论文标题
使用机器学习框架更改井位置的误差校正模型的非侵入性参数模型订单降低
Non-Intrusive Parametric Model Order Reduction With Error Correction Modeling for Changing Well Locations Using a Machine Learning Framework
论文作者
论文摘要
本文的目的是开发用于改变油田中井位置的问题的全球非侵入参数模型订购方法（PMOR）方法，最终可以用于井位优化以获得大量的计算节省。在这项工作中，我们提出了一个基于正交分解（POD）的PMOR策略，该策略对模拟器源代码毫不侵入，因此将其适用性扩展到任何商业模拟器。所提出的技术的非毒性源于制定与POD一起使用的基于机器学习的新型机器学习（ML）框架。 ML模型的特征旨在使它们考虑到状态解决方案的时间演变，从而避免使用模拟器访问解决方案的时间依赖性。我们通过引入基于几何的特征和流程诊断灵感的基于物理学的特征来代表良好的位置变化作为参数。稍后制定了基于简化模型解决方案的误差校正模型，以纠正井网的状态解决方案中的差异。据观察，全球PMOR可以预测井块处的压力和饱和溶液的总体趋势，但是观察到一些偏见导致了利益量的预测（QOI）的差异。因此，将基于物理的简化模型解决方案视为特征的误差校正模型被证明可大大减少QOI中的误差。该工作流程应用于异质通道的储层，该储层显示出良好的解决方案精度，并且在考虑的不同情况下观察到了50x-100x的加速度。该方法的制定使所有仿真时间步骤都是独立的，因此可以非常有效地利用并行资源，并避免稳定性问题，这些问题可能是由于时间段上的错误积累而导致的。
The objective of this paper is to develop a global non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field, that can eventually be used for well placement optimization to gain significant computational savings. In this work, we propose a proper orthogonal decomposition (POD) based PMOR strategy that is non-intrusive to the simulator source code and hence extends its applicability to any commercial simulator. The non-intrusiveness of the proposed technique stems from formulating a novel Machine Learning (ML) based framework used with POD. The features of ML model are designed such that they take into consideration the temporal evolution of the state solutions and thereby avoiding simulator access for time dependency of the solutions. We represent well location changes as a parameter by introducing geometry-based features and flow diagnostics inspired physics-based features. An error correction model based on reduced model solutions is formulated later to correct for discrepancies in the state solutions at well gridblocks. It was observed that the global PMOR could predict the overall trend in pressure and saturation solutions at the well blocks but some bias was observed that resulted in discrepancies in prediction of quantities of interest (QoI). Thus, the error correction model that considers the physics based reduced model solutions as features, proved to reduce the error in QoI significantly. This workflow is applied to a heterogeneous channelized reservoir that showed good solution accuracies and speed-ups of 50x-100x were observed for different cases considered. The method is formulated such that all the simulation time steps are independent and hence can make use of parallel resources very efficiently and also avoid stability issues that can result from error accumulation over timesteps.
