Global Journal of Technology and Optimization

Ehsan Amirian

Publications

• Research Article
  Cognitive Data-Driven Proxy Modeling for Performance Forecasting of Waterflooding Process
  Author(s): Ehsan Amirian and Zhang-Xing John ChenEhsan Amirian and Zhang-Xing 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.. Read More»
  

  Abstract PDF