Robust decision-making and reliable forecasting under uncertainty are the two critical factors to modern reservoir development and management success. The current project focuses on developing robust decision-making and reliable forecasting solutions using a python-based simulator validated using the CMG IMEX simulator. Several challenges in the framework, including uncertainty quantification and optimization, have been identified to investigate the impact of the uncertainty in the model parameterization on forecast reliability, complex computation problems, and reservoir development optimization under uncertainty. The project proposes solutions for each of the challenges mentioned above through an extensive field case supported by rigorous statistical evaluations. The pursued objectives in order thus include the static reservoir modeling of a hypothetical field under production with the help of the python programming language, investigating the results based on the production data obtained from the python simulation, reservoir modeling of a single flow of fluids from the proposed reservoir using the CMG suite, as well as the comparison, evaluation, and optimization of the simulation models thereby obtained. A simulation model based on python script was developed for simulating the finite difference formulation for the flow of single-phase fluid in a porous medium using an explicit finite-difference algorithm with the consideration of optimum time increments and minimized computational time. The proposed workflow can be applied to obtain a consistent and reliable decision-making outcome with reduced computational cost. Ultimately, the inclusion of python with a data-driven process such as reservoir simulation should help improve understanding of reservoir engineering phenomena, solve specific encountered problems in reservoirs and lead to better-informed decision-making.