基于机器学习的断裂导电性预测
Xiaopeng Wang1,2, Binqi Zhang1,2, Jianbo Du1,2
1State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China.
ACS omega
|March 25, 2024
概括
本研究引入了一种机器学习模型来预测断裂导电性,这是低透性水库液压压裂的关键因素. 该模型准确估计电导率,减少与传统方法相关的时间和劳动力成本.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能在水库开发中的作用
背景情况:
- 水力压裂对于利用低透性的水库至关重要.
- 断裂导电性是评估液压压裂效率和优化设计的关键参数.
- 计算断裂导电性的现有方法是劳动密集型和耗时的.
研究的目的:
- 开发一种基于机器学习的模型,用于预测断裂导电性.
- 确定影响断裂导电性的主要因素.
- 为传统的断裂导电性计算方法提供一个更有效的替代方案.
主要方法:
- 使用皮尔森系数和灰色相关性分析确定断裂导电性的关键控制因素.
- 开发使用反向传播 (BP) 神经网络的断裂导电性预测模型.
- 使用遗传算法优化BP神经网络.
主要成果:
- 机器学习模型准确地预测了断裂导电性.
- 开发的BP神经网络模型实现了高精度,R2值为A块0.981,B块0.975.
- 皮尔森系数和灰色相关性分析成功地确定了断裂导电性的主要控制因素.
结论:
- 机器学习为预测断裂导电性提供了准确有效的方法.
- 拟议的模型大大减少了断裂导电性评估所需的时间和劳动力.
- 这种方法为优化低透性水库中的液压压裂设计提供了有价值的工具.
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