The real-time interpretation of the logging-while-drilling data allows us to estimate the positions and properties of the geological layers in an anisotropic subsurface environment. Robust real-time estimations capturing uncertainty can be very useful for efficient geosteering operations. However, the model errors in the prior conceptual geological models and forward simulation of the measurements can be significant factors in the unreliable estimations of the profiles of the geological layers. The model errors are specifically pronounced when using a deep-neural-network (DNN) approximation which we use to accelerate and parallelize the simulation of the measurements. This paper presents a practical workflow consisting of offline and online phases. The offline phase includes DNN training and building of an uncertain prior near-well geo-model. The online phase uses the flexible iterative ensemble smoother (FlexIES) to perform real-time assimilation of extra-deep electromagnetic data accounting for the model errors in the approximate DNN model. We demonstrate the proposed workflow on a case study for a historic well in the Goliat Field (Barents Sea). The median of our probabilistic estimation is on-par with proprietary inversion despite the approximate DNN model and regardless of the number of layers in the chosen prior. By estimating the model errors, FlexIES automatically quantifies the uncertainty in the layers' boundaries and resistivities, which is not standard for proprietary inversion.

翻译：随钻测井数据的实时解释使我们能够在各向异性的地下环境中估算地质层位的位置与属性。能够捕捉不确定性的稳健实时估计对于高效地质导向作业具有重要价值。然而，先验概念地质模型中的建模误差以及测量的正演模拟可能成为地质层位剖面不可靠估计的关键因素。当我们采用深度神经网络近似方法来加速并行化测量模拟时，此类建模误差尤为显著。本文提出一种包含离线与在线阶段的实用工作流程。离线阶段涵盖DNN训练及近井地质不确定性先验模型构建；在线阶段采用灵活迭代集合平滑器，在考虑近似DNN模型误差的前提下实现超深电磁数据的实时同化。我们在巴伦支海Goliat油田的历史井案例研究中验证了该工作流程。尽管采用近似DNN模型且无论先验模型中设置多少层位，我们的概率估计中值结果与专用反演方法性能相当。FlexIES通过估计建模误差，自动量化了层位边界与电阻率的不确定性，而这在专用反演中并非标准流程。