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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >DeepLog: Identify Tight Gas Reservoir Using Multi-Log Signals by a Fully Convolutional Network
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DeepLog: Identify Tight Gas Reservoir Using Multi-Log Signals by a Fully Convolutional Network

机译：Deeplog：通过完全卷积的网络使用多记录信号识别紧的气体储层

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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.
机译：在大多数情况下，可以通过在仅此深度点处的信号来阐述层中一定深度的一个储层属性。实际上，复杂储存器的特性通常涉及来自该某一深度点的整个相邻区域的测量信号。到目前为止，没有有效的方法可以完全解决这个问题。这封信首次试图建立一个完全卷积的神经网络（FCNN）来检测来自鄂尔多斯盆地的紧的气体储层的测井信号的碳氢化合物。 FCNN基于精心设计的VGG网。经验方法（EMA）和FCNN之间的预测比较在48层上实施。 FCNN的准确性约为87.5％，高于EMA（75.0％）。 FCNN提供了更可靠的气体测试推荐，特别是当薄层导致复杂的储层条件时。已被证明是深度学习（DL）是一种自动特征提取和伐木信号的直接碳氢化合物检测方法。我们期待其在地球物理中的改善和发展。

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