Well placement optimization is a very challenging problem due to the large number of decision variables involved and the nonlinearity of the reservoir response as well as of the well placement constraints. Over the years, a lot of research has been done on this problem, most of which using optimization routines coupled to reservoir simulation models. Despite all this research, there is still a lack of robust computer-aided optimization tools ready to be applied by asset teams in real field development projects. This paper describes the implementation of a tool, based on a Genetic Algorithm, for the simultaneous optimization of number, location and trajectory of producer and injector wells. The developed software is the result of a two-year project focused on a robust implementation of a computer-aided optimization tool to deal with realistic well placement problems with arbitrary well trajectories, complex model grids and linear and nonlinear constraints. The developed optimization tool uses a commercial reservoir simulator as the evaluation function without using proxies to substitute the full numerical model. Due to the large size of the problem, in some cases involving more than 100 decision variables, the optimization process may require thousands of reservoir simulations. Such a task has become feasible through a distributed computing environment running multiple simulations at the same time. The implementation uses a technique called Genocop III- Genetic Algorithm for Numerical Optimization of Constrained Problems - to deal with well placement constraints. Such constraints include grid size, maximum length of wells, minimum distance between wells, inactive grid cells and user-defined regions of the model, with non-uniform shape, where the optimization routine is not supposed to place wells. The optimization process was applied to three full-field reservoir models based on real cases. It increased the net present values and the oil recovery factors obtained by well placement scenarios previously proposed by reservoir engineers. The process was also applied to a synthetic case, based on outcrop data, to analyze the impact of using reservoir quality maps to generate an initial well placement scenario for the optimization routine without using an engineer-defined configuration. Introduction The definition of a well placement is a key aspect with major impact in a field development project. In this sense, the use of reservoir simulation allows the engineer to evaluate different placement scenarios. However, the current industry practice is still, in most cases, a manual procedure of trial and error that requires a lot of experience and knowledge from the engineers involved in the project. Considering that, the development of well placement optimization tools which can automate this process is a high desirable goal.

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One of the main challenges of maneuvering an Unmanned Aerial Vehicle (UAV) to keep a stabilized flight is dealing with its fast and highly coupled nonlinear dynamics. There are several solutions in the literature, but most of them require fine-tuning of the parameters. In order to avoid the exhaustive tuning procedures, this work employs a Fuzzy Logic strategy for online tuning of the PID gains of the UAV motion controller. A Cascaded-PID scheme is proposed, in which velocity commands are calculated and sent to the flight control unit from a given target desired position (waypoint). Therefore, the flight control unit is responsible for the lower control loop. The main advantage of the proposed method is that it can be applied to any UAV without the need of its formal mathematical model. Robot Operating System (ROS) is used to integrate the proposed system and the flight control unit. The solution was evaluated through flight tests and simulations, which were conducted using Unreal Engine 4 with the Microsoft AirSim plugin. In the simulations, the proposed method is compared with the traditional Ziegler-Nichols tuning method, another Fuzzy Logic approach, and the ArduPilot built-in PID controller. The simulation results show that the proposed method, compared to the ArduPilot controller, drives the UAV to reach the desired setpoint faster. When compared to Ziegler-Nichols and another different Fuzzy Logic approach, the proposed method demonstrates to provide a faster accommodation and yield smaller errors amplitudes.

This work presents the conception, modeling and development of an E&P projects economical analysis system under uncertainty that integrates the following modules: -an optimization hybrid system for oilfields development based in evolutionary algorithms with distributed evaluation, proxies and use of quality maps to optimize the place and quantity of wells in a delimited petroleum field to maximizing the NPV of the alternative. Also, this system considers some technical constrains as the minimum wells distance and maximum wells trajectory.-a model based on Genetic Algorithms and Monte Carlo simulation designed to find an optimal decision rule for some oil field development alternatives obtained from previous module, considering market uncertainty (oil price), that may help decision-making with regard to: developing a field immediately or waiting until more favorable market conditions. In the economic analysis also is considered, for each alternative under evaluating, the option of investment in information taking in account interactions of different uncertainties types. This analysis considers the option of future production expansion by installing an additional well under reserve volume uncertainties in the in the area to be drained by the additional well. Some computational intelligence techniques were applied in this system as: evolutionary algorithms, neural networks and fuzzy numbers; also, the system uses other techniques as Real Options and Monte Carlo Simulation to treat uncertainties. The obtained outcomes show the benefits to have an integrated decision support system to the decision-making in the economic analysis of oilfields development.

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. Introduction 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 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.

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.