Intelligent optimized earthquake multi-attribute fusion method based on crack model

A technology of intelligent optimization and fusion method, applied in seismic signal processing and other directions, to achieve the effect of strong anti-interference ability and reduction of multi-solution

Inactive Publication Date: 2016-06-08
北京石大创新石油科技有限公司
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  • Intelligent optimized earthquake multi-attribute fusion method based on crack model
  • Intelligent optimized earthquake multi-attribute fusion method based on crack model
  • Intelligent optimized earthquake multi-attribute fusion method based on crack model

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[0017] The present invention will be further described below with reference to specific examples.

[0018] An intelligent optimal seismic multi-attribute fusion method based on a fracture model in the embodiment of the present invention, the method mainly uses a probabilistic neural network (PNN), and in the process of fracture parameter prediction, based on the forward modeling based on the fracture model, it can be used The non-linear expansion of the algorithm, the combined optimization of multiple attributes of fractures, and multiple training and probability estimations can effectively reduce the multiple solutions of different scale fracture predictions; the data used for the training of the probabilistic random neural network PNN includes a series of training "instances" ”, in the analysis time window, each seismic sample point has a corresponding well:

[0019] {A 11 , A 21 , A 31 , L 1}

[0020] {A 12 , A 22 , A 32 , L 2}

[0021] {A 13 , A 23 , A 33 , L ...

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Abstract

The invention relates to an intelligent optimized multi-attribute fusion method based on a crack model. The method comprises the following steps that S1) earthquake and logging data is loaded and calibrated; S2) a logging curve is corrected manually to ensure that the logging data matches the earthquake data; S3) a crack density curve is calculated, and different logging response and different earth physical response caused by different sizes of cracks are analyzed; S4) according to the earth physical response of the cracks of different sizes, logging is combined with earthquake to establish a crack network forward model, and optimized crack sensitive attributes are analyzed; S5) a crack sensitive parameter is introduced, and earthquake data is trained near a well point; and S6) the trained nonlinear relation is applied to the whole earthquake data body, and the aim of predicting the crack variable parameter is achieved. The fusion method has the advantages that the multiple crack attributes are combined and optimized via nonlinear expansion of the algorithm, and the multi-solution performance of prediction of cracks of different sizes is effectively reduced via multiple times of training learning and probability estimation.

Description

technical field [0001] The invention relates to an intelligent optimization seismic multi-attribute fusion method based on a fracture model. Background technique [0002] Seismic exploration has always been the main method to reduce oil and gas exploration risks and production costs. Since the 1980s, research on how to use seismic data to quantitatively detect fractured oil and gas reservoirs has gradually attracted the attention of major oil companies in the world. By the late 1990s, the academic and industrial circles basically reached a consensus that using seismic data (including longitudinal waves and shear waves) can effectively predict the direction and relative distribution of fractures. In terms of fracture detection methods, the effect of shear wave exploration should be better than that of longitudinal waves Good, but more difficult, such as strong destructive source, low signal-to-noise ratio, long record, difficult static correction, high cost, etc. Neverthele...

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Application Information

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IPC IPC(8): G01V1/28
Inventor 边树涛李国超
Owner 北京石大创新石油科技有限公司
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