Optimization is an essential task for field development planning because it has the potential to increase the economic value of the project. Because of advancements in technology, optimization is now shifting from manual to automated schemes. However, because of the complexity and high dimensionality of the problem of field-development optimization, the automated scheme receives less attention. In this paper, we demonstrate an increased efficiency of optimization algorithms in well-placement problems by application of reservoir-engineering practices. Analytical and empirical solutions to the flow equations as developed for reservoir engineering are added to the algorithm to improve the sampling efficiency of the algorithm. The main engineering methods that have been applied in this project are Buckley-Leverett (BL) and decline curve analysis (DCA). We begin by modifying and validating these analytical functions for different reservoir and well conditions. They are then applied in two stages: the initialization stage and optimization stage. In the former stage, numerous samples are created and evaluated by these analytical functions to identify good candidates for the initial population of the optimization algorithm. In the latter stage, these functions are used as a proxy model of the full simulation. The proposed algorithm is compared with a base-case approach in which no engineering-based analytical functions are used. The use of these engineering aspects is shown to be useful in both stages. The results also show that considerable computation time can be saved by applying these engineering aspects.
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Geotechnical Engineering and Engineering Geology