Excess Water Production Diagnosis in Oil Fields Using Ensemble Classifiers

Abstract

Excessive water production in oil fields is a challenging problem affecting oil production and entailing high handling and disposing costs as well as environmental issues. Accurate and timely diagnosis of the water production problem will significantly increase the success of the remedial actions taken. The traditional approaches in production data analysis by means of empirical techniques for proper diagnosis of water production mechanisms are still debatable. This paper presents a novel approach in water production problem identification using data mining techniques for production data analysis. The data used in this approach are water-oil ratio and some reservoir knowledge. New parameters used to identify two common types of water production mechanisms, i.e. water coning and channeling, are developed, and tree based ensemble classifiers are used for diagnosis. Our results demonstrate the applicability of this technique in successful diagnosis of water production problems.