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Updated: Jun 8, 2026

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人工智能和数值模拟在钻井流体凝性能研究和配方设计中的应用潜力

Keming Sheng¹, Yinbo He², Mingliang Du²

¹College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249, China.

Gels (Basel, Switzerland)

|June 26, 2024

概括

人工智能和数值模拟提高了钻井流体的设计. 研究强调了数据采集和模型假设的挑战,提出了改善钻井液凝性能的未来方向.

科学领域:

石油工程是石油工程中的一个.
材料科学材料科学材料科学
计算科学计算科学

背景情况:

钻井液凝对于高效的钻井至关重要,但使用传统方法来优化是复杂的.
人工智能 (AI) 和数值模拟为钻井流体设计和性能提供了先进的解决方案.
现有研究面临的挑战是现场数据采集和理想化的模型假设.

研究的目的:

审查AI和数值模拟技术在钻井流体设计和性能优化中的应用.
确定当前的挑战,并提出钻井液凝性能未来的研究方向.
分析不同人工智能技术的普及程度以及该领域的关键研究问题.

主要方法:

人工智能技术的文献综述:专家系统,人工神经网络 (ANN),支持矢量机 (SVM) 和遗传算法.
数字模拟技术的文献综述:计算流体动力学 (CFD),分子动力学 (MD) 和蒙特卡洛模拟.
分析研究趋势,重点关注与ANN相关的论文的百分比和从业人员的主要关注点.

主要成果:

人工神经网络 (ANN) 是最普遍的AI技术,在52.0%的审查论文中出现.
泄漏问题是研究人员的主要关注点,占钻井液凝性能研究的17%以上.
获得现场数据的困难和过度理想化的模型假设是重大挑战.

结论:

未来的研究应该强调功能工程和可解释的人工智能,以更好地利用数据.
建立开放式数据通道或大规模数据库对于推动研究至关重要.
专注于微观结构的深度数值模拟可以减少数据依赖性并提高准确性.

关键词:

深度学习是一种深度学习.流入流体的流体流入.分子仿真是分子仿真.神经网络的神经网络的神经网络性能优化性能优化性能优化软凝软凝软凝

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