An ensemble-based technique has been developed and successfully applied to simultaneously estimate the relative permeability and capillary pressure in a tight formation by history matching the conventional measurement data from displacement experiments. Relative permeability and capillary pressure curves are represented by the power-law model. Then, the to-be-estimated coefficients of the power-law model are tuned automatically and finally determined once the measurement data have been assimilated completely and history matched. This new technique has been validated by a synthetic coreflooding experiment and then extended to a real coreflooding experiment. Simultaneous estimation of relative permeability and capillary pressure has been found to improve, while standard deviation of the estimated coefficients is reduced gradually as more measurement data is assimilated. There exists an excellent agreement between both the updated relative permeability and capillary pressure and their corresponding reference values, once all the measurement data are assimilated. The relative permeability can be determined more accurately than the capillary pressure owing to the fact that it is more sensitive to the conventional measurement data. This newly developed technique has good computation efficiency and is suitable for performing uncertainty analysis under the same framework.

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