Reservoir Geophysics: Seismic Pattern Recognition Applied to Ultra-Deepwater Oilfield in Campos Basin, Offshore Brazil

Abstract

Abstract Usually, seismic data is used in a qualitative approach to detect changes in the waveform and to pick acoustic continuity of a peak and/or a through as a structural mapping tool. The seismic interpretation is a qualitative process for building a geological model. Today, many works try to use the seismic information in a quantitative approach. Seismic interpretation in a quantitative approach is a key process in the integration of geoscience data at scales from basin-wide studies, reservoir focused and field-development process. Quantitative modeling could be deterministic and/or probabilistic. We use, in many steps of a seismic processing sequence, examples of quantitative deterministic modeling like seismic migration, some seismic inversion methodology, etc. Probabilistic modeling can be gathered in two groups: multivariate statistics and geostatistics approaches. Close to probabilistic modeling, we have also the neural network method. In this paper, we focus on the application of neural network modeling for seismic pattern recognition (seismic facies analysis) applied an ultra-deepwater turbidite oilfield reservoir in Campos Basin, offshore Brazil.