使用声辐射技术 (AET) 预测钻探透率的智能方法
Mehrbod Khoshouei1, Raheb Bagherpour2, Mohammad Hossein Jalalian1
1Department of Mining Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran.
Scientific reports
|October 28, 2025
概括
地质技术钻井性能可以使用振动声学信号和人工智能 (AI) 来预测. 机器学习模型准确预测透率,提高挖掘项目的效率和可持续性.
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
- 信号处理 信号处理
背景情况:
- 在地质工程中,由于能源限制和成本需求,优化至关重要.
- 钻井效率对于采矿和道开采至关重要,需要智能性能策略.
- 钻井时监控 (MWD) 和声波发射技术 (AET) 提供实时数据采集和分析.
研究的目的:
- 通过分析振动声信号和钻探参数来预测钻探透率 (PR).
- 评估人工神经网络 (ANN),随机森林 (RF) 和支持矢量回归 (SVR) 模型在PR预测方面的有效性.
- 展示振动声学监测与人工智能的集成,以提高钻探性能.
主要方法:
- 收集和分析振动声学信号和钻井参数.
- 开发并比较了三种机器学习模型:ANN,RF和SVR.
- 使用R平方,MAPE和RMSE等指标评估模型性能.
主要成果:
- 所有模型都显示了对透率的可靠预测准确度.
- 随机森林 (RF) 实现了最高的R平方值 (0.816) 和最低的MAPE (31.54%).
- 支持向量回归 (SVR) 显示了与0.808的R平方和29.52%的MAPE相似的性能.
结论:
- 将振动声学监测与人工智能驱动的模型集成为准确的PR预测是可行的.
- 这种方法支持实时决策,提高钻井效率.
- 这些发现促进了地下和地表挖掘项目的可持续实践.
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