Geostatistical simulation (e.g., sequential Gaussian simulation, SGSim) provides an effective framework for quantifying variability of geoscientific variables and supporting risk-informed decision-making in various scenarios. These approaches are theoretically well grounded under assumptions such as stationarity and Gaussianity, and their practical implementation typically involves explicit variogram modeling and repeated neighborhood-based computations, which may become demanding in large-scale or high-dimensional settings. Recently, data-driven modeling strategies have gained increasing attention across scientific disciplines, offering flexible mechanisms for learning spatial dependence structures directly from data. This development motivates the exploration of learning-based alternatives for stochastic simulation. In this paper, artificial neural network-based models were constructed to address the above issues. A series of simulation experiments was generated to test and validate the proposed model. Our results suggest that: (1) spatial dependence can be captured by two complementary strategies, using neighboring attributes (e.g., spatial lag features) and encoding relative positions (e.g., MEM); (2) within our experiments, the proposed data-driven model appears less sensitive to non-Gaussianity and non-stationarity; and (3) the model provides a feasible complement to SGSim by reproducing key statistics (histogram, variogram) with favorable computational cost and flexible model configuration, particularly for large conditioning neighborhoods.