Harnessing Machine Learning in the Subsurface to Promote Operational Efficiency

Chris Hanton

doi:10.3997/1365-2397.fb2023014

ISSN: 0263-5046
E-ISSN: 1365-2397

Harnessing Machine Learning in the Subsurface to Promote Operational Efficiency
By Chris Hanton¹
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Affiliations: ¹ Ikon Science
Source: First Break, Volume 41, Issue 2, Feb 2023, p. 69 - 71
DOI: https://doi.org/10.3997/1365-2397.fb2023014
- Published online: 01 Feb 2023

Abstract

Machine learning, despite having more than 50 years of history in subsurface disciplines, has largely remained a niche workflow, frequently performed in isolation with lack of repeatability. While advances in computing and programming language have opened up access to machine learning as a tool, we have yet to see the same growth in operational efficiency experienced by other segments and verticals within the energy industry. Application of ML toward data conditioning and workflow set-up could save geoscientists hundreds of hours each year, allowing for faster delivery of results and improving standardisation and consistency across departments.

EAGE Publications BV

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References

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Harnessing Machine Learning in the Subsurface to Promote Operational Efficiency

First Break 41, 69 (2023); https://doi.org/10.3997/1365-2397.fb2023014

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Harnessing Machine Learning in the Subsurface to Promote Operational Efficiency

Abstract

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