Deep learning applied to seismic attribute computation

Abstract

We have trained deep convolutional neural networks (DCNs) to accelerate the computation of seismic attributes by an order of magnitude. These results are enabled by overcoming the prohibitive memory requirements typical of 3D DCNs for segmentation and regression by implementing a novel, memory-efficient 3D-to-2D convolutional architecture and by including tens of thousands of synthetically generated labeled examples to enhance DCN training. Including diverse synthetic labeled seismic in training helps the network generalize enabling it to accurately predict seismic attribute values on field-acquired seismic surveys. Once trained, our DCN tool generates attributes with no input parameters and no additional user guidance. The DCN attribute computations are virtually indistinguishable from conventionally computed attributes while computing up to 100 times faster.

Research on prediction methods of formation pore pressure based on machine learning

“…For example, it has been successfully applied in several research fields such as fluid flow rate prediction, [19][20][21][22][23][24][25] crude oil physical property prediction, [26][27][28] rock lithology physical property prediction, [29][30][31][32] and geophysical data processing. [33][34][35][36] Some studies have been conducted on the prediction of formation pressure. Keshavarzi et al 37 developed a backpropagation artificial neural network (BPANN) to predict the Pp gradient in the Asmari (Oligocene) and reservoir (Cretaceous) in the Asmari (Summer) area.…”

“…Therefore, to overcome this limitation of parametric models, some scholars have introduced machine learning techniques into the field of petroleum. For example, it has been successfully applied in several research fields such as fluid flow rate prediction, 19–25 crude oil physical property prediction, 26–28 rock lithology physical property prediction, 29–32 and geophysical data processing 33–36 …”

Comparison of Convolutional Neural Networks for Dividing Seismic Sequences

“…. 탄성파 탐사 분야에서는 현재까지 탄성 파 상 분석 (Wrona et al, 2018;Keynejad et al, 2019), 저류 층 특성화 (Chaki, 2015;Tian and Daigle, 2018;Smith et al, 2019), 단층해석 (Zhang et al, 2014;Xiong et al, 2018;Kumar and Sain, 2018), 암염구조 해석 (Guillen et al, 2015;Shi, et al, 2019;Sen et al, 2019), 탄성파 속성분석 (Zhao, 2018b;Griffith et al, 2019) (Bengio et al, 1994;Glorot and Bengio, 2010…”