Title: |
Optimization of Field Development Using Particle Swarm Optimization and New Well Pattern Descriptions |
Author: |
Jerome Emeka Onwunalu |
Year: |
2010 |
Degree: |
PhD |
Adviser: |
Durlofsky |
File Size: |
1.6MB |
View File: |
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Access Count: |
1399 |
Abstract:
The optimization of the type and location of new wells is an important issue in oil field development. Computational algorithms are often employed for this task. The problem is challenging, however, because of the many different well configurations (vertical, horizontal, deviated, multilateral, injector or producer) that must be evaluated during the optimization. The computational requirements are further increased when geological uncertainty is incorporated into the optimization procedure. In large-scale applications, hundreds of wells, the number of optimization variables and the size of the search space can be very large. In this work, we developed new optimization procedures for well placement optimization using particle swarm optimization (PSO) as the underlying optimization algorithm. We first applied PSO to a variety of well placement optimization problems involving relatively few wells. Next, a new procedure for large-scale field development involving many wells was implemented. Finally, a meta-optimization procedure for determining optimal PSO parameters during the optimization was formulated and tested.
The particle swarm optimization is a population-based, global, stochastic optimization algorithm. The solutions in PSO, called particles, move in the search space based on a “velocity.” The positions and velocity of each particle are updated iteratively according to the objective function value for the particle and the position of the particle relative to other particles in its (algorithmic) neighborhood. The PSO algorithm was used to optimize well location and type in several problems of varying complexity including optimizations of a single producer over ten realizations of reservoir model and optimizations involving nonconventional wells. For each problem, multiple optimization runs using both PSO and the widely used (binary) genetic algorithm (GA) were performed. The optimizations showed that, on average, PSO provides results that are superior to those using GA for the problems considered.
In order to treat large-scale optimizations involving significant numbers of wells, we next developed a new procedure, called well pattern optimization (WPO). WPO avoids some of the difficulties of standard approaches by considering repeated well patterns and then optimizing the parameters associated with the well pattern type and geometry. WPO consists of three components: well pattern description (WPD), well-by-well perturbation (WWP), and the core PSO algorithm. In WPD, solutions encode well pattern type (e.g., five-spot, seven-spot) and their associated pattern operators. These pattern operators de- fine geometric transformations (e.g., stretching, rotation) applied to a base pattern element. The PSO algorithm was then used to optimize the parameters embedded within WPD. An important feature of WPD is that the number of optimization variables is independent of the well count and the number of wells is determined during the optimization. The WWP procedure optimizes local perturbations of the well locations determined from the WPD solution. This enables the optimized solution to account for local variations in reservoir properties. The overall WPO procedure was applied to several optimization problems and the results demonstrate the effectiveness of WPO in large-scale problems. In a limited comparison, WPO was shown to give better results than optimizations using a standard representation (concatenated well parameters).
In the final phase of this work, we applied a meta-optimization procedure which optimizes the parameters of the PSO algorithmduring the optimization runs. Metaoptimization involves the use of two optimization algorithms, where the first algorithm optimizes the PSO parameters and the second algorithm uses the parameters in well placement optimizations. We applied the meta-optimization procedure to determine optimum PSO parameters for a set of four benchmark well placement optimization problems. These benchmark problems are relatively simple and involve only one or two vertical wells. The results obtained using meta-optimization for these cases are better than those obtained using PSO with default parameters. Next, we applied the optimized parameter values to two realistic optimization problems. In these problems, the PSO with optimized parameters provides comparable or better results than the default PSO. Finally, we applied the full meta-optimization procedure to a realistic case, and the results were shown to be an improvement over those achieved using either default parameters or parameters determined from benchmark problems.
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Copyright 2010, Jerome Emeka Onwunalu: Please note that the reports and theses are copyright to their original authors. Authors have given written permission for their work to be made available here. Readers who download reports from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the author, Jerome Emeka Onwunalu.
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