Lithology identification is of great importance in reservoir characterization. Recently, many researchers have applied machine-learning techniques to solve lithology identification problems from well-log curves, and their works indicate three main characteristics. First, most works predict lithofacies using features measured during logging, whereas very few consider adding stratigraphic sequence information that is available prior to drilling to solve this problem. Second, most studies predict lithofacies using measured properties of one depth point, whereas few take the influence of the neighboring formation into account. Third, due to a lack of publicly available interpreted well-log data, previous research has concentrated on applying different algorithms on their private data set, making it impossible to perform a comparison. We have developed a machine-learning framework to solve the lithology classification problem from well-log curves by incorporating an additional geologic constraint. The constraint is a stratigraphic unit, and we use it as an additional feature. We evaluate three types of recurrent neural networks (RNNs), bidirectional long short-term memory, bidirectional gated recurrent unit (Bi-GRU), and GRU-based encoder-decoder architecture with attention, as well as two types of 1D convolutional neural networks (1D CNNs), temporal convolutional network and multiscale residual network, on a publicly available data set from the North Sea. The RNN-based networks and 1D CNN-based networks can process sequential data, enabling the model to have access to information from neighboring formations when performing lithofacies prediction at a particular depth. Our experiments indicate that geologic constraint improves the performance of the models significantly, and that the overall performance of RNN-based networks is better and more consistent.

中文翻译：

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通过具有附加地质约束的神经网络从测井曲线识别岩性

岩性识别在储层表征中具有重要意义。最近，许多研究人员应用机器学习技术解决测井曲线岩性识别问题，他们的工作表明了三个主要特征。首先，大多数工作使用测井期间测量的特征来预测岩相，而很少考虑添加钻井前可用的地层层序信息来解决这个问题。其次，大多数研究使用一个深度点的测量特性预测岩相，而很少考虑相邻地层的影响。第三，由于缺乏公开可用的解释性测井数据，以前的研究集中在他们的私有数据集上应用不同的算法，因此无法进行比较。我们开发了一个机器学习框架，通过结合额外的地质约束来解决测井曲线的岩性分类问题。约束是一个地层单位，我们将其用作附加特征。我们评估了三种类型的循环神经网络 (RNN)、双向长短期记忆、双向门控循环单元 (Bi-GRU) 和基于 GRU 的编码器-解码器架构，以及两种类型的一维卷积神经网络(1D CNNs)、时间卷积网络和多尺度残差网络，基于来自北海的公开数据集。基于 RNN 的网络和基于 1D CNN 的网络可以处理序列数据，使模型能够在特定深度进行岩相预测时访问来自相邻地层的信息。