使用基于注意力的残余神经网络对储水面进行分类
An Hai Nguyen1, Khang Nguyen2, Nga Mai3
1R&D Department, Petrovietnam Exploration Production Corporation, Hanoi, Vietnam.
PeerJ. Computer science
|September 24, 2025
概括
这项研究引入了一个先进的深度学习框架来对水库面部进行分类,提高了石油地质科学的准确性. 新方法提高了水库的特征,并减少了现场开发规划中的不确定性.
科学领域:
- 石油地质科学 石油地质科学
- 机器学习 机器学习
- 储水池的表征 储水池的表征
背景情况:
- 准确的水库面积分类对于有效的资源提取和水库表征至关重要.
- 传统的方法难以应对记录数据的复杂性和异质性.
- 现有的深度学习模型可能无法完全捕捉层次数据表示和上下文依赖.
研究的目的:
- 开发和验证一种新的深度学习框架,用于增强水库面层分类.
- 解决传统和现有的机器学习方法在处理异质井日志数据方面的局限性.
- 提高识别面界边界和复杂地质构造中的岩石学变异的准确性.
主要方法:
- 开发了一个深度学习框架,其架构方法与单流或非剩余设计不同.
- 通过使用来自八个不同的地质环境的井日志测量来训练和评估框架.
- 对传统机器学习和最先进的深度学习技术进行了比较实验.
主要成果:
- 拟议的方法实现了接收器运行特征曲线 (AUROC) 下的面积为0.883.
- 该方法获得了0.502的精度回调曲线下的面积 (AUPRC),优于现有技术.
- 展示了专注于关键地质特征和保存层次数据表示的卓越能力.
结论:
- 新的深度学习框架显著提高了水库面的分类准确性.
- 该方法有效地解决了数据异质性和井日志数据中的上下文依赖性.
- 强大且可重复的性能使其适用于现实世界水库表征和现场开发规划.
相关概念视频
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