slice has the best effect. It shows the most obvious sand body, the clearest internal details, the most accurate well-seismic calibration. From that figure, we can clearly analyze the oil group sedimentary evolution, study the varieties of sand distribution, and effectively improve the thin reservoir prediction accuracy. Therefore, by making sensitive attribute slice on pre-stack seismic data of cut set can improve the prediction accuracy of the deep water reservoir.

AAPG Datapages/Search and Discovery Article #90217 © 2015 International Conference & Exhibition, Melbourne, Australia, September 13-16, 2015