Well logging helps geologists find hidden oil, natural gas and other resources. However, well log data are systematically insufficient because they can only be obtained by drilling, which involves costly and time-consuming field trials. Additionally, missing or distorted well log data are common in old oilfields owing to shutdowns, poor borehole conditions, damaged instruments and so on. As a workaround, pseudo-data can be generated from actual field data. In this study, we propose a spatio-temporal neural network (STNN) algorithm, which is built by leveraging the combined strengths of a convolutional neural network (CNN) and a long short-term memory network (LSTM). The STNN exploits the ability of the CNN to effectively extract features related to pseudo-well log data and the ability of the LSTM to extract the key features from well log data along the depth direction. The STNN method allows full consideration of the well log data trend with depth, the correlation across different log series and the actual depth accumulation effect. The method proved successful in predicting acoustic sonic log data from gamma-ray, density, compensated neutron, formation resistivity and borehole diameter logs. Results show that the proposed method achieves higher prediction accuracy because it takes into account the spatio-temporal information of well logs.

中文翻译：

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一种基于时空神经网络的测井数据生成方法

测井有助于地质学家发现隐藏的石油、天然气和其他资源。然而，测井数据系统性不足，因为它们只能通过钻井获得，这涉及昂贵且耗时的现场试验。此外，由于停工、井眼条件差、仪器损坏等原因，老油田的测井数据丢失或失真也很常见。作为一种解决方法，可以从实际现场数据生成伪数据。在这项研究中，我们提出了一种时空神经网络 (STNN) 算法，该算法是通过利用卷积神经网络 (CNN) 和长短期记忆网络 (LSTM) 的综合优势构建的。STNN 利用 CNN 有效提取与伪测井数据相关的特征的能力和 LSTM 从测井数据中沿深度方向提取关键特征的能力。STNN方法可以充分考虑测井数据随深度变化的趋势、不同测井系列之间的相关性以及实际的深度积累效应。该方法成功地预测了伽马射线、密度、补偿中子、地层电阻率和井眼直径测井的声波测井数据。结果表明，所提方法考虑到了测井的时空信息，具有较高的预测精度。不同测井系列之间的相关性和实际深度累积效应。该方法成功地预测了伽马射线、密度、补偿中子、地层电阻率和井眼直径测井的声波测井数据。结果表明，所提方法考虑到了测井的时空信息，具有较高的预测精度。不同测井系列之间的相关性和实际深度累积效应。该方法成功地预测了伽马射线、密度、补偿中子、地层电阻率和井眼直径测井的声波测井数据。结果表明，所提方法考虑到了测井的时空信息，具有较高的预测精度。