Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

Author:

Goliatt LeonardoORCID,Saporetti C.M.,Oliveira L.C.,Pereira E.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Universidade Federal de Juiz de Fora

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Elsevier BV

Subject

Geochemistry and Petrology,Geology,Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference95 articles.

1. A study of the soluble and insoluble organic matter from the livello bonarelli, a cretaceous black shale deposit in the central apennines, Italy;van Graas;Geochem. Cosmochim. Acta,1983

2. Total Organic Carbon Content Logging Prediction Based on Machine Learning: A Brief Review;Zhu,2022

3. A new method for estimating total organic carbon content from well logs;Zhao;AAPG (Am. Assoc. Pet. Geol.) Bull.,2016

4. A practical model for organic richness from porosity and resistivity logs;Passey;AAPG Bull.,1990

5. Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network;Mahmoud;Int. J. Coal Geol.,2017

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