Determination of pressure drops in flowing geothermal wells by using artificial neural networks and wellbore simulation tools

Bassam, A.; Melgoza‐Castillo, Alicia; Garcı́a-Valladares, O.; Santoyo, E.

doi:10.1016/j.applthermaleng.2014.05.048

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2016

2024

Publication Types

Select...

Book4

Article1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 29 publications

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Al‐Fakih,

Abdulraheem,

Kaka

2024

Deep Underground Science and Engineering

View full text Add to dashboard Cite

This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post‐2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.

show abstract