Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage-Reference-Cited by-同舟云学术

Improving predictions of shale wettability using advanced machine learning techniques and nature-inspired methods: Implications for carbon capture utilization and storage

Published:2023-06 Issue: Volume:877 Page:162944
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Zhang Hemeng,Thanh Hung Vo,Rahimi Mohammad,Al-Mudhafar Watheq J.,Tangparitkul Suparit,Zhang Tao,Dai Zhenxue,Ashraf Umar

Funder

Department of Education of Liaoning Province

Chiang Mai University

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference85 articles.

1. Current understanding of shale wettability: a review on contact angle measurements;Abdul;Earth-Sci.Rev.,2018

2. Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs;Al-Mudhafar;J. Pet. Sci. Eng.,2020

3. Evaluating the applications of dendritic neuron model with metaheuristic optimization algorithms for crude-oil-production forecasting;Al-qaness;Entropy,2022

4. 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

5. Predicting solubility of CO2 in brine by advanced machine learning systems: application to carbon capture and sequestration;Amar;J. CO2 Util.,2019

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