Integrating clustering and classification techniques: a case study for reservoir facies prediction

The need for integration of different data in the understanding and characterization of reservoirs is continuously growing in petroleum geology. The large amount of data for each well and the presence of different wells to be simultaneously analyzed make this task both complex and time consuming. In this scenario, the development of reliable interpretation methods is of prime importance in order to help the geologist and reduce the subjectivity of data interpretation. In this paper, we propose a novel interpretation method based on the integration of unsupervised and supervised learning techniques. This method uses an unsupervised learning algorithm to objectively and quickly evaluate a large dataset made of subsurface data from different wells in the same field. Then it uses a supervised learning algorithm to predict and propagate the characterization over new wells. To test our approach, we use first hierarchical clustering to then feed several supervised learning algorithms in the classification domain (e.g. decision trees and linear regression).