Multivariate statistical analyses applied to seismic facies recognition

Abstract

One of the most important goals of seismic stratigraphy is to recognize and analyze seismic facies with regard to the geologic environment. The first problem is to determine which seismic parameters are discriminant for characterizing the facies, then to take into account all those parameters simultaneously. The second problem is to be sure that there is a link between the seismic parameters and the geologic facies we are investigating. This paper presents a methodology for automatic facies recognition based upon two steps. The first, or learning step, begins with the definition of learning seismic traces for each facies we wish to recognize. The choice of learning traces is based upon either well data or a seismic stratigraphic interpretation. A large number of seismic parameters are then computed from the learning traces; multidimensional analyses are carried out in order to validate the choice of learning traces and to select, among all the available parameters, those that discriminate best. At this stage, a modeling step may be carried out to relate the seismic parameters to the geologic features. The second step is a predictive one which allows automatic facies recognition. We compute the previously chosen discriminant parameters on unknown seismic traces and classify the unknown traces with regard to the learning traces. We develop the methodology and successfully apply it to two examples of reservoir facies recognition. Our main conclusion is that seismic traces contain geologic information that can be extracted by multivariate data analyses of a large number of seismic parameters. Automatic facies recognition is reliable and fast; the derived facies map has the great advantage of combining simultaneously several discriminant parameters.