Applications of Artificial Neural Network for Seismic Facies Classification: A Case Study from the Mid-Cretaceous Reservoir in Supergiant Oil Field

Facies classification is significant for characterization and evaluation of a reservoir because the distributionof facies has an important impact on reservoir modelling which is important for decision making andmaximizing return. Facies classification using data from sources such as wells and outcrop cannot captureall reservoir characterization in the inter-well region and therefore as an alternative approach, seismic faciesclassification schemes have to be applied to reduce the uncertainties in the reservoir model. In this research,a machine learning neural network was introduced to predict the lithology required for building a full fieldearth model for carbonate reservoirs in Sothern Iraq. In the present research, multilayer feed forward network (MLFN) and probabilistic neural network(PNN) were undertaken to classify facies and its distribution. The well log that was used for litho-faciesclassification is based on a porosity log. The spatial distribution of litho-facies was validated carefullyusing core data. Once successfully trained, final results show that PNN technique classified the carbonatereservoir into four facies, while the MLFN presented two facies. The final results on a blind well, showthat PNN technique has the best performance on facies classification. These observations implied thisreservoir consists of a wide range of lithology and porotype fluctuations due to the impact of depositionalenvironment. The work and the methodology provide a significant improvement of the facies classification and revealedthe capability of probabilistic neural network technique when tested against the neural network. Therefore,it proved to be very successful as developed for facies classification in carbonate rock types in the MiddleEast and similar heterogeneous carbonate reservoirs.