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

Noor alhuda K.  Mohammed; Ghanim M.  Farman

doi:10.52716/jprs.v14i1.777

Authors

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

DOI:

https://doi.org/10.52716/jprs.v14i1.777

Keywords:

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

Abstract

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.

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