An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Model calibration workflows for reservoir simulation models including historical production data, also called history matching, deliver non-unique solutions and remain technically challenging. The objective of this work is to present a manageable workflow design with well-defined project workflow tasks for reproducible result presentation. Data analysis techniques are applied to explore the information content of multiple-realization workflow designs for decision support. Experimental design, sampling and Markov Chain Monte Carlo (MCMC) techniques are applied for case generation. Data analytics is applied to identify patterns in data sets supporting the evaluation of the history matching process. Visualization techniques are used to present dependencies between contributions to the history matching error metric. Conflicting history matching responses are identified and add value to the interpretation of history matching results. Probability maps are calculated on the basis of multiple-realizations sampled from a posterior distribution to investigate potentially under-developed reservoir regions. Technologies are applied to a real gas field in the Southern North Sea. For the purpose of the benchmark, a structured workflow design to history matching and estimation of prediction uncertainty is presented. Sensitivity evaluations are used to identify key uncertain input parameters and perform parameter reduction. Markov Chain Monte Carlo (MCMC) is applied for optimization and uncertainty quantification. Statistical stability of key performance parameters is verified by repeating relevant phases of the workflow several times. In conclusion practical consequences and best practices as well as the use of data analytics in history matching workflows are discussed.