Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations-Reference-Cited by-同舟云学术

Combined machine-learning and optimization models for predicting carbon dioxide trapping indexes in deep geological formations

Published:2023-08 Issue: Volume:143 Page:110408
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Davoodi Shadfar^ORCID,Vo Thanh Hung^ORCID,Wood David A.^ORCID,Mehrad Mohammad^ORCID,Rukavishnikov Valeriy S.^ORCID

Funder

Tomsk Polytechnic University

Publisher

Elsevier BV

Subject

Software

Reference87 articles.

1. Three years to safeguard our climate;Figueres;Nature,2017

2. Role of carbon capture and utilization (CCU) for decarbonization of industrial sector: A case study of Japan;Kawai;Appl. Energy,2022

3. Economic analysis of CCUS: accelerated development for CO2 EOR and storage in residual oil zones under the context of 45Q tax credit;Ren;Appl. Energy,2022

4. A review of physical modelling and numerical simulation of long-term geological storage of CO2;Jiang;Appl. Energy,2011

5. An optimized neuro-fuzzy system using advance nature-inspired Aquila and salp swarm algorithms for smart predictive residual and solubility carbon trapping efficiency in underground storage formations;Al-qaness;J. Energy Storage,2022

Cited by 19 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

1. A novel data-driven model for real-time prediction of static Young's modulus applying mud-logging data;Earth Science Informatics;2024-09-11

2. A Machine Learning Based Framework for Brine-Gas Interfacial Tension Prediction: Implications for H2, CH4 and CO2 Geo-Storage;Day 2 Wed, May 08, 2024;2024-05-07

3. Reservoir temperature prediction based on characterization of water chemistry data—case study of western Anatolia, Turkey;Scientific Reports;2024-05-06

4. Forecasting solar power generation using evolutionary mating algorithm-deep neural networks;Energy and AI;2024-05

5. Development of new materials for electrothermal metals using data driven and machine learning;PLOS ONE;2024-04-26