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Seismic wave impedance thin layer inversion method based on full convolutional neural network

A convolutional neural network and seismic wave technology, applied in the field of seismic wave impedance thin-layer inversion based on full convolutional neural network, can solve the problems of high processing cost, low processing efficiency, and low precision, and achieve the effect of improving resolution

Pending Publication Date: 2021-11-09
YANGTZE UNIVERSITY
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Problems solved by technology

[0004] With the emergence of more and more complex thin layers, due to factors such as low processing efficiency, high processing cost, and low accuracy, the prediction of thin layers by conventional wave impedance inversion can no longer fully meet the production requirements

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  • Seismic wave impedance thin layer inversion method based on full convolutional neural network
  • Seismic wave impedance thin layer inversion method based on full convolutional neural network
  • Seismic wave impedance thin layer inversion method based on full convolutional neural network

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Embodiment Construction

[0039] The implementation of the present invention will be described in detail below in combination with the model data obtained from forward modeling, but they do not constitute a limitation of the present invention, and are only used as examples. At the same time, the advantages of the present invention will become clearer and easier to understand.

[0040] Such as figure 1 As shown, a method for thin layer inversion of seismic wave impedance based on a fully convolutional neural network in this embodiment includes the following steps:

[0041] S1: Extract the seismic model data and the corresponding wave impedance from the forward modeling data as a sample pair;

[0042] Since the section obtained by forward modeling is saved in the form of an array, a part is taken out from the whole seismic model data obtained by forward modeling and the corresponding wave impedance through the operation of slicing in python as a sample pair.

[0043] S2: Build a fully convolutional neu...

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Abstract

The invention relates to the technical field of reservoir prediction in seismic exploration, in particular to a seismic wave impedance thin layer inversion method based on a full convolutional neural network. According to the method, a model is obtained by training a seismic sample, predicted seismic data are input into the model, and a wave impedance inversion result is obtained through prediction. Through the full convolutional neural network, known seismic data is utilized to perform nonlinear modeling, and the full convolutional neural network is utilized to use zero filling to enable each output layer to keep the same size as an input layer to be continuously transmitted, so that the effect of improving the resolution is achieved, and an intelligent new method is provided for seismic wave impedance thin layer prediction.

Description

technical field [0001] The invention relates to the technical field of reservoir prediction in seismic exploration, in particular to a thin layer inversion method of seismic wave impedance based on a fully convolutional neural network. Background technique [0002] "Wave impedance inversion is the final expression form of high resolution seismic data processing" shows that seismic wave impedance inversion has a special status and is very important in seismic exploration technology. Wave impedance is a complex parameter closely related to formation velocity and density, and also closely related to formation lithology, which has a good correspondence with oil-bearing reservoirs. Wave impedance inversion is a kind of seismic lithology inversion. The inversion method is a process in which people use existing knowledge for seismic inversion modeling. Therefore, seismic wave impedance inversion is an extremely important and effective method for reservoir prediction. One of the me...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01V1/28G01V1/30G06K9/62G06N3/04G06N3/08
CPCG01V1/282G01V1/306G06N3/08G06N3/048G06N3/045G06F18/241
Inventor 许辉群王泽峰
Owner YANGTZE UNIVERSITY