Advanced Machine Learning application for Permeability Prediction for (M) Formation in an Iraqi Oil Field


  • Noor alhuda K. Mohammed Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq
  • Ghanim M. Farman Department of Petroleum Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq



Machine learning, Random forest, Multiple linear regression, Permeability prediction.


Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy and the performance of the algorithms. The random forest algorithm was the most accurate method leading to lowest Root Mean Square Prediction Error (RMSPE) and highest Adjusted R-Square than multiple linear regression algorithm for both training and testing subset respectively. Thus, random Forest algorithm is more trustable in permeability prediction in non-cored intervals and its distribution in the geological model.


Z. A. Mahdi and G. M. Farman, “A Review on Models for Evaluating Rock Petrophysical Properties”, Iraqi Journal of Chemical and Petroleum Engineering, vol. 24, no. 1, pp. 125–136, 2023.

R. A. Hashim and G. M. Farman, “Evaluating Petrophysical Properties of Sa’di Reservoir in Halfaya Oil Field”, Iraqi Geological Journal, vol. 56, no. 2D, pp. 118–126, 2023.

X. Yang, Y. Mehmani, William A. Perkins, A. Pasquali, M. Schönherr, K. Kim, M. Perego, M. L. Parks, N. Trask, M. T. Balhoff, M. C. Richmond, M. Geier, M. Krafczyk, L. Luo, A. M. Tartakovsky, and T. D. Scheibe, “Intercomparison of 3D pore-scale flow and solute transport simulation methods”, Adv. Water Resour., vol. 95, pp. 176–189, 2016.

H A. H. Tali, S. K. Abdulridha, L. A. Khamees, J. I. Humadi, G. M. Farman, and S. J. Naser, “Permeability estimation of Yamama formation in a Southern Iraqi oil field, case study”, Second International Conference on Innovations in Software Architecture and Computational Systems (ISACS 2022), 2023-01-05 | Conference paper.

D. A. Otchere, T. O. A. Ganat, R. Gholami, and M. Lawal, “A novel custom ensemble learning model for an improved reservoir permeability and water saturation prediction,” J. Nat. Gas Sci. Eng., vol. 91, 103962, 2021.

N. A. Kadhim and G. M. Farman, “Advanced Geostatistical Techniques for Building 3D Geological Modeling: A Case Study from Cretaceous Reservoir in Bai Hassan Oil Field”, Iraqi Geological Journal, vol. 56, no. 2F, pp. 214-227, 2023.

G. Zeynalov, Muhammed A. Ismail, and F. Al-Beyati, “Petro physical Modeling of the Tertiary Reservoirs in the Small Giant Bai Hassan Oil”, Int. J. Enhanc. Res. Sci. Technol. Eng., vol. 5, no. 12, pp. 80–89, 2016.

U. S. Alameedy, A. T. Almomen, and N. Abd, “Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data”, Iraqi Geol. J., vol. 56, no. 1D, pp. 175–187, 2023.

M. W. Berry, A. Mohamed, and B. W. Yap, "Supervised and unsupervised learning for data science", Springer, 2019.

A. Singh, N. Thakur, and A. Sharma, “A review of supervised machine learning algorithms”, in 2016 3rd international conference on computing for sustainable global development (INDIACom), Ieee, pp. 1310–1315, 2016.

H. Wang and S. Chen, “Insights into the Application of Machine Learning in Reservoir Engineering: Current Developments and Future Trends”, Energies, vol. 16, no. 3, 1392, 2023.

A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry”, Pet. Res., vol. 6, no. 4, pp. 379–391, 2021.

H. Gamal, A. Alsaihati, and S. Elkatatny, “Predicting the rock sonic logs while drilling by random forest and decision tree-based algorithms”, J. Energy Resour. Technol., vol. 144, no. 4, 2022.

H. Gamal, A. Alsaihati, S. Elkatatny, S. Haidary, and A. Abdulraheem, “Rock strength prediction in real-time while drilling employing random forest and functional network techniques”, J. Energy Resour. Technol., vol. 143, no. 9, 2021.

M. A. Mirza, M. Ghoroori, and Z. Chen, “Intelligent Petroleum Engineering”, Engineering, vol. 18, pp. 27–32, 2022.

A. Kumar, “A Machine Learning Application for Field Planning,” in Offshore Technology Conference, Offshore Technology Conference, Paper Number: OTC-29224-MS, Houston, Texas, 2019.

M. Cordeiro-Costas, D. Villanueva, P. Eguía-Oller, and E. Granada-Álvarez, “Machine learning and deep learning models applied to photovoltaic production forecasting”, Appl. Sci., vol. 12, no. 17, 8769, 2022.

L. Breiman, “Random forests”, Mach. Learn., vol. 45, pp. 5–32, 2001.

W. J. Al-Mudhafar, “Applied Geostatistical Reservoir Characterization in R: Review and Implementation of Rock Facies Classification and Prediction Algorithms-Part I”, in Offshore Technology Conference, OnePetro, 2016.

S. Bhattacharya and S. Mishra, “Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: Case studies from the Appalachian basin, USA”, J. Pet. Sci. Eng., vol. 170, pp. 1005–1017, 2018.

W. J. Al-Mudhafar, “Advanced supervised machine learning algorithms for efficient electrofacies classification of a carbonate reservoir in a giant southern iraqi oil field”, in Offshore Technology Conference, OnePetro, 2020.

J. L. L. Pereira, "Permeability prediction from well log data using multiple regression analysis", West Virginia University, 2004.

B. A. Al-Baldawi, “Permeability Estimation of Khasib Formation in Amara Oil Field from Well logs Data Using Multilinear Regression Technique and Empirical”, J. Pet. Res. Stud., vol. 6, no. 1, 2016.




How to Cite

Mohammed, N. alhuda K. .; Farman, G. M. . Advanced Machine Learning Application for Permeability Prediction for (M) Formation in an Iraqi Oil Field . Journal of Petroleum Research and Studies 2024, 14, 60-76.