Evolutionary Optimization of Smart-Wells Control under Technical Uncertainties

Abstract

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. Introduction In projects of the petroliferous exploration area [1], the optimization of the exploitation of a field involves the search for production strategies that are more economically attractive. Following this idea, the engineer intervenes in the wells production by performing operations such as: isolating producer intervals, opening of new intervals, acidifications, fracturing, tests of formation for data collection and other restoring operations. The high costs of these operations, however, especially those in offshore fields with wet completion, can make some of these operations unfeasible, and as a consequence, the field management will not be optimal. The concept of wells with intelligent completion arises as a technological alternative. This concept is proposed to reduce the cost of the most common restoring operations, as the isolation or the opening of producer intervals. In addition, the monitoring of the production data in real time - flows, pressures and temperature - allows a better field management. An intelligent completion can be defined as a system capable of collecting, transmitting and analyzing data, which enables the monitoring and the remote drive of flow control devices. As a consequence, the control of reservoir production is made possible. However, generally these technologies are associated with high costs, because they are new and with fewer field information related to reliability and ways of use. This fact makes the assets managers feel a little fearful in approving the implantation of these technologies, especially because there is not a standard methodology to calculate their benefits. Considering the different possible combinations of flow control devices operation, several profiles of production can be generated, suggesting the application of an efficient optimization method that allows the discovery of a profile that optimize the production under some criteria.

Well control optimization using a two-step surrogate treatment

“…Stochastic methods used in this setting include genetic algorithms (GAs) and particle swarm optimization (PSO), which are both population-based evolutionary methods. These procedures were shown to be applicable for both well control [4,5,6,7] and well location optimization [8,9]. Pattern-search or stencilbased optimization techniques, such as mesh adaptive direct search (MADS) [10], have also been used for well control optimization [11,12,13,14].…”