Pre-stack non-linear fluid identification method for fuzzy neural network of chaotic quantum-behaved particle swarm

Abstract

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.

Description

technical field [0001] The invention belongs to the field of petroleum exploration, relates to the identification of fluid properties by using a chaotic quantum particle swarm fuzzy neural network nonlinear optimization algorithm, and provides a new technical method for fluid identification. Background technique [0002] With the continuous deepening of oil and gas exploration and development, the requirements for identifying fluids in reservoirs are also higher, but it is also very difficult. Using seismic data to identify fluids in reservoirs is the most important research work in oil and gas exploration. After multiple stacking of post-stack seismic data, the signal-to-noise ratio has been greatly improved, but at the same time, a large amount of amplitude information has been lost. It is difficult to accurately determine the filling properties of fluid in the reservoir through post-stack techniques Judgment has led to many examples in actual exploration where reservoirs...

AI Technical Summary

Problems solved by technology

Through the improvement of the algorithm of the present invention, a new nonlinear swarm intelligence optimization algorithm of "chaotic quantum particle swarm fuzzy neural network" has been developed, which fundamentally improves the problems of poor global search ability and premature conver

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