Abstract: This paper describes the identification of waterflooded zones and the impact of waterflooding on reservoir properties of sandstones of the Funing Formation at the Gao 6 Fault-block of the Gaoji Oilfield, in the Subei Basin, east China. This work presents a new approach based on a back-propagation neural network using well log data to train the network, and then generating a cross-plot plate to identify waterflooded zones. A neural network was designed and trained, and the results show that the new method is better than traditional methods. For a comparative study, two representative wells at the Gao 6 Fault-block were chosen for analysis: one from a waterflooded zone, and the other from a zone without waterflooding. Results from this analysis were used to develop a better understanding of the impact of waterflooding on reservoir properties. A range of changes are shown to have taken place in the waterflooded zone, including changes in microscopic pore structure, fluids, and minerals.

Key words: Waterflooding, Reservoir properties, Neural network, Gao 6 Fault-block, Gaoji Oilfield

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