Application of different classification methods for litho-fluid facies prediction: A case study from the offshore Nile Delta

Abstract

In this work we test four classification methods for litho-fluid facies identification in a clastic reservoir located in the offshore Nile Delta. The ultimate goal of this study is to find an optimal classification method for the area under examination. The geologic context of the investigated area allows us to consider three different facies in the classification: shales, brine sands and gas sands. The depth at which the reservoir zone is located (2300-2700 m) produces a significant overlap of the P- and S-wave impedances of brine sands and gas sands that makes discrimination between these two litho-fluid classes particularly problematic. The classification is performed on the feature space defined by the elastic properties that are derived from recorded reflection seismic data by means of amplitude versus angle Bayesian inversion. As classification methods we test both deterministic and probabilistic approaches: the quadratic discriminant analysis and the neural network methods belong to the first group, whereas the standard Bayesian approach and the Bayesian approach that includes a 1D Markov chain a priori model to constrain the vertical continuity of litho-fluid facies belong to the second group. The ability of each method to discriminate the different facies is evaluated both on synthetic seismic data (computed on the basis of available borehole information) and on field seismic data. The outcomes of each classification method are compared with the known facies profile derived from well log data and the goodness of the results is quantitatively evaluated using the so-called confusion matrix. The results show that all methods return vertical facies profiles in which the main reservoir zone is correctly identified. However, the consideration of as much prior information as possible in the classification process is the winning choice for deriving a reliable and physically plausible predicted facies profile.