# Soft Sensor for Bottom-Hole Pressure Estimation in Petroleum Wells Using Long Short-Term Memory and Transfer Learning

arXiv: https://arxiv.org/abs/2602.03737 · HTML: https://mlsanity.com/paper/2602.03737/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 40 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 1 of 1)

- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)

## Abstract (arXiv)

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.