Waterflood has been widely applied across the world as the most important secondary recovery process to improve reservoir performance. The schemes applied to waterflood projects involve key decision-making which aims to maximize the net present value (NPV) for a given period, improve recovery and sweep efficiency, maintain reservoir pressure, reduce water production, and avoid water recycling. Traditionally, the preferred approach to estimate the future performance of any given waterflood project is through the numerical reservoir simulation. However, despite the great power and intuition associated with the simulation models, sometimes this potential is unachievable due to massive computational requirements. Also, in some other cases, there may exist no simulation model to begin with. To tackle these problems, data-driven proxy models are proposed. In this research, we propose a novel machine-learning-based proxy model for waterflood performance prediction and further apply it for production optimization purpose to obtain the optimal future well control. The data-driven model is realized using the Echo State Network (ESN) under the paradigm of “Reservoir Computing” (RC). Compared with traditional ESNs, this specific methodology includes a feedback loop from the output into the high dimensional “reservoir” to achieve improved prediction results for separated phase production rates. Teacher Forcing (TF) technique is used for easier incorporation of the feedback information without introducing additional recurrent loops during the training process. Furthermore, training ESN proxy models utilizes Ridge Regression, thus all calculations within have analytical solutions that guarantee much improve speed when evaluating forward model-runs, which further lowers the computational demand during the optimization process. The proposed workflow is typically more suitable for mature fields since reliable production data after breakthrough from each producer could greatly improve the training process, and it can be used under the circumstance where no reservoir model has been established. In this research, we present two test examples where we apply ESN to learn the reservoir simulation results of waterflood and further perform open-loop optimization on each of them.

注水作为提高储层性能的最重要的二次采油工艺已在世界范围内得到广泛应用。适用于注水项目的计划涉及关键决策，旨在使给定时期内的净现值（NPV）最大化，提高采收率和吹扫效率，维持储层压力，减少水的产生，并避免水的循环利用。传统上，估算任何给定注水项目未来性能的首选方法是通过数值油藏模拟。但是，尽管仿真模型具有强大的功能和直觉，但由于庞大的计算需求，有时仍无法实现这一潜力。同样，在某些其他情况下，可能不存在任何模拟模型。为了解决这些问题，提出了数据驱动的代理模型。在这项研究中，我们提出了一种基于机器学习的代理模型来预测注水性能，并将其进一步用于生产优化目的，以获得最佳的未来井控。数据驱动模型是在“储层计算”（RC）范式下使用回声状态网络（ESN）实现的。与传统的ESN相比，此特定方法包括从输出到高维“储层”的反馈回路，以提高分离相生产率的预测结果。教师强迫（TF）技术用于更轻松地合并反馈信息，而无需在训练过程中引入其他重复循环。此外，训练ESN代理模型会利用Ridge回归，因此，其中的所有计算都具有解析解决方案，可以确保在评估正向模型运行时大大提高速度，从而进一步降低了优化过程中的计算需求。所提出的工作流程通常更适合成熟领域，因为从每个生产商处获得突破后的可靠生产数据可以大大改善培训过程，并且可以在尚未建立储层模型的情况下使用。在本研究中，我们提供了两个测试示例，在这些示例中，我们使用ESN来学习注水的油藏模拟结果，并进一步对每个油藏进行开环优化。所提出的工作流程通常更适合成熟领域，因为从每个生产商处获得突破后的可靠生产数据可以大大改善培训过程，并且可以在尚未建立储层模型的情况下使用。在本研究中，我们提供了两个测试示例，在这些示例中，我们使用ESN来学习注水的油藏模拟结果，并进一步对每个油藏进行开环优化。所提出的工作流程通常更适合成熟领域，因为从每个生产商处获得突破后的可靠生产数据可以大大改善培训过程，并且可以在尚未建立储层模型的情况下使用。在本研究中，我们提供了两个测试示例，在这些示例中，我们使用ESN来学习注水的油藏模拟结果，并进一步对每个油藏进行开环优化。