A data-space inversion (DSI) method has been recently proposed and successfully applied to the history matching and production prediction of reservoirs. Based on Bayesian theory, DSI can directly and effectively obtain good posterior flow predictions without inversion of geological parameters of reservoir model. This paper presents an improved DSI method to fast predict reservoir state fields (e.g. saturation and pressure profiles) via observed production data. Firstly, a large number of

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... ion curves and state data are generated by reservoir model simulation to expand the data space of original DSI. Then, efficient history matching only on the observed production data is carried out via the original DSI to obtain related parameters which reflects the weight of the real reservoir model relative to prior reservoir models. Finally, those parameters are used to predict the oil saturation and pressure profiles of the real reservoir model by combining large amounts of state data of prior reservoir models. Two examples including conventional heterogeneous and J o u r n a l P r e -p r o o f unconventional fractured reservoir are implemented to test the performances of predicting saturation and pressure profiles of this improved DSI method. Besides, this method is also tested in a real field and the obtained results show the high computational efficiency and high accuracy of the practical application of this method.