An empirical method for predicting waterflooding performance in low-permeability porous reservoirs combining static and dynamic data: a case study in Chang 6 formation, Jingan Oilfield, Ordos Basin, China

After waterflooding, the distribution of the remaining oil in low-permeability porous reservoirs is quite complicated. Strong heterogeneity of formations makes the waterflooding performance more complex. Therefore, accurate prediction and evaluation of the spatial distribution of the remaining oil and the waterflooding performance of low-permeability reservoirs are essential for understanding the waterflooding process and improving oil recovery. In the study, an empirical method is proposed to predict waterflooding performance combined with static and dynamic data for porous reservoirs. Static data, including logging curves, core porosity and permeability data, are adopted to classify the formation into three hydraulic flow units (HFUs). The proportions of the thicknesses of different HFUs (HFUp) are proposed to characterize the remaining oil distribution. In addition, a waterflooding performance prediction method based on the Koval method was built using dynamic production data. The results show that the HFUp plays the key role in predicting the distribution of the remaining oil in the research well group. The K-factor-based waterflooding prediction method is highly correlated with the history matching in low-permeability waterflooded layers. The study also found Type 3 HFUp shows a great effect in predicting the duration of the low water-cut oil production. Therefore, the empirical method can provide a quick and intuitive evaluation of waterflooding performance in space and time of low-permeability waterflooded reservoirs with the local average K-factor and the HFUp results. The empirical method is of great significance to evaluate the remaining oil, infilling of well pattern, and improving oil recovery.