Abstract: The quantum selforganizing network method was used to solve the problem of water-flooded layer identification in oil field logging interpretation. Firstly, the reservoir feature indicator set was constructed, including sequence indicators from the log curves and some single numerical indicators. For the sequence indicators, the discrete Walsh filtering method was first used to remove the noise in sequence indicators, then the average was taken, and then the reservoir feature vector was constructed together with other single numerical indicators. Secondly, the reservoir feature vectors were transformed into quantum state descriptions, and the quantum self-organizing networks were implemented clustering. Finally, the converged network was used as a mathematical model for waterflooded layer recognition in similar blocks of oil fields. The simulation results show that the accuracy of the proposed method is 6% higher than that of the traditional selforganizing clustering method.

Key words: waterflooded layer identification, selforganizing network, quantum selforganizing network, Walsh filtering, algorithm design