Image processing tools for better incorporation of 4D seismic data into reservoir models

Reservoir engineers have to predict the behavior of a hydrocarbon reservoir by building a simulation model which can best reproduce the data collected in the field. These data fall into two types: static data, which are invariable in time, and dynamic data, which evolve according to fluid motions in the reservoir. In this paper, we focus on the integration of dynamic data related to four-dimensional (4D) inverted seismic data. Such seismic data constitute an invaluable source of information on fluid displacement and geology over extensive areas of the reservoir. However, incorporating them in the reservoir model through a matching process is a challenging task. Classical formulations of the objective function, which computes the misfit between observed data and responses computed by the reservoir model, are not adapted to 4D inverted seismic data. For example, a least square based mismatch is not representative of the visual difference between two seismic images. In this paper, we define a new formulation of the objective function based on simplification of seismic data in order to extract relevant information. This simplification involves filtering and segmentation techniques, as well as image comparison methods rooted in image analysis. More precisely, we focus on the non-local means algorithm for filtering, on the level-set framework for segmentation and on the local modified Hausdorff distance for image comparison. We investigate the efficiency of such techniques in the context of seismic data, and illustrate their potential on a synthetic history matching reservoir example.