English(EN) Soft Sensor for Bottom-Hole Pressure Estimation in Petroleum Wells Using Long Short-Term Memory and Transfer Learning

AI模型估算油井压力，性能超越物理传感器

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一种机器学习模型来估算油井井底压力，为物理传感器提供了一种经济高效的替代方案。该研究利用长短期记忆（LSTM）和迁移学习，在巴西盐下盆地真实海上数据集上实现了低于2%的平均绝对百分比误差。该方法旨在通过在各种储层和流动条件下提供准确的压力数据来改进生产优化和安全性。 AI

影响为关键工业监测提供了一种更准确、更经济高效的方法，有可能提高能源行业的效率和安全性。

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arXiv cs.LG TIER_1 English(EN) · M. A. Fernandes, E. Gildin, M. A. Sampaio · 2026-10-16 04:00

利用长短期记忆和迁移学习的石油井井底压力估算软传感器

arXiv:2602.03737v2 Announce Type: replace Abstract: Monitoring bottom-hole variables in petroleum wells is essential for production optimization, safety, and emissions reduction. Permanent Downhole Gauges (PDGs) provide real-time pressure data but face reliability and cost issues…