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.
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.
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