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首页> 外文期刊>Journal of natural gas science and engineering >Downhole working conditions analysis and drilling complications detection method based on deep learning
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Downhole working conditions analysis and drilling complications detection method based on deep learning

机译：基于深度学习的井下工作条件分析与钻探并发症检测方法

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摘要

Drilling complications, which are usually hard to be discovered in time using the traditional surface detecting methods, result in much time and money wasted in handling these problems. Restricted to data transmission speed with the measurement while drilling (MWD), downhole measured data is usually ignored in downhole complications detection. And the surface detection methods with some pressure and rate of flow sensors always demand much professional knowledge and contain detection delay. In this paper, we used the measured downhole parameters to discover the drilling complications combined with deep leaning methods. Firstly, we described the difficulties of applying deep learning methods into the exploring drilling data. Then we used wavelet decomposition and reconstruction method to reduce the influence of the data trend with well depth and remove the high frequency noise. The fluctuation items coupling analysis method, consisted with rock breaking theory and transient fluctuating pressure theory, was established to make sure whether the wavelet reconstruction results contain the information to do detection. We applied a deep learning method called Bidirectional Generative Adversarial Network (BiGAN) in complications detection. BiGAN can distinguish whether the data belongs to normal working condition data or not. An end to end deep neural network mainly composed with one dimensional convolutional neural network was established to determine the specific kind of normal working condition. Then, large numbers of real field drilling data collected by the measuring tool were used to test the detection method. The testing results indicated that BiGAN indeed learned the normal working condition data distribution and the end to end network performed high accuracy in the normal working conditions classification. Therefore, we chose the combination of BiGAN and the supervised neural network to detect drilling complications with six field cases. The experiment results showed that the detection method can detect the complications much earlier than the surface detection results except for nozzle clogging case.
机译：钻孔并发症，通常使用传统的表面检测方法及时难以在时间内发现，导致在处理这些问题时浪费的时间和金钱浪费。在钻孔（MWD）的同时，在测量时限制数据传输速度，通常在井下并发症检测中忽略井下测量数据。和表面检测方法具有一些压力和流量传感器的速率总是需要很多专业知识并包含检测延迟。在本文中，我们使用了测量的井下参数来发现钻孔并发症与深层倾斜的方法相结合。首先，我们描述了将深度学习方法应用于探索钻井数据的困难。然后我们使用小波分解和重建方法来减少数据趋势的影响，深入深度，消除高频噪声。建立了波动项目耦合分析方法，构建了岩石破碎理论和瞬态波动理论，以确保小波重建结果是否包含要做检测的信息。我们应用了一种深入的学习方法，称为双向生成对抗性网络（BIGAN）并发症检测。 BIGAN可以区分数据是否属于正常工作条件数据。建立了一个端到端深度神经网络，主要建立了一个维卷积神经网络，以确定具体的正常工作条件。然后，测量工具收集的大量实地钻井数据用于测试检测方法。测试结果表明，BIGAN确实学习了正常的工作条件数据分布，并且结束到终端网络在正常工作条件分​​类中执行了高精度。因此，我们选择了BIGAN和监督神经网络的结合，以检测六个现场情况的钻探并发症。实验结果表明，除了喷嘴堵塞外壳外，检测方法可以检测比表面检测结果更早的并发症。

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