| Popis: |
In the domain of oil exploration, geostatistical methods aim at simulating petrophysical properties in a 3D grid model of reservoir. The main input comes from drilled wells data in the geographic space. Lithofacies and petrophysical properties as porosity and permeability are measured along these wells trajectories. These data are then assigned to every cell of the 3D grid model which intersects a well trajectory. At this step, only a small amount of cells are populated with petrophysical properties. Roughly speaking, the question is: which properties we should give to cell c, knowing the properties of n cells at a given distance from c? Obviously, the population of the whole reservoir must be computed while respecting the spatial correlation distances between petrophysical properties. Thus, the computing of these correlation distances is a key feature of the geostatistical simulations. In the classical geostatistical simulation workflow, the evaluation of the correlation distance is imprecise. Indeed, they are computed in a Cartesian simulation space which is not representative of the geometry of the reservoir. Thus, depending on the deformation degree of the lithostratigraphic units in the geographic space, significant errors may be introduced in the geostatistical simulation. This lack of accuracy has prompted us to work on and devise a new methodology in order to increase the reliability of the parameters required by the geostatistical simulators. We propose a new methodology in order to better estimate the correlation distances between wells. Our methodology is based on the isometric flattening of lithostratigraphic units. Thanks to this flattening process, we accurately reposition the initial populated cells in a flat simulation space, before computing the correlation distances. In this paper, we introduce our methodology through study case representing different deposit modes of the sub-surface models. We finally present some preliminary results. |
