Production Strategy Optimization Using Genetic Algorithm and Quality Map

Abstract

Abstract The definition of a production strategy is one of the most important tasks in reservoir management, since it influences projects attractiveness. However, the process to define variables such as well placement, number and type of wells and operational conditions is time-consuming and it demands high computational effort. The use of an optimization algorithm to achieve a good solution can be very valuable to the process but it can also lead to an exhaustive search, demanding a great number of simulations to test many possibilities. In order to minimize the number of combinations (number of wells and positions), it is proposed the use of the quality map, that is a two dimensional representation of regions with production potential in a reservoir, as a criteria to allocate the wells. The optimization algorithm used in this work is the genetic algorithm (GA) which is a method based on natural evolution process. The main characteristic of GA is the ability to work in a solution space with non-smooth and non-linear topology, where the traditional methods generally fail. The methodology proposed in this work is used to optimize production strategies in a realistic reservoir model, defining the number and position of production and injection wells, and the production/injection flow rates. The number of individuals in a population and the number of generations were also varied to evaluate the efficiency of the algorithm. Results showing the performance of several optimization processes are presented. The influence of the GA parameters control are analyzed and compared.