深度学习用于钻井前复杂地质预测
Deep learning for prediction of complex geology ahead of drilling
摘要：
在地质导向操作期间，响应于在钻井时获取的新数据而有意地调整井路径。为了实现一致的高质量决策，特别是在复杂环境中钻井时，决策支持系统可以帮助应对大量数据和解释复杂性。他们可以将实时测量同化到概率地球模型中，并使用更新的模型进行决策建议。最近，机器学习（ML）技术已经实现了将计算成本从在线计算重新分配到离线计算的各种方法。在本文中，我们介绍了两个ML技术的地质导向决策支持框架。首先，使用生成对抗网络（GAN）生成复杂的地球模型表示。其次，商业超深电磁模拟器表示使用前向深度神经网络（FDNN）。数值试验表明，在一个集合随机极大似然数据同化方案的组合的GAN和FDNN提供了复杂的地质不确定性的实时估计。这使得从井眼后面和周围收集的测量结果减少钻头前面的地质不确定性。
英文摘要：
During a geosteering operation the well path is intentionally adjusted in response to the new data acquired while drilling. To achieve consistent high-quality decisions, especially when drilling in complex environments, decision support systems can help cope with high volumes of data and interpretation complexities. They can assimilate the real-time measurements into a probabilistic earth model and use the updated model for decision recommendations. Recently, machine learning (ML) techniques have enabled a wide range of methods that redistribute computational cost from on-line to off-line calculations. In this paper, we introduce two ML techniques into the geosteering decision support framework. Firstly, a complex earth model representation is generated using a Generative Adversarial Network (GAN). Secondly, a commercial extra-deep electromagnetic simulator is represented using a Forward Deep Neural Network (FDNN). The numerical experiments demonstrate that the combination of the GAN and the FDNN in an ensemble randomized maximum likelihood data assimilation scheme provides real-time estimates of complex geological uncertainty. This yields reduction of geological uncertainty ahead of the drill-bit from the measurements gathered behind and around the well bore.
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