assisted and automatic navigation in black oil reservoir models based on probabilistic roadmaps
Three-dimensional visualization is a valuable technique in the oil industry. One of its important applications is for inspecting black oil reservoir models, allowing engineers to design and analyze different model configurations, focusing on maximum production and minimum cost. These models are huge structures, with complex well arrangements, that require fly-through navigation for accurate analysis. The common approach of direct camera control is difficult for inexperienced users and results in inappropriate camera motions and collisions with the environment, which can easily lead to user distraction. Moreover, investments are being made on immersive caves, and environment collisions in this case are very uncomfortable. It becomes necessary to provide an assisted navigation, in which the user guides the camera more easily, without restricting the environment exploration. Also, it is interesting to provide a fully automatic navigation mode, in which the user selects a target and the system computes a smooth, collision-free path throughout the environment.In this paper, we present a specific solution for navigation in models used in numerical simulations of black oil reservoirs. Our approach is based on probabilistic roadmaps. A global roadmap of the environment is pre-computed using a randomized motion-planning algorithm. At runtime, graph searching is performed using the A* algorithm to compute collision-free paths. We propose a new roadmap-construction algorithm based on a hierarchical environment-sampling strategy, along with a topology-based coverage estimate. For multi-layered reservoir models, we present a new heuristic function for the A* algorithm that greatly increases graph searching performance. We also present new approaches to use the pre-computed roadmap for providing both assisted and automatic navigation at runtime.
navigation collision detection virtual reality scientific visualization