Constraining 3D Process Sedimentological Models to Geophysical Data Using Image Quilting

Wednesday, 17 December 2014
Pejman Tahmasebi1, Andrea Da Pra2, Marco Pontiggia2 and Jef Caers3, (1)Stanford University, Stanford, CA, United States, (2)ENI, San Donato Milanese 20097, Italy, (3)Stanford Earth Sciences, Stanford, CA, United States
3D process geological models, whether for carbonate or sedimentological systems, have been proposed for modeling realistic subsurface heterogeneity. The problem with such forward process models is that they are not constrained to any subsurface data whether to wells or geophysical surveys. We propose a new method for realistic geological modeling of complex heterogeneity by hybridizing 3D process modeling of geological deposition with conditioning by means of a novel multiple-point geostatistics (MPS) technique termed image quilting (IQ). Image quilting is a pattern-based techniques that stiches together patterns extracted from training images to generate stochastic realizations that look like the training image. In this paper, we illustrate how 3D process model realizations can be used as training images in image quilting. To constrain the realization to seismic data we first interpret each facies in the geophysical data. These interpretation, while overly smooth and not reflecting finer scale variation are used as auxiliary variables in the generation of the image quilting realizations. To condition to well data, we first perform a kriging of the well data to generate a kriging map and kriging variance. The kriging map is used as additional auxiliary variable while the kriging variance is used as a weight given to the kriging derived auxiliary variable. We present an application to a giant offshore reservoir. Starting from seismic advanced attribute analysis and sedimentological interpretation, we build the 3D sedimentological process based model and use it as non-stationary training image for conditional image quilting.