Research on oil and gas production prediction process based on machine learning

Author:

Liu Zhenzhi,Li Sanshan,Li Lu

Abstract

In recent years, the development trend of artificial intelligence is getting better and better. It has been widely used not only in the fields of big data analysis, automobile automatic driving, intelligent robot and face recognition, but also in various fields of oil and gas industry. Oil and gas production prediction is an important part of reservoir engineering, which is very important for the future production and development of strata, and can give developers some development suggestions. At present, the methods used in oil and gas production prediction are mainly traditional means such as numerical simulation and history matching. With the application of artificial intelligence in various fields of oil and gas industry, the use of machine learning models for oil and gas production prediction has become the direction of development and research. This paper summarizes the basic process and main technical means of applying machine learning model to predict oil and gas production by investigating the research of domestic and foreign scholars on artificial intelligence in oil and gas production prediction in recent years. It provides ideas and lays a foundation for future researchers to study this aspect, and also contributes to the development of smart oil fields in the future.

Publisher

Darcy & Roy Press Co. Ltd.

Reference28 articles.

1. MIN C, DAI B R, ZHANG X H, et al. A Review of the Application Progress of Machine Learning in Oil and Gas Industry[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 1- 15.

2. Kuang L C, Liu H, Ren Y L, et al. Application and development trend of artificial intelligence in petroleum exploration and development[J]. Petroleum Exploration and Development. 2021, 48(1): 1- 11.

3. Cheng Y F,Yang Y. Application of Artificial Intelligence in Oil Well Production Prediction[J]. Chemical Engineering Design Communications, 2021, 47(01): 125- 126.

4. Guo Y. Feature recognition from potential fields using neural networks[J]. SEG Technical Program Expanded Abstracts, 1949, 11(1): 1410.

5. Gupta K D, Vallega V, Maniar H, et al. A deep-learning approach for borehole image interpretation[C]//SPWLA 60th Annual Logging Symposium. OnePetro, 2019.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3