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AI model estimates petroleum well pressure, outperforming physical sensors

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine learning model to estimate bottom-hole pressure in petroleum wells, offering a cost-effective alternative to physical sensors. The study, which utilizes Long Short-Term Memory (LSTM) and transfer learning, achieved a Mean Absolute Percentage Error below 2% on real offshore datasets from Brazil's Pre-salt basin. This approach aims to improve production optimization and safety by providing accurate pressure data across various reservoir and flow conditions. AI

IMPACT Offers a more accurate and cost-effective method for critical industrial monitoring, potentially improving efficiency and safety in the energy sector.

RANK_REASON Academic paper detailing a new machine learning methodology and its application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model estimates petroleum well pressure, outperforming physical sensors

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Academic paper detailing a new machine learning methodology and its application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · M. A. Fernandes, E. Gildin, M. A. Sampaio ·

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

    arXiv:2602.03737v2 Announce Type: replace 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…

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