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

TL;DR

This paper presents a machine learning soft sensor using LSTM and transfer learning to accurately estimate bottom-hole pressure in petroleum wells, reducing reliance on costly physical sensors.

Contribution

It introduces a novel transfer learning approach for adapting pressure estimation models across different operational environments.

Findings

Achieved MAPE below 2% on real offshore datasets

Outperformed traditional models like MLP and Ridge Regression

Demonstrated broad applicability across reservoir conditions

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. 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.