Optimizing vertical and deviated wells based on advanced initialization using new productivity potential map

ABSTRACT Well placement optimization is an important part during the development of different types of reservoirs. As a highly nonlinear problem, it is difficult to obtain the most effective placement simply relying on screening different scenarios generated with engineering judgement. Automatic well placement optimization based on numerical simulation models and optimization algorithms can effectively solve this nonlinear problem, and greatly save the human labor. However, there is still space for improvement to avoid overwhelming computational cost and premature convergence of optimization algorithms. This paper focused on developing an advanced initialization procedure for well placement optimization, where multiple high-quality well placement scenarios are automatically generated and used as initial guesses. Specifically, we proposed a new form of productivity-potential map (NPPM), which measures not only the productivity potential for producers, but also the injectivity potential for injectors. What is more, compared to the traditional PPM which is defined only for vertical wells, NPPM is generalized to also consider deviated wells. Moreover, the proposed NPPM takes into account inter-well distance which penalizes cases where wells would otherwise be placed too close together. The advanced initialization with NPPM is applied to the PUNQ-S3 reservoir and compared to the initialization with a traditional PPM and a Random Initialization. Results based on the optimization of both vertical wells and deviated wells demonstrate that initialization with NPPM could improve both the quality of the optimal solutions and the robustness of a stochastic optimization algorithm (e.g., particle swarm optimization algorithm in this paper) compared to the rest of the two initialization strategies. Considering that NPPM is used as an initialization procedure, it is easy to couple it with other optimizers.