This reseach project investigates the application of the ensemble Kalman filter (EnKF) to the integration of well test data into heterogeneous reservoir models generated from geological and geophysical data. EnKF does not require computing the gradient of an objective function, and hence can be applied easily with any reservoir simulator, and more importantly, is far more eficient than a gradient-based history matching procedure when the forward model is represented by a reservoir simulator. In the procedure for integrating pressure transient data considered here, the static geological/geophysical data are assumed to be encapsulated in a multivariate probability density function characterized by a prior mean and covariance for the joint distribution of the porosity and permeability fields. As the prior mean of the property fields obtained from the core and log data can sometimes be erroneous, a partially doubly stochastic model is applied to account for the uncertainty of the prior mean. In the doubly stochastic model, a correction to the prior mean is adjusted together with the heterogeneous field during history matching. In this this research project, we also investigate the assimilation of production logging data (layer flow rates) in highly heterogeneous reservoirs.
Example of assimilation of pressure transient data using EnKF. In order to capture the transient behavior of the reservoir, it is necessary to use several levels of local grid refinements in the reservoir simulation model.
Recent publications:
- Coutinho, E.; Emerick, A.A.; Li, G. and Reynolds, A.C.: Conditioning Multilayered Geologic Models to Well-Test and Production-Logging Data Using the Ensemble Kalman Filter – paper SPE 134542, SPE Annual Technical Conference and Exhibition held in Florence, Italy, 19-22 September 2010.
- Li, G.; Han, M.; Banerjee, R. and Reynolds, A.C.: Integration of Well Test Pressure Data Into Heterogeneous Geological Reservoir Models, SPE Reservoir Evaluation and Engineering, 2010.