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Machine learning application to predict in-situ stresses from logging data
Determination of in-situ stresses is essential for subsurface planning and modeling, such as horizontal well planning and hydraulic fracture design. In-situ stresses consist of overburden stress (σ(v)), minimum (σ(h)), and maximum (σ(H)) horizontal stresses. The σ(h) and σ(H) are difficult to determ...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648745/ https://www.ncbi.nlm.nih.gov/pubmed/34873259 http://dx.doi.org/10.1038/s41598-021-02959-9 |