Methods and Systems for Continuous Bottomhole Pressure Estimation

The hybrid BHP modeling method addresses the limitations of existing methods by integrating physics-based preprocessing and machine learning to estimate BHP accurately and efficiently, enhancing prediction accuracy and applicability across diverse well conditions.

US20250250888A1Pending Publication Date: 2025-08-07XECTA INTELLIGENT PROD SERVICES
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Patent Information

Application Number
US18/754345
Authority / Receiving Office
US · United States
Patent Type
Applications(United States)
Current Assignee / Owner
Priority Date
2024-02-07
Filing Date
2024-06-26
Publication Date
2025-08-07

AI Technical Summary

Technical Problem

Existing methods for estimating bottomhole pressure (BHP) in subterranean petroleum reservoirs are costly and lack generalizability, as they rely on empirical or mechanistic models that are not applicable to various flow conditions and require manual tuning, making them unsuitable for continuous measurement across large well counts.

Method used

A hybrid BHP modeling method combining physics-based preprocessing and regularization with machine learning models to estimate BHP from routine production data, using a two-step approach to determine the best physics correlation and residual correction.

Benefits of technology

The hybrid model provides accurate and scalable BHP estimation, improving prediction accuracy and generalizability across different wellbore configurations and operational conditions, reducing the need for continuous downhole sensors.

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Abstract

A method of modeling borehole pressure (BHP) for a wellbore, comprising: receiving field data for the wellbore; training a machine learning (ML) classification model to determine a best physics correlation for BHP in the wellbore using a plurality of physics-based models; determining, using the ML classification model, the best physics correlation based on the field data for the wellbore; determining, using the best physics correlation, a BHP estimate based on the field data for the wellbore; training an ML regression model to determine a residual correction using the BHP estimate and the field data for the wellbore; and determining, using the ML regression model, a final BHP using the BHP estimate and the residual correction for the wellbore.
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Citation Information

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