Machine learning driven high resolution sequence stratigraphy

WO2025097017A1PCT designated stage expired Publication Date: 2025-05-08SCHLUMBERGER TECH CORP +3
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Patent Information

Application Number
PCT/US2024/054210
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2023-11-01
Filing Date
2024-11-01
Publication Date
2025-05-08

AI Technical Summary

Technical Problem

Existing methods for high-resolution sequence stratigraphy in subsurface structures face challenges due to limited data availability and human errors, leading to inefficient and inaccurate stratigraphic data collection and processing.

Method used

The use of a machine learning-driven approach to generate stratigraphic marker data and geological facies data by configuring a machine learning model with training data from sensors and synthetic data, isolating cyclic stratigraphic features, and determining inflection points to create a trained stratigraphic model for energy development operations.

Benefits of technology

This approach enables accurate and efficient determination of stratigraphic marker data and geological facies data, improving the precision of geological models and facilitating more effective energy development operations.

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Abstract

Disclosed are methods, systems, and computer programs that determine stratigraphic marker data for energy development operations at a resource site. According to one embodiment, the methods include generating a stratigraphic model for the resource site. The stratigraphic model, for example, comprises a machine learning model having one or more parameters. The methods also include configuring the stratigraphic model using training data following which the trained stratigraphic model is used to generate, based on data captured at the resource site, stratigraphic marker data or geological facies data for the resource site. The stratigraphic marker data or geological facies data comprises geological sequence boundary data indicating uniform or non-uniform sediment deposition information or geological layering information associated with the resource site.
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Citation Information

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Cited By

  • Intelligent interpretation method and device for sequence strata and storage medium

    CN122330975A