Most simulation algorithms based on Multiple-Point Statistics (MPS) deploy training images that are usually constructed from conceptual 2D or 3D spatial distribution of the reservoir parameters by means of a similarity measure such as Bayesian likelihood probability or pattern-based matching methods to map complicated geological structures and heterogeneities inherent to most petroleum reservoirs. The recent pattern-based MPS algorithms employ common techniques in the course of unconditional simulation. However, there is no guarantee to ensure that the desired targets in the training patterns could have been even captured by a specified search template. That is, the number of training patterns produced by any search template size is always limited, while the data events formed during simulation could potentially show any unexpected patterns. This is where previous algorithms encounter some difficulties in finding actual patterns even if they are equipped with precise pattern recognition measures. To address this issue and avoid extra complexity in the course of simulation, we have proposed a new unconditional pattern-based simulation procedure which deploys the edge-similarity and morphological image processing concepts. In the course of our proposed algorithm, each pattern is temporarily substituted with its edge features followed by performing a k-means classification of their similarity measures using Manhattan distance function for primary infilling of optimum patterns in the simulation grid. Next, through using a combination of morphological operations with automatically determined or user-defined specifications, the algorithm named as MORPHSIM in this context would effectively improves pattern reproduction and structural continuity. Results have shown the strength and flexibility of MORPHSIM in reproducing patterns for unconditional simulations, even when the input training image is poorly constructed and includes just a simple non-stationary structure. The algorithm is not only computationally effective in terms of simulation run time and memory usage relative to FILTERSIM but also can overcome the imperfection of training image by applying morphological image processing filters.