Pseudo-sonic curve construction method based on pulse coupling neural network

Abstract

The invention belongs to the field of geophysical well logging and signal processing and provides a method for realizing pseudo-sonic curve construction by utilizing a pulse coupling neural network. The method comprises the following steps: introducing a sonic curve and other logging curves; pre-processing to remove noise components of each logging curve in the same depth section; carrying out normalized treatment on curve amplitudes; determining the structure of a network and initializing the network; respectively counting the time of an output value of each nerve cell, which is equal to 1; and determining the amplitudes normalized by a pseudo-sonic curve to obtain the pseudo-sonic curve. According to the invention, a pulse coupling neural network method is used for carrying out synthesis treatment on the sonic curve and the other logging curves; a sample is not needed and the network does not need to be trained; the iteration time is fewer and the data processing amount is less, so that the construction method of the pseudo-sonic curve has the characteristic of being simple, rapid and reliable, high in efficiency and accuracy, strong in processing capability, and very obvious in synthesis treatment effect on the logging curve with the greater data amount, and capability of effectively improving the resolution ratio and the precision of inversion of an earthquake reservoir stratum and the like.

Description

technical field [0001] The invention belongs to the application fields of geophysical well logging, oil and gas resource exploration and signal processing, in particular a method for realizing the construction of pseudoacoustic curves by using a pulse-coupled neural network. By adopting the method of the invention, various (types) of well logging curves and Corresponding acoustic curves are synthesized to construct pseudo-acoustic curves, thereby effectively improving the resolution and accuracy of seismic reservoir inversion and making up for the defects of conventional acoustic logging. Background technique [0002] Acoustic curves are the necessary basic data for wave impedance inversion based on logging constraints. However, due to the influence of non-formation lithology factors such as wellbore pollution, reservoir cementation degree and porosity, the original acoustic curves cannot reflect well The difference between the reservoir and the surrounding rock makes it dif...

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Problems solved by technology

[0034] The BP neural network method uses the nonlinear processing function of the neural network, which is easy to realize the construction of the quasi-acoustic curve, but its parameters are many, and the number of iterations is relatively large, and the error value e of the network output is easy to fall into the minimum value during the iteration process. That is to say, the change value of the error value e is not large in a long period of time, and it is difficult to reach the minimum target value; therefore, the above-mentioned BP neural network method requires a relatively long time to train the network, and the requirements for training samples are also relatively high, otherwise the constructed pseudophonic wave The accuracy of the curve is not high; and for the logging curve with a large amount of data, the efficiency of the BP neural network method is not high

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