Uncertainty Quantification and Management in Model Calibration andHistory Matching with Ensemble Kalman Methods

History matching field performance is a time-consuming, complex and non-unique inverse problem thatyields multiple plausible solutions. This is due to the inherent uncertainty associated with geological andflow modeling. The history matching must be performed diligently with the ultimate objective of availingreliable prediction tools for managing the oil and gas assets. Our work capitalizes on the latest developmentin ensemble Kalman techniques, namely, the Ensemble Kalman Filter and Smoother (EnKF/S) to properlyquantify and manage reservoir models- uncertainty throughout the process of model calibration and historymatching. Sequential and iterative EnKF/S algorithms have been developed to overcome the shortcomings of theexisting methods such as the lack of data assimilation capabilities and abilities to quantify and manageuncertainties, in addition to the huge number of simulations runs required to complete a study. An initialensemble of 40 to 50 equally probable reservoir models was generated with variable areal, verticalpermeability and porosity. The initial ensemble captured the most influencing reservoir properties, whichwill be propagated and honored by the subsequent ensemble iterations. Data misfits between the fieldhistorical data and simulation data are calculated for each of the realizations of reservoir models to quantifythe impact of reservoir uncertainty, and to perform the necessary changes on horizontal, vertical permeabilityand porosity values for the next iteration. Each generation of the optimization process reduces the datamisfit compared to the previous iteration. The process continues until a satisfactory field level and welllevel history match is reached or when there is no more improvement. In this study, an application of EnKF/S is demonstrated for history matching of a faulted reservoirmodel under waterflooding conditions. The different implementations of EnKF/S were compared. EnKF/Spreserved key geological features of the reservoir model throughout the history matching process. Duringthis study, EnKF/S served as a bridge between classical control theory solutions and Bayesian probabilisticsolutions of sequential inverse problems. EnKF/S methods demonstrated good tracking qualities whilegiving some estimate of uncertainty as well. The updated reservoir properties (horizontal, vertical permeability and porosity values) are conditionedthroughout the EnKF/S processes (cycles), maintaining consistency with the initial geologicalunderstanding. The workflow resulted in enhanced history match quality in shorter turnaround time withmuch fewer simulation runs than the traditional genetic or Evolutionary algorithms. The geological realismof the model is retained for robust prediction and development planning.