A fast method to infer Nuclear Magnetic Resonance based effective porosity in carbonate rocks using machine learning techniques

Zeeshan Tariq, Manojkumar Gudala, Bicheng Yan*, Shuyu Sun*, Mohamed Mahmoud

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


A better estimation of the effective porosity of the reservoir rock is a critical task for petrophysicist and well logs analyst. A majority of the current approaches to estimate the effective porosity of the reservoir rocks from well logs are based on the information of the Density-Neutron logs. These approaches usually resulted in the inaccurate estimation of the rock porosity particularly in the naturally fractured carbonates or dolomite rocks. The Nuclear Magnetic Resonance (NMR) based effective porosity is independent of the rock matrix and mineralogy, on contrary it depends on the number of hydrogen nuclei in the pore spaces of the rock. In this study, we have used six machine learning (ML) techniques to predict the NMR based effective porosity in carbonate rocks. The ML models to predict the effective porosity includes deep neural networks (DNN), random forest regressor (RF), decision trees (DT), K-Nearest Neighbors algorithm (KNN), extreme gradient boosting (XGB), and adaptive gradient boosting (AdaBoost). These models were trained on the geophysical well logs such as Gamma ray log (GR), caliper log (Cali), neutron porosity log (NPHI), photoelectric factor log (PE), and bulk density log (RHOB). A total of 4002 data points were obtained from the five wells located in the carbonate field. The tuning of ML models hyperparameters were conducted using a ‘GridSearchCv’ method. Furthermore, the K-fold cross-validation criterion was implemented to improve the accuracy of the ML models. The ML models performances were evaluated using multiple graphical and goodness of fit tests including prediction cross-plots, average absolute percentage error (AAPE), root means square error (RMSE), and coefficient of determination (R2) methods. The prediction results showed that the DNN, RF, and XGB models performed better than the other implemented ML techniques. These methods resulted in a significantly low error and high (R2). The achieved accuracy was above 85% when validated on a blind dataset. This study also offered an empirical model that can be used to quickly estimate the NMR based effective porosity using afore-mentioned well logs. The model can also be used as a standalone package that can be coupled with any logging software for quick evaluation of NMR based effective porosity.

Original languageEnglish
Article number211333
JournalGeoenergy Science and Engineering
StatePublished - Mar 2023

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.


  • Carbonate reservoir
  • Deep learning
  • Machine learning
  • Mathematical model
  • Nuclear Magnetic Resonance

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Geotechnical Engineering and Engineering Geology


Dive into the research topics of 'A fast method to infer Nuclear Magnetic Resonance based effective porosity in carbonate rocks using machine learning techniques'. Together they form a unique fingerprint.

Cite this