College of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China; Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, Huludao 125105, China.
Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Viet Nam; Faculty of Mechanical-Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Viet Nam.
Sci Total Environ. 2023 Jun 15;877:162944. doi: 10.1016/j.scitotenv.2023.162944. Epub 2023 Mar 20.
The utilization of carbon capture utilization and storage (CCUS) in unconventional formations is a promising way for improving hydrocarbon production and combating climate change. Shale wettability plays a crucial factor for successful CCUS projects. In this study, multiple machine learning (ML) techniques, including multilayer perceptron (MLP) and radial basis function neural networks (RBFNN), were used to evaluate shale wettability based on five key features, including formation pressure, temperature, salinity, total organic carbon (TOC), and theta zero. The data were collected from 229 datasets of contact angle in three states of shale/oil/brine, shale/CO/brine, and shale/CH/brine systems. Five algorithms were used to tune MLP, while three optimization algorithms were used to optimize the RBFNN computing framework. The results indicate that the RBFNN-MVO model achieved the best predictive accuracy, with a root mean square error (RMSE) value of 0.113 and an R of 0.999993. The sensitivity analysis showed that theta zero, TOC, pressure, temperature, and salinity were the most sensitive features. This research demonstrates the effectiveness of RBFNN-MVO model in evaluating shale wettability for CCUS initiatives and cleaner production.
在非常规地层中利用碳捕集利用与封存（CCUS）是提高烃类产量和应对气候变化的一种有前途的方法。页岩润湿性是 CCUS 项目成功的关键因素之一。在这项研究中，使用了多种机器学习（ML）技术，包括多层感知器（MLP）和径向基函数神经网络（RBFNN），基于五个关键特征，包括地层压力、温度、盐度、总有机碳（TOC）和零接触角，来评估页岩润湿性。数据来自页岩/油/盐水、页岩/CO/盐水和页岩/CH/盐水系统中三种状态的接触角的 229 个数据集。使用了五种算法来调整 MLP，使用了三种优化算法来优化 RBFNN 计算框架。结果表明，RBFNN-MVO 模型具有最佳的预测精度，均方根误差（RMSE）值为 0.113，R 值为 0.999993。敏感性分析表明，零接触角、TOC、压力、温度和盐度是最敏感的特征。这项研究证明了 RBFNN-MVO 模型在评估 CCUS 计划和清洁生产中页岩润湿性方面的有效性。
