During production of oil and gas from wells, solid particles such as removed scales or sand may accompany petroleum fluids. These particles present in this multiphase flow can impact inner walls of transportation infrastructure (straight pipelines, elbows, T-junctions, flow meters, and reducers) multiple times. These repeated impacts degrades the inner walls of piping and as a result, reduce wall thickness occur. This is known as solid particle erosion, which is a complex phenomenon involving multiple contributing factors. Prediction of erosion rates and location of maximum erosion are crucial from both operations and safety perspective. Various mechanistic and empirical solid particle erosion models are available in literature for this purpose. The majority of these models require particle impact speed and impact angle to model erosion. Furthermore, due to complex geometric shapes of process equipment, these solid particles can impact and rebound from walls in a random manner with varying speeds and angles. Hence, this rebound characteristic is an important factor in solid particle erosion modeling which cannot be done in a deterministic sense. This challenge has not been addressed in literature satisfactorily. This study uses experimental data to model particle rebound characteristics stochastically. Experimental setup consists of a nozzle and specimen, which are aligned at different angles so particles impact the specimen at various angles. Information regarding particle impact velocities before and after the impacts are obtained through Particle Tracking Velocimetry (PTV) technique. Distributions of normal and tangential components of particle velocities were determined experimentally. Furthermore, spread or dispersion in these velocity components due to randomness is quantified. Finally, based on these experimental observations, a stochastic rebound model based on normal and tangential coefficients of restitutions is developed and Computational Fluid Dynamics (CFD) studies were conducted to validate this model. The model predictions are compared with experimental data for elbows in series. It is found that the rebound model has a great influence on erosion prediction of both first and second elbows especially where subsequent particle impacts are expected.