Seismic waveform clustering method and device

A technology of seismic waveforms and clustering methods, applied in seismic signal processing, neural learning methods, character and pattern recognition, etc., can solve problems such as poor classification results

Pending Publication Date: 2021-04-20
BC P INC CHINA NAT PETROLEUM CORP +1
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Problems solved by technology

However, due to the sparseness of well logging data relative to seismic data, well logging data can only represent loc

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  • Seismic waveform clustering method and device
  • Seismic waveform clustering method and device
  • Seismic waveform clustering method and device

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Embodiment Construction

[0029] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.

[0030] like figure 1 As shown in the schematic diagram of a method for clustering seismic waveforms in an embodiment of the present invention, an embodiment of the present invention provides a method for clustering seismic waveforms to achieve high-precision waveform classification. The method includes:

[0031] Step 101: Determine the seismic waveform data and the seismic phase label of the seismic waveform data according to the drilling and logging data;

[0032] Step 102: delineate a set area in the target work area, and extract unlabeled seismic phase wavef...

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Abstract

The invention provides a seismic waveform clustering method and device, and the method comprises the steps of: determining seismic waveform data and a seismic facies label of the seismic waveform data according to drilling and logging information; delineating a set area in a target work area, and extracting label-free seismic facies waveform data of the set area; determining labeled seismic facies data according to the seismic waveform data and the unlabeled seismic facies waveform data; performing multi-scale discretization processing on the labeled seismic facies data, and determining multi-scale labeled waveform data; training a recurrent neural network model according to the multi-scale labeled waveform data and the seismic facies label of the seismic waveform data, and determining the trained recurrent neural network model; and inputting the seismic trace of a target work area into the trained recurrent neural network model, and determining a seismic facies classification result of the target work area. According to the invention, the semi-supervised learning method of the recurrent neural network model generative adversarial network is used to realize rapid and precise classification of the seismic facies of the target work area.

Description

technical field [0001] The invention relates to the technical field of oil and natural gas development, in particular to a seismic waveform clustering method and device. Background technique [0002] The overall change of seismic waveform is a comprehensive reflection of seismic wave amplitude, frequency, and phase, and is an important seismic attribute parameter. In the process of exploration and development of complex lithology reservoirs, seismic waveform classification technology is an effective and fast method to predict the spatial distribution of reservoirs. [0003] Seismic waveform classification in the prior art mainly uses neural network technology or cluster analysis technology to classify the shape of the seismic trace (the overall change of the seismic signal). First, several typical seismic trace shapes are divided, and each actual seismic trace is endowed with a typical shape based on similarity; the neural network trains the actual seismic traces in a given...

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Application Information

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IPC IPC(8): G01V1/28G01V1/30G06K9/62G06N3/04G06N3/08
Inventor 林煜李磊臧殿光郁智贺川航王雪梅
Owner BC P INC CHINA NAT PETROLEUM CORP
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