Closing the Loop on a History Match for a Permian EOR Field Using Relative Permeability Data Uncertainty

Reservoir production forecasts are inherently uncertain due to the lack of quality data available to build predictive reservoir models. Multiple data types, including historical production, well tests (RFT/PLT), and time-lapse seismic data, are assimilated into reservoir models during the history matching process to improve predictability of the model. Traditionally, a ‘best estimate’ for relative permeability data is assumed during the history matching process, despite there being significant uncertainty in the relative permeability. Relative permeability governs multiphase flow in the reservoir; therefore, it has significant importance in understanding the reservoir behavior as well as for model calibration and hence for reliable production forecasts. Performing sensitivities around the ‘best estimate’ relative permeability case will cover only part of the uncertainty space, with no indication of the confidence that may be placed on these forecasts. In this paper, we present an application of a Bayesian framework for uncertainty assessment and efficient history matching of a Permian CO2 EOR field for reliable production forecast. The study field has complex geology with over 65 years of historical data from primary recovery, waterflood, and CO2 injection. Relative permeability data from the field showed significant uncertainty, so we used uncertainties in the saturation endpoints as well as in the curvature of the relative permeability in multiple zones, by employing generalized Corey functions for relative permeability parameterization. Uncertainty in the relative permeability is used through a common platform integrator. An automated workflow generates the first set of relative permeability curves sampled from the prior distribution of saturation endpoints and Corey exponents, called ‘scoping runs’. These relative permeability curves are then passed to the reservoir simulator. The assumptions of uncertainties in the relative permeability data and other dynamic parameters are quickly validated by comparing the scoping runs and historical observations. By creating a mismatch or likelihood function, the Bayesian framework generates an ensemble of history matched models calibrated to the production data which can then be used for reliable probabilistic forecasting. Several iterations during the manual history match did not yield an acceptable solution, as uncertainty in the relative permeability was ignored. An application of the Bayesian inference accelerated by a proxy model found the relative permeability data to be one of the most influential parameters during the assisted history matching exercise. Incorporating the uncertainty in relative permeability data along with other dynamic parameters not only helped speed up the model calibration process, but also led to the identification of multiple history matched models. In addition, results show that the use of the Bayesian framework significantly reduced uncertainty in the most important dynamic parameters. The proposed approach allows incorporating previously ignored uncertainty in the relative permeability data in a systematic manner. The user-defined mismatch function increases the likelihood of obtaining an acceptable match and the weights in the mismatch function allow both the measurement uncertainty and the effect of simulation model inaccuracies. The Bayesian framework considers the whole uncertainty space and not just the history match region, leading to the identification of multiple history matched models.