论文标题
部分可观测时空混沌系统的无模型预测
Verification of a real-time ensemble-based method for updating earth model based on GAN
论文作者
论文摘要
地理编码工作流的复杂性是钻探过程中实时量化和更新不确定性的限制因素。我们提出了用于参数化和生成地质模型的生成对抗网络（GAN），并结合集合随机最大似然（ENRML），以快速更新地下不确定性。这种实时集合方法与神经网络建模序列产生的高度非线性模型相结合可能会产生不准确和/或偏置后溶液。本文说明了在几个示例中ENRML的预测能力，在这些示例中，我们吸收了局部深层电磁原木。使用MCMC的统计验证证实，所提出的工作流可以产生晶孔井所需的可靠结果。
The complexity of geomodelling workflows is a limiting factor for quantifying and updating uncertainty in real-time during drilling. We propose Generative Adversarial Networks (GANs) for parametrization and generation of geomodels, combined with Ensemble Randomized Maximum Likelihood (EnRML) for rapid updating of subsurface uncertainty. This real-time ensemble method combined with a highly non-linear model arising from neural-network modeling sequences might produce inaccurate and/or biased posterior solutions. This paper illustrates the predictive ability of EnRML on several examples where we assimilate local extra-deep electromagnetic logs. Statistical verification with MCMC confirms that the proposed workflow can produce reliable results required for geosteering wells.
