The aim of this work is to present a methodology for logfacies classification integrating petrophysical properties derived from formation evaluation analysis and elastic properties computed through a rock physics model. Furthermore a change of scale and domain of a log-facies classification is here presented in order to solve the problem related to reconciling seismic and log scales. The methodology consists of 3 steps: 1) rock physics model calibration and scenarios simulation; 2) log-facies classification; 3) histogram upscaling of log facies. The methodology was applied to real log data from a well in the West Africa deep offshore. The results show the improvement of the classification obtained by integrating both elastic and petrophysical data and the coherency of the upscaling of the original log-facies from a fine scale in depth domain to a coarse seismic scale in time.
The main target of this paper is to present a strategy to optimize facies classification for seismic reservoir characterization combining rock physics model, formation evaluation analysis and upscaling techniques in order to include log-facies classification in static reservoir modelling. A rock physics model is first established in order to accurately link acoustic and elastic variables with petrophysical properties (porosity, clay content and saturations as derived from the quantitative log interpretation). A rock physics model is required to understand how rock properties are related respect to velocities, impedances or other seismic attributes and to integrate geological information that can be obtained from seismic data into the reservoir. Then, a log-facies classification taking into account for both petrophysical and acoustic-elastic information is carried out applying a multivariate statistical technique, cluster analysis. Such classification is obtained using the petrophysical curves from formation evaluation analysis and velocities predictions obtained by means of the rock physics model. The vertical resolution of log interpretation and rock physics modeling is representative of the well-log scale. Thence, a change of scale (scaling-up of log-facies from log resolution to seismic resolution) and domain (depth-time) is needed for the correct integration of litho-fluid facies in the classification of the inverted 3D seismic volumes. The change of scale and domain of the proposed log-facies classification is a challenging task, as the variable is a categorical, and not a continuous one. We tackle the problem using a new approach based on histogram upscaling technique of categorical variables. The methodology can be applied to all the reservoirs in which it is possible to establish a physical link between petrophysical properties and elastic attributes.
The methodology (Figure 1) can be divided into three parts: 1) rock physics model calibration and scenarios simulation; 2) log-facies classification; 3) histogram upscaling of log facies.
The model is usually calibrated using well log data: the parameters that characterize the model are fixed so that the model can be applied to different petrophysical scenarios, even to those situations which are not sampled by well log data.
