Evolutionary optimization of smart-wells control under technical uncertainties

This work presents a system, based on Evolutionary Algorithms, capable of optimizing the controlling process of intelligent wells technology present in Intelligent Fields. The control refers to the opening and shutting operation of valves in these wells. A proactive controlling strategy to find a configuration of opening and shutting valves was assumed. It anticipates and maximizes the oil recuperation, delays the water cut on producer wells, and reduces the quantity of produced water, maximizing the wells life. As a result, the obtained configuration promotes the increasing of NPV (Net Present Value). The use of control strategies to benefit the completion identifies the field as intelligent. The proposed representation can formulate a controlling strategy for all valves, for any desired time interval. To improve the decision making for using or not using smart wells, the fault risk of the control device existing in the intelligent completions was considered into optimization. For this purpose, the system applies Monte Carlo simulation together with some simulation techniques for convergence acceleration and uncertainties representation by probability distributions. Even considering the existence of uncertainties into valves operation, the results obtained in the tests reveal significant gains by using the intelligent completion on the field such as: increasing the recuperation factor of the field, reducing the water inflow and increasing the longevity of the field. For all valves representations, improvements were achieved when compared with the case without valves. The conception and implementation of an intelligent system, capable of supporting the development and management of intelligent petroleum fields, builds up an important advantage for the spreading of intelligent field technology. The results obtained in this work demonstrate that the intelligent control of valves can become a competitive difference in the strategy of hydro-carbon production. Copyright 2007, Society of Petroleum Engineers.