Reservoir uncertainty analysis is significant for petroleum engineers for predictions of reservoir performance. However, analysis of reservoir performance uncertainty is challenging because large amounts of data must be transferred efficiently, reliably and securely between sites, and thousands of simulations are executed across different resources. There are several steps in conducting reservoir performance prediction, including: (a) transferring input files to remote resources, (b) running thousands of simulations in different scheduler systems, (c) monitoring the jobs, (d) transferring output files from remote sites to the local system, and (e) post-processing to determine whether simulations have resolved uncertainties adequately. This whole process may have to be repeated as new data are obtained, or if uncertainty thresholds change. Therefore, it is essential to automate end-to-end processing for this complex, composite application with many tasks that are executed in a specific order. We implemented an end-to-end automated system for reservoir uncertainty analysis using Grid technologies such as Condor-G, DAGMan, and Stork. This paper describes the requirements, design and implementation of such a system.