Distributed massive MIMO (D-mMIMO) has been considered for future networks as it holds the potential to offer superior capacity while enabling energy savings in the network. A D-mMIMO system has multiple arrays. Optimizing the locations of the arrays is essential for the energy efficiency of the system. In existing works, array placement has been optimized mostly based on common channel models, which rely on a given statistical distribution and Euclidean distance between user locations and arrays. These models are justified if applied to sufficiently large cells, where the statistical description of the channel is expected to fit its empirical condition. However, with the advent of small cells, this is no longer the case. The channel propagation condition becomes highly environment-specific. This paper investigates array placement optimization with different ways of modeling the propagation conditions taking the environment information (e.g., buildings) into account. We capture the environment information via a graph. Two shortest path-based propagation models are introduced based on the graph. We validate the performance of the models with a ray-tracing simulator considering different signal coverage levels. The simulation results demonstrate that higher energy efficiency is achieved by using the array placements found via the proposed models compared to a Euclidean distance-based propagation model in small cells. The average power saving at 96% signal coverage, for example, reaches more than 5 dB.