基于循环神经网络的测井曲线生成方法

Synthetic well logs generation via Recurrent Neural Networks

为了在不增加经济成本的基础上补充缺失的测井信息,提出利用机器学习方法根据已有的部分测井曲线生成人工测井曲线,并进行了实验验证和应用效果分析。考虑到传统全连接神经网络(FCNN)无法描述数据的空间相关性,基于一种循环神经网络(RNN)即长短期记忆神经网络(LSTM)来构建测井曲线生成方法。该方法生成的曲线不仅考虑了不同测井曲线的内在联系,同时兼顾了测井信息随深度的变化趋势和前后关联。将标准LSTM与串级系统相结合,提出了一种串级长短期记忆神经网络(CLSTM)。采用真实测井数据进行实验,LSTM明显优于传统FCNN,生成的测井数据精度更高;CLSTM更适用于测井曲线生成这种多序列数据问题;提出的基于机器学习的人工测井曲线生成方法更准确经济。

To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network （FCNN） is incapable of preserving spatial dependency, the Long Short-Term Memory （LSTM） network, which is a kind of Recurrent Neural Network （RNN）, was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that： the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.