Neural-Network based Sensitivity Analysis for Injector-Producer Relationship Identification

Determining injector-producer relationships, i.e., to quantify the inter-well connectivity between injectors and producers in a reservoir, is a complex and non-stationary problem. In this paper, we present a neural-network-based sensitivity analysis approach to address this problem. To the best of our knowledge, sensitivity analysis has never been applied for identification of the injector-producer relationships, yet we show that it is an intuitive while fundamental approach to address this problem. Sensitivity analysis is based on a theory with which the functioning of a closed system is derived by analyzing the derivatives of the output with respect to each input combination. For the injector-producer relationship identification problem, we use sensitivity analysis to determine the injector-producer relationships by varying the injection rates, i.e., the inputs to a trained neural network model of the oilfield, and analyzing the outputs, i.e., the production rates. With our approach, we first generated a neural network to define the mapping function between each producer and its surrounding injectors based on the historical injection and production data. We employed Back-Propagation-Through-Time (BPTT) learning algorithm to train the three-layer feed-forward neural network using real data collected from 1911 to 2005. Next, we utilized the generated neural network model to apply sensitivity analysis in order to quantify the significance of the injectors on the corresponding producers. We evaluated our proposed injector-producer relationship identification technique by experiments with real oilfield dataset as well as field trials. Experimental results show that our sensitivity analysis approach is not only an efficient method for identifying injector-producer relationships but also reveals significantly higher correlation accuracy as compared to the correlation typically estimated by the field engineers.

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