油气工业中的机器学习:聚类技术在油田先进过程控制中的新应用

Kalpesh M Patel, R. Patwardhan
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引用次数: 1

摘要

数据分析是一个新兴领域,涉及使用先进的统计和机器学习算法来发现不同类型数据中存在的信息和关系。本文描述的工作说明了机器学习技术在油田高级过程控制(APC)项目中的应用,该项目涉及在沙特阿美的一个大型陆上常规油田部署APC。APC的实施可以更好地控制和优化数百口油井的产量。APC在大型油田的推广涉及300多口油井的APC部署。使用传统的APC实施方法,部署将非常难以管理,并且将花费大约3年的时间,这是不现实的。使用创新的数据分析技术对于确保及时部署如此大规模的APC项目至关重要。机器学习算法用于聚类类似的井,大大减少了80%的工程工作量和操作人员开发每口井模型的工作量。这使得实施工作提前一年完成,从而比计划更早地实现了APC的效益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning in Oil & Gas Industry: A Novel Application of Clustering for Oilfield Advanced Process Control
Data Analytics is an emerging area that involves using advanced statistical and machine learning algorithms to discover information & relationsips present in different types of data. The work described in this paper illustrates the application of machine learning techniques to an Oilfield Advanced Process Control (APC) project involving deployment of APC at a large onshore conventional oilfield in Saudi Aramco. APC implementation enables better control and optimization of the production from hundreds of oilwells. APC rollout at the large oilfield involved APC deployment on 300+ oil wells. Using conventional APC implementation methodology, the rollout would be very difficult to manage and would have taken about 3 man years which was not practical. Use of innovative data analytics techniques was essential to ensuring the timely deployment of such a large scale APC project. A machine learning algorithm used to cluster similarly behaving wells, enabled significant (80%) reduction in the engineering effort and operator involvement in developing the models for each well. This allowed the implementation to be completed one year in advance thus realizing the APC benefits earlier than planned.
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