History matching is the process whereby the values of uncertain attributes of a reservoir model are changed with the purpose of finding models that match existing reservoir production data. As an inverse and ill-posed problem in engineering, it admits multiple solutions and plays a key role in reservoir management tasks: reservoir models support important and strategic field development decisions and, the more calibrated the models, the higher the confidence on their forecast for the actual reservoir’s performance. In this work, we introduce a stochastic learning-from-data approach to the history-matching problem. With a data-driven nature, the proposed algorithm has dedicated components to handle petrophysical and global uncertain attributes, and generates new solutions using the patterns of attributes present in solutions that are judiciously selected among a set of solutions for each well and variable involved in the history-matching process. We apply our approach to the UNISIM-I-H benchmark, a challenging synthetic case based on the Namorado Field, Campos Basin, Brazil. The results indicate the potential of our learning proposal towards generating multiple solutions that not only match the history data but, most importantly, offer acceptable performance while forecasting field production. Compared with history-matching methodologies previously applied to the same benchmark, our approach produces competitive results in terms of matching quality and forecast capacity, using substantially fewer simulations.

历史匹配是改变油藏模型的不确定属性值以发现与现有油藏生产数据匹配的模型的过程。作为工程中的逆向问题，它接受多种解决方案并在油藏管理任务中发挥关键作用：油藏模型支持重要的战略性油田开发决策，并且模型越标定，对其预测的信心就越高。实际储层的性能。在这项工作中，我们为历史匹配问题引入了一种从数据中随机学习的方法。由于具有数据驱动特性，因此该算法具有专用组件来处理岩石物理属性和全局不确定属性，并使用存在于解决方案中的属性模式生成新的解决方案，这些解决方案是为历史匹配过程中涉及的每个孔和变量从一组解决方案中明智地选择的。我们将我们的方法应用于UNISIM-IH基准，这是一个具有挑战性的综合案例，基于巴西坎波斯盆地Namorado油田。结果表明，我们的学习建议具有产生多种解决方案的潜力，这些解决方案不仅与历史数据匹配，而且最重要的是，在预测油田产量时可以提供可接受的性能。与以前应用于同一基准的历史记录匹配方法相比，我们的方法使用更少的模拟就匹配质量和预测能力而言产生了可观的结果。