Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern France Article Swipe
Joshua Pwavodi
,
Guy Marquis
,
Vincent Maurer
,
Carole Glaas
,
Anais Montagud
,
Jean‐Luc Formento
,
Albert Genter
,
Mathieu Darnet
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2024jh000154
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1029/2024jh000154
Accurately determining the mineralogical composition of rocks is essential for precise assessments of key petrophysical properties like effective porosity, water saturation, clay volume, and permeability. Mineral volume inversion is particularly critical in geological contexts characterized by heterogeneity, such as in the Upper Rhine Graben (URG), where both carbonate and siliciclastic formations are prevalent. The estimation of mineral volumes poses challenges that involve both linear and nonlinear relationships associated with geophysical data. To address this complexity, our methodology strategically integrates the robust insights from standard statistical approaches with three machine learning (ML) algorithms: multi‐layer perceptron, random forest regression, and gradient boosting regression. Furthermore, we propose a new hybrid ensemble model that incorporates a weighted average of multiple ML approaches to predict mineral composition within the Muschelkalk and Buntsandstein formations of the URG. ML techniques for mineral composition prediction in these formations exhibit robust predictive performance. The predicted mineral volumes align closely with quantitative estimates derived from X‐ray diffraction analysis. Additionally, they are in good qualitative agreement with mineral descriptions obtained from cores and cuttings of the Muschelkalk and Buntsandstein formations.
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- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.1029/2024jh000154
- OA Status
- diamond
- Cited By
- 1
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399651972
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Raw OpenAlex JSON
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https://openalex.org/W4399651972Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1029/2024jh000154Digital Object Identifier
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Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern FranceWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-01Full publication date if available
- Authors
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Joshua Pwavodi, Guy Marquis, Vincent Maurer, Carole Glaas, Anais Montagud, Jean‐Luc Formento, Albert Genter, Mathieu DarnetList of authors in order
- Landing page
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https://doi.org/10.1029/2024jh000154Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1029/2024jh000154Direct OA link when available
- Concepts
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Petrophysics, Geology, Geothermal gradient, Graben, Algorithm, Perceptron, Mineralogy, Water saturation, Petrology, Machine learning, Artificial neural network, Porosity, Geomorphology, Geophysics, Computer science, Geotechnical engineering, Structural basinTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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