Jornal Global de Tecnologia e Otimização

Cognitive Data-Driven Proxy Modeling for Performance Forecasting of Waterflooding Process

Abstract

Ehsan Amirian and Zhangxing John Chen

Assessment of diverse operational constraints and risk appraisal associated with reservoir heterogeneities are essential foundation of production optimization and oil field development scenarios. Water-flooding performance evaluation that comprises comprehensive numerical simulations is typically cumbersome in terms of time and money, which is not reasonably appropriate for practical decision making and future performance forecasting. Cognitive data-driven proxy modeling practices, which incorporate data-mining techniques and machine learning concepts, offer a fascinating substitute for explicit models of the underlying process that can be instantaneously reassessed, especially for extremely nonlinear system forecasts. In this paper, an exploratory data analysis is applied to create a comprehensive data set from Water-flooding actual field data, which entails different characteristics labeling reservoir heterogeneities and other pertinent operational constraints. Artificial neural network (ANN) is applied as a cognitive data-driven proxy modeling effort to predict Water-flooding production in heterogeneous reservoirs. This study presents the great potential of cognitive data-driven proxy modeling techniques for practical applications and as a feasible add on for investigating a huge quantity of real field data efficiently. In addition, the suggested methodology can be incorporated directly into most present reservoir development decision making routines.