Modeling of a deepwater turbidite reservoir conditional to seismic data using principal component analysis and multiple-point geostatistics

A new geostatistical approach, known as multiple-point statistics (MPS) simulation, recently has been proposed to generate 3D depositional facies models that integrate both large-scale information derived from seismic data and fine-scale information derived from well logs, cores, and analog studies. In this paper, the practicality, flexibility, and CPU advantage of this new approach are demonstrated through the modeling of an actual deepwater turbidite reservoir. First, based on well-log interpretation and a global geological understanding of the reservoir architecture, a training image depicting sinuous sand bodies is generated using a nonconditional object-based simulation algorithm. Then, disconnected sand bodies are interpreted from seismic amplitude data using a principal component cluster analysis technique, while a sand probability cube is generated using a principal component proximity transform of the same seismic. Multiple-point geostatistics allows simulating multiple realizations of channel bodies similar to the training image, constrained to the local sand probabilities, partially interpreted sand bodies, and well-log data. As illustrated in this paper, to account for uncertainty about the geometry of the sand bodies, different training images associated with alternative conceptual models proposed by the geologists can be considered.