水准点(测量)
储存效率
计算机科学
遗传算法
随机森林
数据建模
数据挖掘
计算机数据存储
油藏计算
联轴节(管道)
算法
机器学习
工程类
数据库
人工神经网络
机械工程
大地测量学
循环神经网络
地理
操作系统
作者
Hung Vo Thanh,Byung Jun Min
标识
DOI:10.3997/2214-4609.202310147
摘要
Summary Carbon capture and storage (CCS) is crucial to reaching net-zero emissions globally. However, uncertainty in geological CO2 storage ability forecasts is a key impediment to CCS. The majority of studies predict CO2 trapping using reservoir modeling. This method requires many computer resources to analyze a lot of subsurface data, which is costly for CO2 storage performance. This paper builds a robust machine-learning model to forecast CO2 trapping in saline aquifers with high precision to overcome a reservoir modeling restriction. This method uses a genetic algorithm (GA) and random forest concepts (RF). We acquired 1911 simulated data samples from the literature to ensure our technique was efficient and viable. These data samples were utilized to train and assess the intelligent models we provided (GA-RF). The results reveal that the proposed models’ CO2 trapping performance is outstanding and acceptable. The GA-RF outperforms machine learning (ML) approaches in statistical prediction performance for measuring CO2 trapping efficiency in reservoir saline aquifers. The ML model performed well in reservoir simulations using Sleipner benchmark datasets. Our model was able to match reservoir simulation results with GA-RF predictions. The suggested robust machine learning system can assess CO2 storage operations’ feasibility.
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