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. We propose a machine learning-based soft sensor to estimate flowing Bottom-Hole Pressure (BHP) using wellhead and topside measurements. A Long Short-Term Memory (LSTM) model is introduced and compared with Multi-Layer Perceptron (MLP) and Ridge Regression. We also pioneer Transfer Learning for adapting models across operational environments. Tested on real offshore datasets from Brazil's Pre-salt basin, the methodology achieved Mean Absolute Percentage Error (MAPE) consistently below 2\%, outperforming benchmarks. This work offers a cost-effective, accurate alternative to physical sensors, with broad applicability across diverse reservoir and flow conditions.
基于长短期记忆与迁移学习的石油井底压力估计软测量方法 / Soft Sensor for Bottom-Hole Pressure Estimation in Petroleum Wells Using Long Short-Term Memory and Transfer Learning
这项研究提出了一种利用井口和地面测量数据、结合长短期记忆网络与迁移学习技术的软测量方法，能够以低于2%的平均绝对百分比误差准确估算石油井的流动井底压力，为替代昂贵且易损的物理传感器提供了一种低成本、高精度的解决方案。
源自 arXiv: 2602.03737
