Physics versus machine learning models

Q: What does this research mean for the field?

Machine learning models can complement traditional physics-based approaches in shale exploration and drilling prediction. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Neil McNaughton

doi:10.69894/20210108

February 28, 2021Oil IT Journal.

Physics versus machine learning models

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Key Points

The aim is to evaluate the effectiveness of machine learning models compared to traditional physics-based methods in energy exploration.
Presentation by Hess on data-driven models in shale exploration.
Discussion by Corva on ROP (rate of penetration) drilling prediction methods.
Insights from Schlumberger on integrating both ML and physics in models.
Xecta's stance on avoiding ML for small data scenarios.
Highlighting the varied applications of ML and physics in predictive modeling.
Identifying scenarios where ML may enhance traditional physics-based approaches.
Emphasizing the role of data-driven reserves reporting across different stakeholders.

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Implication

Webinar discusses data-driven models in shale exploration and drilling predictions, highlighting differing approaches.

Abstract

AAPG/SEG/SPE Energy in Data webinar hears from Hess on data-driven models in shale exploration. Corva on ROP drilling prediction. Schlumberger – use both ML and physics! Xecta don’t use ML on small data! Data-driven reserves reporting for anyone?

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Cite This Study

Neil McNaughton (2021) studied this question.

synapsesocial.com/papers/6997fa03ad1d9b11b3452d8c https://doi.org/10.69894/20210108

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Physics versus machine learning models

Key Points

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Implication

Abstract

Discussion

Cite This Study

Physics versus machine learning models

Key Points

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Implication

Abstract

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