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.

翻译：石油井底变量监测对于生产优化、安全运行及减排至关重要。永久性井下压力计（PDGs）虽能提供实时压力数据，但存在可靠性及成本问题。本文提出一种基于机器学习的软传感器，利用井口与地面测量数据估计流动井底压力（BHP）。研究引入长短期记忆（LSTM）模型，并与多层感知器（MLP）及岭回归方法进行对比。同时，本研究开创性地采用迁移学习技术实现模型在不同作业环境间的自适应。通过在巴西盐下盆地真实海上数据集的测试，该方法获得的平均绝对百分比误差（MAPE）持续低于2%，性能优于基准模型。本工作为物理传感器提供了一种经济高效且精确的替代方案，在多种储层与流动条件下具有广泛适用性。