Abstract
We propose a novel learning optimization approach to find nearly optimal field solutions with a high probability and at a relatively low computational expense. Key attributes of the approach include capabilities to handle non-linear constraints in a dynamic fashion, automatic control of search directions; use of machine learning-based proxies; real time control of the optimization execution flow and, risk management decisions. The resulting algorithm retains the attractive property that the number of simulations is independent of the number of decision parameters as it progressively improves its rate of convergence as more information becomes available. We illustrate the new approach to determine multiple well locations and operation strategies to optimize field production on deep water and unconventional resource applications.
