Application of Artificial Neural Networks for Identification of Lithofacies by Processing of Core Drilling Data

Author:

Yang Mingsheng12,Hu Yuanbiao12ORCID,Liu Baolin12,Wang Lu12ORCID,Zhou Zheng12,Jia Mingrang12

Affiliation:

1. School of Engineering and Technology, China University of Geosciences, Beijing 100083, China

2. Key Laboratory of Deep Geodrilling Technology, Ministry of Natural Resources, Beijing 100083, China

Abstract

Identifying lithofacies types from core drilling data presents significant challenges, especially given the limited number of physical drilling characteristics available for analysis. Traditional machine learning methods often face issues with poor training and testing due to these limitations. Addressing this, we propose a new method for processing core drilling data to improve the accuracy of deep artificial neural networks (DANNs) in lithofacies recognition. Our approach transforms torque, weight on bit (WOB), and rotational speed data into three square matrices, creating a novel three-channel lithofacies image. This method allows for the application of DANNs by converting the complex lithofacies recognition task into a more standard image recognition problem. The developed method dramatically increases the input vector dimensions, enhancing the richness of the data input. The validation of results revealed that the DANN model trained for merely 3000 iterations successfully predicted lithofacies types of all eight testing samples in a mere 2.85 ms, showcasing superior accuracy. The innovative drilling data processing method proposed in this study enables DANNs to identify lithofacies with increased speed and accuracy. This offers a new direction for other DANNs utilizing drilling data.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference28 articles.

1. The mechanics of diamond core drilling of rocks;Huang;Int. J. Rock Mech. Min. Sci.,1997

2. Tewari, S., and Dwivedi, U. (2018, January 12–15). A novel automatic detection and diagnosis module for quantitative lithofacies modeling. Proceedings of the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, United Arab Emirates.

3. A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies;Tewari;J. Pet. Explor. Prod. Technol.,2020

4. Recognition of interface and category of roadway roof strata based on drilling parameters;Liu;J. Pet. Sci. Eng.,2021

5. Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines;Anifowose;Appl. Soft Comput.,2015

Cited by 2 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