The invention relates to a pre-stack non-linear fluid identification method for a
fuzzy neural network of a
chaotic quantum-behaved particle swarm. Fluid identification is always a key point and difficult point problem in the oil-gas exploration field. By aiming at deficiency in the common fluid identification method at present, a multi-attribute angle gather combination fluid identification factor is built by researching an AVO (
amplitude versus offset) response characteristic comprising different fluids; a chaos search mechanism, a
quantum-behaved particle swarm and a fuzzy
system theory are organically combined to fully perform respective advantages and complementarities of the chaos search mechanism, the
quantum-behaved particle swarm and the fuzzy
system theory; a novel group intelligent optimization
algorithm of a ''
chaotic quantum-behaved particle swarm fuzzy
system'' is developed and researched, and a mechanism and an optimizing performance of the pre-stack non-linear fluid identification method are researched from two aspects of the theory and practicality; problems of poor global search capability,
premature convergence and the like in a traditional optimization
algorithm are fundamentally improved; the optimization
algorithm is introduced into fluid identification to form the pre-stack non-linear fluid identification method for the
fuzzy neural network of the
chaotic quantum-behaved particle swarm; the problem existing when a traditional fluid detection means is used for carrying out fluid identification is effectively solved; fluid identification precision is improved; and a new scientific and effect technical method is provided for the fluid identification.