使用深度学习的泥损失数据估计形成透率
Yaser Abdollahfard1, Seyed Morteza Mirabbasi1, Mohammad Ahmadi2
1Petroleum Engineering Department, Amirkabir University of Technology, Tehran, Iran.
Scientific reports
|April 30, 2025
概括
这项研究引入了一种使用泥损失数据和深度学习来估计水库透性的新方法. 像1D-CNN和DJINN这样的机器学习模型可以准确地从钻探数据中预测形成的透性.
科学领域:
- 石油工程是石油工程中的一个.
- 机器学习 机器学习
- 地质科学是地球科学.
背景情况:
- 透率估计对于储库评估和碳化合物开采至关重要.
- 现有的透性评估方法可能不准确或无法使用.
- 泥损失数据经常被忽视,是估计透性的潜在来源.
研究的目的:
- 开发和验证一种使用泥损失数据估计形成透性的新方法.
- 应用深度学习技术来准确预测透性.
- 探索实时钻探数据对水库特征的实用性.
主要方法:
- 使用水库模拟器生成的泥损失率数据,具有不同的水库和钻井参数.
- 采用一维卷积神经网络 (1D-CNN) 进行透度估计.
- 使用了一种新的深度联合信息神经网络 (DJINN) 模型,集成神经网络和决策树.
主要成果:
- 1D-CNN在透率估计中获得了高准确度 (R2=0.970训练,R2=0.964测试).
- DJINN模型的表现优于1D-CNN,显示出卓越的准确性 (R2=0.978训练,R2=0.972测试).
- 验证了生成数据的相关系数,以确保在真实环境下可靠性.
结论:
- 泥损失数据可以通过深度学习有效地利用,以准确估计形成的透性.
- 与1D-CNN相比,DJINN模型为此应用提供了更准确的方法.
- 这种方法为钻探数据提供了新的应用,使石油工程师能够改进水库设计和表征.
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