Pumping unit noise positioning method based on deep learning

Abstract

The invention discloses a pumping unit noise positioning method based on deep learning. The method comprises the following steps: S1, partitioning seismic data according to columns by using single-step sliding of a sliding window to obtain a plurality of local seismic trace sets with fixed sizes, if the number of seismic traces containing pumping unit noise reaches a threshold Th1, marking the number as 1, and if the number of seismic traces containing pumping unit noise is lower than a threshold Th2, marking the number as 0; S2, preprocessing the local seismic trace gather, calculating an energy spectrum of the local seismic trace gather, carrying out mean filtering, then carrying out downsampling, randomly selecting part of data as a training set, and taking the rest part as a test set;S3, establishing a deep CNN network, training the CNN network by using the training set obtained in the step S2, and supplementing the training set with misclassified data for repeated training in thetraining process; S4, testing the CNN trained in the step S3 by using a test sample, and quantitatively evaluating the positioning function of the CNN; and S5, performing width estimation on the positioned noise of the oil pumping unit according to the positioning result in the step S4.

Description

technical field [0001] The invention relates to the technical field of geophysical and seismic data detection and analysis, in particular to a method for locating noise of pumping units based on deep learning. It is a means of intelligently locating the pumping unit noise in the seismic data and adaptively determining the filter parameters. Background technique [0002] Pumping unit noise is an important factor affecting secondary high-precision exploration and deep seismic exploration in old oilfields. Its shape is as follows: figure 1 shown. The existence of pumping unit noise seriously reduces the resolution of seismic data and increases the difficulty of subsequent seismic data processing. Shutting down the pumping machine seriously affects the production efficiency and the cost is high, so the follow-up processing of the seismic data is very important. At present, the noise suppression methods of pumping units are mostly blind source separation, abnormal amplitude at...

AI Technical Summary

Problems solved by technology

The existence of pumping unit noise seriously reduces the resolution of seismic data and increases the difficulty of subsequent seismic data processing

Shutting down the oil pumping machine seriously affects the production efficiency and the cost is high, so the subsequent processing of the seismic data is very important

At present, the noise suppression methods of pumping units are mostly blind source separation, abnormal amplitude attenuation, etc.

These methods use fixed parameters for noise filtering, which is likely to cause noise residue and loss of effective signals. Therefore, the present invention proposes a deep convolutional neural network (Convolutional Neural Network, CNN) to automatically locate the noise of the pumping unit, and use A method for estimating the noise width of pumping units after image post-processing, which is used for adaptively estimating the parameters of noise filtering

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