This work presents the development and evaluation of a hybrid intelligent system to optimize oil fields development. This system employs the following techniques: evolutionary algorithms to optimize the positioning and characteristics of wells in a reservoir; distributed processing to perform simultaneous reservoir simulations; function approximation models as simulator proxies; and quality maps to use some reservoir information to improve the optimization process.
This work represents the first stage in the application of modern methodologies for the analysis of alternatives of oil field development under uncertainties, where no uncertainties are considered. In this sense, the optimization consists in finding wells positioning, type and geometry in a delimited petroleum field, in order to maximize the NPV of alternative, considering some technical constrains as the minimum wells distance and maximum wells trajectory.
The problem approached in this work is considered of up most importance and it is recognized as a complex optimization problem, since the benefit of the option to develop an oil field depends on investments which in turn depend on the alternative chosen. The combination with other aspects makes this problem even more complex, yet properly optimized by Evolutionary Algorithms.
The development of a petroleum field can be understood as the needed actions to make the petroleum field productive, these actions can be: drills, injection system, platforms, etc. The way to perform this development defines an alternative. The alternative definition is one of the most important task in petroleum engineering, because this definition impacts the production behavior, future decisions, economic analysis and in consequence, the resultant attractivity of the defined project. This task involves some variables as the number, type and positioning of the petroleum wells; the operation conditions in reservoir, and even the economic scenery. In this work the main activity is determine the wells number and positioning.
Most recent optimization systems involve the usage of reservoir simulator; even with a high computational cost, reservoir simulation is still the most reliable way of obtaining forecasts of oil and gas production.
In the literature, early works approaching the petroleum fields development use the recovered oil as the optimization criterion to find configurations for oil exploiting that maximize the recovered oil quantity. Nevertheless, more recent works consider the usage of economic optimization criteria. The most used criteria in literature is the Net Present Value (NPV) and will be used in this work.
Evolutionary Optimization basically consists in the application of Evolutionary Algorithms (EAs) 1 to approach complex optimization problems.
EAs are global search methods that simulate some of the processes taking place in natural evolution. They maintain a population of potential solutions to a given problem that are transformed, over successive generations, via processes of selection and genetic modification. Even though it seems simplistic from a biologist's viewpoint, these algorithms are robust enough to provide competent adaptive search mechanisms.
In the past few years, EAs have been successfully applied to a large number of optimization problems. Some of the most relevant examples belong to the class of combinatorial optimization problems such as the traveling salesperson, scheduling, packing or routing. Additionally, there are many other situations that occur in various industrial, economical, and scientific domains that have also been solved using evolutionary methods.
The most know evolutionary algorithms are: genetic algorithms, genetic programming, cultural algorithms, artificial immune systems, co-evolutionary algorithms, differential evolution, among others.