Philippe Nivlet

A new methodology to account for uncertainties in 4D seismic interpretation

Summary Multi-attribute interpretation with discriminant analysis is an efficient tool to interpret 4D seismic data in terms of reservoir changes. Yet, because this met hod cannot take into account data uncertainties which are in most cases significant, the interpretation may be questionable. The following paper presents an algorithm based on interval arithmetic, which allows to assess the impact of the data errors on the interpretation. An application is proposed on a heavy oil pool produced by steam injection, where a base and two repetitive surveys are available. It includes two parts: first, the quantification of 4D data uncertainties and secondly, their propagation and final impact on the interpretation results.

A new nonparametric discriminant analysis algorithm accounting for bounded data errors

In a statistical pattern recognition context, discriminant analysis is designed to classify, when possible, objects into predefined categories. Because this method requires precise input data, uncertainties cannot be propagated in the classifying process. In real case studies, this could lead to drastic misinterpretations of objects. A new nonparametric algorithm based on interval arithmetic has thus been developed to propagate interval-form data. They consist in calculating interval conditional probability density functions and interval posterior probabilities. Objects are eventually assigned to a subset of classes, consistent with the data and their uncertainties. The classifying model is thus less precise, but more realistic than the standard one, which we prove on a real case study.