油气藏评价与开发 >
2021 , Vol. 11 >Issue 4: 613 - 620
DOI: https://doi.org/10.13809/j.cnki.cn32-1825/te.2021.04.018
机器学习预测油气产量现状
收稿日期: 2021-05-18
网络出版日期: 2021-08-19
基金资助
国家自然科学基金项目“页岩含气性关键参数测试及智能评价系统”(41927801)
Overview of oil and gas production forecasting by machine learning
Received date: 2021-05-18
Online published: 2021-08-19
机器学习是一种通用的数据驱动分析方法,也是一个重要的油气大数据分析利用手段。油气勘探开发作为具有悠久历史和庞大数据基础的重要领域,具有很大的数据挖掘潜力。利用油气田大数据分析技术可以帮助决策者进行投资分析、风险评估、生产优化,带来巨大的经济效益。机器学习方法早已被研究人员尝试应用于油气领域相关研究,随着机器学习算法的发展,许多应用场景被不断提出,但针对具体场景的通用方案仍在探索中。笔者从最基本原理着手介绍了机器学习的建模过程,梳理了用于油气田大数据分析的3类主要机器学习方法的发展历史,结合油气田大数据的特点,讨论了油气田大数据分析利用的核心内容、目标及优势,分析了机器学习在油气领域的主要应用场景,总结了目前典型油气产量预测中存在的问题及对策。
黄家宸 , 张金川 . 机器学习预测油气产量现状[J]. 油气藏评价与开发, 2021 , 11(4) : 613 -620 . DOI: 10.13809/j.cnki.cn32-1825/te.2021.04.018
The machine learning is not only an important tool for oil and gas big data analysis, but also a general data-driven analysis method. As an important field with a long history and a large data base, oil and gas exploration and development has a great potential for data mining. The use of big data analysis technology for oil and gas field can help decision makers to conduct investment analysis, risk assessment and production optimization, which brings significant economic benefits. The machine learning method has been tried by the researchers applying to the researches on oil and gas. Nowadays, many application scenarios have been proposed with the development of machine learning algorithms, but general solutions for specific scenario are still divided. So that, we introduces the procedure of a machine learning modeling upon the most basic principles, and summarizes the development history of the main three kinds of machine learning methods that can be applied to oil and gas big data analysis. And then based on the characteristics of oil and gas field big data, the core contents, goals and advantages of oil and gas field big data analysis and utilization are discussed, the main application scenarios of machine learning in oil and gas field are analyzed, and the existing problems and countermeasures in typical oil and gas production prediction are summarized.
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