Application of fast marching method and quality map to well trajectory optimization with a novel well parametrization

This paper proposes a novel parametrization technique for deviated and horizontal wells and investigates the potential of combining the Fast Marching Method (FMM) with Particle Swarm Optimization (PSO) to facilitate optimization of the trajectory of vertical, deviated, and horizontal wells. The parametrization technique is based on practical drilling parameters, which can be controlled easily. Results showed that the PSO-FMM could find similar solutions to the direct optimization (PSO-DIRECT) on simple, synthetic cases with few number wells, but not on complex cases with a large number of wells. The FMM was hybridized with a reservoir simulator (PSOHYBRID) that resolved the shortcomings of the PSO-FMM by filtering the solutions of the PSO-FMM based on the net present value (NPV). Also, initializing the optimizer by a quality map (QM) and the PSO-FMM method significantly improved the solutions. Initializing the PSO-DIRECT method by the PSO-FMM increased the NPV by 12% compared to the PSO-FMM method, and an average growth of 8.4% compared to the PSO-HYBRID. Also, the PSO-DIRECT + QM could find better results than the direct optimization, with an average increase of 1.4% in the NPV; however, its computational cost was two times the computational cost of the PSO-DIRECT + FMM and even higher than the computational cost of the direct optimization. The novelty of this paper is proposing a new well trajectory parametrization technique and comprehensively investigating the application of the FMM in well location and trajectory optimization on synthetic and real-field cases. It was shown that it is better to use the FMM as a helper function for the reservoir simulator rather than using it to completely substitute the reservoir simulator.

Automated summary

This study introduces a novel well trajectory parametrization technique, combines FMM and PSO for trajectory optimization, and addresses the limitations of the PSO-FMM approach. Results suggest that employing the PSO-DIRECT + QM method leads to superior outcomes, albeit at a higher computational cost.