Assessing seismic uncertainty via geostatistical velocity-model perturbation and image registration: An application to subsalt imaging

Abstract

Seismic data play a critical role in reservoir forecasting and decision making. However, large uncertainties are associated with seismic data, many of which arise from depth migration and the absence of accurate velocity models. Moreover, because of the computational and labor costs associated with velocity-model building, generally only a single seismic image and interpretation are performed. In that sense, seismic uncertainty largely is neglected in the reservoir-modeling workflow. A novel framework for assessing this uncertainty combines geostatistics with image-registration techniques. A novel geostatistical perturbation algorithm is used to generate multiple velocity models which are verified to be consistent with observed geophysical data. To alleviate the computational bottlenecks associated with migration, a distance-based model-selection step is performed to select a representative subset of the velocity models for depth migration. Image registration in combination with manual interpretation is used to generate a set of interpretations. This allows for direct linking of seismic-velocity uncertainty to the positional uncertainty of imaged structures. The workflow is demonstrated for a subsalt imaging case using the SEG Advanced Modeling (SEAM) model.