Integrating NMR and machine learning for pore-type driven rock classification in the heterogeneous Asmari carbonate reservoirs
Maryam Veysi, Ali Kadkhodaie, Mehran Arian, Mohsen Aleali, Zahra Maleki, Pooria Kianoush · 2025
Read Integrating NMR and machine learning for pore-type driven rock classification in the heterogeneous Asmari carbonate reservoirs by Maryam Veysi, Ali Kadkhodaie, Mehran Arian, Mohsen Aleali, Zahra Maleki, Pooria Kianoush (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12824165.txt.
Description
Maryam Veysi and co-authors classify oil-bearing rock by its pores in a 2025 study. Iran's Asmari carbonate reservoirs vary wildly, and stone type alone misjudges flow. The team combined magnetic resonance well logs, which sense pore sizes and fluid movement, with explainable machine learning.
Intervals with equal porosity differed three to five fold in how well hydrocarbons move, proving pore shape rules. Thin sandstone layers, just fifteen to twenty percent of thickness, delivered sixty percent of production. The model sorted flow units with ninety-two percent accuracy. Readers get a pore-first method for squeezing mature fields.
Catalog record
| CREATOR | Maryam Veysi, Ali Kadkhodaie, Mehran Arian, Mohsen Aleali, Zahra Maleki, Pooria Kianoush |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 134 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |