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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects

Crossref DOI link: https://doi.org/10.1007/s40948-024-00904-4

Published Online: 2024-12-02

Published Print: 2024-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Davy, Nandito https://orcid.org/0009-0009-4820-4858
El-Husseiny, Ammar https://orcid.org/0000-0001-5762-6109
Waheed, Umair bin https://orcid.org/0000-0002-5189-0694
Ayranci, Korhan https://orcid.org/0000-0002-4808-9898
Fawad, Manzar https://orcid.org/0000-0001-6835-7442
Mahmoud, Mohamed https://orcid.org/0000-0002-4395-9567
Harris, Nicholas B. https://orcid.org/0000-0001-6044-4701
Funding

Funding for this research was provided by:

College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals (CPG/2024/00604, CPG/2024/00604)
License Information

Text and Data Mining valid from 2024-12-01

Version of Record valid from 2024-12-02
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Article History

Received: 27 August 2024

Accepted: 9 November 2024

First Online: 2 December 2024

Declarations

:

: The authors declare no competing interests.

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