SPE SPE-121395-PP A Mutual Information-Based Metric for Identification of Nonlinear Injector Producer Relationships in Waterfloods

In this paper we introduce a new analytical approach for management of waterfloods in heterogeneous reservoirs. The main contribution is the development of a process and metric to evaluate the pair-wise injector-producer (IP) relationships, i.e., to quantify the impact of any injection well on the neighboring producing wells. The proposed metric is particularly designed to consider the non-linearity of the IP relationship between the injection and production rates by using the Mutual Information (MI) data mining tool. Non-linearity of the IP relationship is the main challenge in quantifying this relationship and, to the best of our knowledge, this is the first time that MI is used in the petroleum literature for IP relationship identification. In addition to MI that captures the non-linear correlation in the IP relationship, our metric considers other parameters such as the distance between the IP pair as well as their relative injection and production rates, respectively. Leveraging our proposed metric, we propose a system, for optimal waterflooding with which a field engineer can automatically: 1) Identify the under-performing producers based on their performance characteristics such as wateroil ratio, gas oil ratio, and oil production rate; 2) Rank all injectors based on their impact on the under-performing producers using our proposed IP relationship identification metric; 3) Decide on optimal injection volumes for individual injectors that have the most impact on the under-performing producers and maximize the recovery factor. The proposed technique can significantly reduce the decision-making time for the effective management of complex waterflood.