具有不确定性量化和深度学习功能的战略地质导向工作流程:对戈里亚特野外数据的初步测试

GEOPHYSICS Pub Date : 2024-07-14 DOI:10.1190/geo2023-0576.1
M. H. Rammay, S. Alyaev, David Larsen, R. Bratvold, S. Alyaev
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引用次数: 0

摘要

将钻井过程中的实时测井数据持续集成到具有相关地质不确定性的地下模型中,可实现战略性地质导向:对井位策略进行现场优化。过于简化的概念地质模型和不完善的模拟测量所产生的模型误差会导致不可靠的地下模型更新。当使用快速但不完善的模型(如深度神经网络(DNN))对合成测量进行近似时,模型误差尤为明显。#xD;我们提出了一种实用的数据同化工作流程,包括离线和在线两个阶段。离线阶段包括 DNN 训练和建立不确定的先验近井地质模型。在线阶段利用灵活迭代集合平滑器(FlexIES)对深层外电磁数据进行实时同化,同时考虑近似 DNN 模型中的模型误差。我们在 Goliat 油田(巴伦支海)的历史井记录数据上演示了所提出的工作流程。#xD;无论所选先验或近似 DNN 模型的层数如何,我们的概率估计中值与专有反演相当。通过估算模型误差,FlexIES 自动量化了各层边界和电阻率的不确定性,这在专利反演中并不常见。#xD;这一功能使我们能够更有效地捕捉不确定性,从而为未来的定量决策支持方法提供输入。我们通过直观估算考虑作业期间发生的储层退出的超前位风险,展示了定量决策支持的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strategic Geosteering Workflow with Uncertainty Quantification and Deep Learning: Initial Test on the Goliat Field Data
Continuous integration of real-time logging-while-drilling data into a subsurface model with relevant geological uncertainties enables strategic geosteering: a field-level optimization of the well-placement strategy. Model errors arising from oversimplified conceptual geological models and imperfect simulation of measurements result in unreliable subsurface-model updates. The model errors are particularly pronounced when synthetic measurements are approximated with a fast but imperfect model, such as a deep neural network (DNN).#xD;We present a practical data-assimilation workflow consisting of offline and online phases. The offline phase involves DNN training and building an uncertain prior near-well geo-model. The online phase utilizes the flexible iterative ensemble smoother (FlexIES) to perform real-time assimilation of extra-deep electromagnetic data while accounting for the model errors in the approximate DNN model. We demonstrate the proposed workflow on historic well-log data from the Goliat Field (Barents Sea). #xD;The median of our probabilistic estimation is on par with proprietary inversion, regardless of the number of layers in the chosen prior or the approximate DNN model. By estimating model errors, FlexIES automatically quantifies the uncertainty in the boundaries and resistivities of layers, which is not standard in proprietary inversion. #xD;This capability allows us to capture uncertainties more efficiently, thus providing input for future quantitative decision support methods. We demonstrate the potential of quantitive decision support by visually estimating the ahead-of-bit risk of reservoir exit that has occurred during the considered operation.
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