基于小波去噪神经网络在数字岩心的应用
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东北石油大学石油工程学院

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TP391. 41

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万米深井PDC钻头冲击破岩机理及提速方法研究


Application of Wavelet Denoising Neural Network in Digital Core
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School of Petroleum Engineering, Northeast Petroleum University

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    摘要:

    以鄂尔多斯盆地某区块的砂泥夹层岩心为研究对象,使用基于小波变换的去噪神经网络(DWTNet)对于岩心的图像进行去噪研究。该方法的评断结果采用峰值信噪比(PSNR)和去噪后的图像结果进行了对比。研究表明,利用小波变换的去噪神经网络(DWTNet)在测试集YX1、YX2测试所提出的算法,并与EGDNet等去噪算法进行对比,PSNR在噪声为25,50,75时,高于EGDNet算法0.527 dB ,0.418 dB ,1.1 dB 。所提的算法在峰值信噪比等指标均高于其他算法;并在视觉效果上其处理得到的图像也更加清晰。方法的提出对于孔隙度、平均体积比表面积,平均曲率计算等都有着非常重要的意义。

    Abstract:

    A denoising neural network based on wavelet transform (DWTNet) was used to study the denoising of core images in a sand and mud interlayer core of a certain block in the Ordos Basin. The evaluation results of this method were compared using peak signal-to-noise ratio (PSNR) and denoised image results. Research has shown that the proposed algorithm using wavelet transform denoising neural network (DWTNet) was tested on test sets YX1 and YX2, and compared with denoising algorithms such as EGDNet. When the noise levels were 25, 50, and 75, PSNR was 0.527 dB , 0.418 dB , and 1.1 dB higher than the EGDNet algorithm. The proposed algorithm outperforms other algorithms in terms of peak signal-to-noise ratio and other indicators; And in terms of visual effects, the processed images are also clearer. The proposal of methods is of great significance for porosity, average volume specific surface area, and average curvature calculation.Keywords: digital core technology, Sandstone, Wavelet transform, Neural network, Denoising; Rock characteristi

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何田素,李玮,盖京明,等. 基于小波去噪神经网络在数字岩心的应用[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-10-25
  • 最后修改日期:2024-05-23
  • 录用日期:2024-05-29
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