一种深度学习的识别方法,紧密的砂岩 lithofacies 集成多层感知和多变量时间序列
Zihao Mu1, Chunsheng Li1, Zongbao Liu2
1School of Computer & Information Technology, Northeast Petroleum University, Daqing, 163318, China.
Scientific reports
|December 29, 2024
概括
这项研究介绍了一种使用混合多层感知子 (MLP) 和多变量时间序列 (MTS-Mixers) 模型的智能方法,用于在紧密的砂岩水库中准确识别石灰坑. 开发的深度学习模型实现了超过90%的识别效率,克服了手动分类的局限性.
科学领域:
- 石油地质学 石油地质学
- 人工智能在地球科学中的应用
- 储水池的表征 储水池的表征
背景情况:
- 石灰岩物种的分类对于探索紧密的砂岩水库至关重要.
- 目前的手工方法耗时,主观,数据有限.
研究的目的:
- 开发一种智能方法,用于在紧密的砂岩水库中识别石灰岩.
- 解决手动分类的挑战,提高勘探效率.
主要方法:
- 根据智能歧视贡献率选择的利用日志记录曲线参数.
- 应用数据预处理以确保实验数据的质量.
- 构建了一个混合多层感知器 (MLP) 和多变量时间序列 (MTS-Mixers) 模型.
主要成果:
- 混合型MLP-MTS模型显示了所有灯火状态阶段的高识别效率.
- 在光电位置识别方面实现了超过90%的准确性.
- 验证了深度学习方法对水库特征的有效性.
结论:
- 拟议的MLP-MTS模型提供了一种高效准确的解决方案,用于在紧密的砂岩水库中识别石灰岩.
- 深度学习模型显示,在水库灯光位置的相位识别中具有显著的应用性.
- 这种方法增强了对石油和天然气勘探的石灰岩特性分析.
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