机器学习和物联网实现蒸汽驱优化以提高石油产量
Machine Learning and the Internet of Things Enable Steam Flood Optimization for Improved Oil Production
摘要：
最近开发的机器学习技术与物联网（IoT）相关联，允许实现增加来自重油井的石油产量的方法。蒸汽驱是一种广泛应用的提高原油采收率的技术，它利用热势和重力势来调动和稀释稠油，从而提高原油产量。与基于经典物理学原理的传统蒸汽驱模拟相比，我们在这里介绍一种使用尖端机器学习技术的方法，该方法有可能提供更好的方法来描述蒸汽驱的性能。我们提出了一个工作流程来解决一类时间序列数据，这些数据可以用监督机器学习算法和物联网进行分析。我们证明了该技术的有效性，用于预测蒸汽驱方案中的石油产量。此外，我们建立了一个优化系统，建议一个最佳的蒸汽分配计划，并表明它导致石油产量提高3%。我们在云平台上开发一个最小可行的产品，可以实现实时数据收集，传输和存储，以及基于云的机器学习模型的训练和实施。此工作流还为类似时间序列数据结构的其他问题提供了适用的解决方案，例如预测性维护。
英文摘要：
Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. Steam flood injection, a widely used enhanced oil recovery technique, uses thermal and gravitational potential to mobilize and dilute heavy oil in situ to increase oil production. In contrast to traditional steam flood simulations based on principles of classic physics, we introduce here an approach using cutting-edge machine learning techniques that have the potential to provide a better way to describe the performance of steam flood. We propose a workflow to address a category of time-series data that can be analyzed with supervised machine learning algorithms and IoT. We demonstrate the effectiveness of the technique for forecasting oil production in steam flood scenarios. Moreover, we build an optimization system that recommends an optimal steam allocation plan, and show that it leads to a 3% improvement in oil production. We develop a minimum viable product on a cloud platform that can implement real-time data collection, transfer, and storage, as well as the training and implementation of a cloud-based machine learning model. This workflow also offers an applicable solution to other problems with similar time-series data structures, like predictive maintenance.
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