An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Reservoir model calibration workflows for reservoir simulation models including historical production data, also called history matching, deliver non-unique solutions and remain technically challenging. In addition, the validation process of the reservoir simulation model often introduces a break of the conceptual connection to the geological model. This raises questions on how to quantify the deviation between the calibrated simulation model and the original geological model. Workflow designs for history matching require scalable and efficient optimization techniques to address project needs. Derivative-free techniques like Markov Chain Monte Carlo (MCMC) are used for optimization and uncertainty quantification. Adjoint techniques derive analytical sensitivities directly from the flow equations. For history matching those sensitivities are efficiently used for property updates on grid block level. Both techniques have different characteristics and support alternative history matching strategies like global vs. local, stochastic vs. deterministic. In this work both techniques will be applied in an integrated workflow design to the Norne field. The Norne field is a North Sea oil-and-gas reservoir with approximately 30 wells, with one third being used for WAG injection for pressure support. Field data was previously released by Statoil and made available for a public benchmark study (NTNU Norway) testing history matching techniques including production and time-lapsed seismic data. We focus on well production data for history matching. MCMC is used for global parameter updates and uncertainty quantification in a Bayesian context. An implementation of an adjoint technique is applied for analytical sensitivity calculations and local parameter adjustments of rock properties. History matching results are presented for field wide and well-by-well production data. Consistency checks between updated and original geological model are presented for rock property distribution maps. Geostatistical measures including spatial correlations are used to quantify deviations between updated and original geological model. In conclusion scalability and performance efficiency of the practical workflow implementation is discussed with a perspective of a consistent feedback loop from history matching to geological modeling.

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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.

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