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This is a Quantitative Study study.

September 24, 2025Open Access

Machine Learning-Based Soft Sensors for Reliable Bottom-Hole Pressure Estimation

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Error metrics for bottom-hole pressure estimates remained below 2% across varying conditions, confirming the method's high reliability.
Machine learning techniques, including gradient boosting and neural networks, were evaluated using a dataset from 60 wells in a Brazilian Pre-Salt oilfield.
The proposed framework enhances operational efficiency by offering a reliable and cost-effective alternative to permanent downhole gauges.
The methodology enables tailored supervised training through clustering techniques, leading to improved performance under diverse production scenarios.

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Implication

This study reveals effective machine learning techniques for bottom-hole pressure estimation, highlighting the role of soft sensors in reservoir management.

Cite This Study

Fernandes et al. (2025) studied this question.

synapsesocial.com/papers/68d6d8ba8b2b6861e4c3f22a https://doi.org/10.20944/preprints202509.1717.v1

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Implication

This study reveals effective machine learning techniques for bottom-hole pressure estimation, highlighting the role of soft sensors in reservoir management.

Machine Learning-Based Soft Sensors for Reliable Bottom-Hole Pressure Estimation

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Implication

Cite This Study

Machine Learning-Based Soft Sensors for Reliable Bottom-Hole Pressure Estimation

Key Points

Discussion

Implication

Cite This Study

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