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在碳捕获和储存系统中使用自回归深度神经网络预测地力学承载能力

Sulieman Ibraheem Shelash Al-Hawary¹, Eyhab Ali², Suhair Mohammad Husein Kamona³

¹Department of Business Administration, Business School, Al al-Bayt University, P.O.Box 130040, Mafraq, 25113, Jordan.

|November 30, 2023

概括

自行回归深度神经网络 (ARDNN) 准确预测碳捕获和储存 (CCS) 系统的地力学承载能力. 这种深度学习方法提高了地下二氧化碳储存的安全性和可靠性.

科学领域:

地质技术工程地质技术工程
人工智能的人工智能
环境科学环境科学

背景情况:

碳捕获和储存 (CCS) 对于减轻温室气体排放至关重要.
地力学稳定性,特别是承载能力,是CCS操作中的一个关键挑战.
需要准确的预测模型来确保CCS的长期安全.

研究的目的:

研究使用自回归深度神经网络 (ARDNN) 来预测CCS系统中的地力学承载能力.
为了评估ARDNN的性能,使用剪切波速度作为深层岩石构成的索引.
展示深度学习在提高CCS地力学稳定性评估方面的潜力.

主要方法:

使用了23000个数据点的数据集来训练和测试ARDNN模型.
采用剪波速度作为评估承载能力的代理.
利用深度学习技术进行自动特征提取和可适应的预测.

主要成果:

在ARDNN模型中,测试数据的R平方值为0.9906.
记录了0.0438的平均平方误差,这表明预测准确度很高.
该模型在预测地力学承载能力方面表现出色.

结论:

ARDNN是预测CCS系统中地力学承载能力的高效工具.
该研究强调了使用先进的人工智能来确保CCS运营安全的实际影响.
准确的地力学稳定性预测对于减轻碳捕获和储存中的风险至关重要.

关键词:

自行回归的深度神经网络碳捕获和储存 (CCS) 是一种地质力学地质力学机器学习机器学习石化物理学石化物理学剪切波的速度是剪切波的速度.

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