In most cases, reservoir properties at one certain depth in the layer can be explicated by logging signals at just this depth point. In fact, the properties of complex reservoirs are often implicated in logging signals from the whole adjacent region of this certain depth point. So far, there is no effective way to solve this problem completely. For the first time, this letter tried to build a fully convolutional neural network (FCNN) to detect hydrocarbon from logging signals for the tight gas reservoir of Ordos Basin. The FCNN was based on a well-designed VGG-net. The prediction comparison between the empirical approach (EMA) and FCNN was implemented on 48 layers. The accuracy of FCNN was about 87.5%, which was higher than that of the EMA (75.0%). FCNN provided more reliable gas testing recommendations, especially when thin layers led to complex reservoir conditions. Deep learning (DL) has been proven to be an automatic feature extraction and direct hydrocarbon detection approach from logging signals. We are looking forward to its improvement and development in geophysics.

中文翻译：

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DeepLo​​g：通过全卷积网络使用多对数信号识别致密气藏

在大多数情况下，可以通过仅在该深度点的测井信号来解释层中某一深度处的储层特性。事实上，复杂储层的特性往往与来自该特定深度点的整个相邻区域的测井信号有关。到目前为止，还没有有效的方法可以彻底解决这个问题。这封信首次尝试构建全卷积神经网络（FCNN），从测井信号中检测鄂尔多斯盆地致密气藏油气。FCNN 基于精心设计的 VGG-net。经验方法 (EMA) 和 FCNN 之间的预测比较是在 48 层上实现的。FCNN 的准确率约为 87.5%，高于 EMA 的准确率（75.0%）。FCNN 提供了更可靠的气体检测建议，特别是当薄层导致复杂的储层条件时。深度学习 (DL) 已被证明是一种自动特征提取和直接从测井信号中检测碳氢化合物的方法。我们期待着它在地球物理学方面的改进和发展。