The increasing demand for sustainable materials in construction and engineering has sparked renewed interest in paper as a versatile, eco-friendly alternative to traditional resources. This shift towards natural materials presents a significant opportunity to advance sustainability, yet it also highlights the need for a deeper understanding of their complex mechanical properties. Paper, a natural fiber-based material, exhibits significant variations in its mechanical properties due to its inherent microstructural heterogeneity. These variations, coupled with its anisotropic material behavior, pose challenges in accurately predicting its macroscale mechanical response. To address this, we present an approach that incorporates microstructural variations into macroscale simulations using spatial random fields. Our methodology begins with a statistical analysis of real paper samples, including the use of variograms to characterize spatial correlations in fiber formation. This data informs the generation of random field realizations that capture the stochastic nature of the microstructure of paper. Then, we integrate these realizations into a macroscale material model for paper, enabling a more realistic representation of its heterogeneous properties. We demonstrate the efficacy of our approach through various structural simulations. By comparing results from models with and without random fields, we illustrate the impact of microstructural variations on macroscale behavior. Our findings highlight the importance of considering these variations in computational modeling of fibrous materials and composites. This work contributes to the field of computational mechanics by bridging the gap between microstructural variations and macroscale simulations. The presented methodology not only enhances our understanding of the complex mechanical behavior of paper but also provides a framework applicable to other fibrous materials and composites. Future work will focus on refining random field models and exploring their application in more complex structural analyses.