Machine Learning Approach for Reservoir Petrophysical Properties Prediction from Well-Logs Data in the Niger Delta

Abstract

Abstract In this study, machine learning (ML) models were developed to predict permeability (k), porosity (φ) and water saturation (Sw) using 1241 datasets obtained from well-logs data in the Niger Delta. The datasets were screened to remove incomplete sets and outliers and make them suitable for adequate training using the maximum-minimum normalization approach. Three multiple-input multiple-output (MIMO) machine learning methods, namely artificial neural network (ANN), decision tree (DT) and random forest (RF), were used to train the datasets. Five performance metrics, coefficient of determination (R2), correlation coefficient (R), mean absolute error (MAE), average absolute relative error (AARE), and average relative error (ARE), were used to evaluate the performance of the developed models. The results indicate that the MIMO neural-based model had overall MSE and R values of 1.9801×10-3 and 0.9866, while the DT model had 2.2540×10-3 and 0.98281, and the RF model had 5.1490×10-3 and 0.95989. The ANN model predicted k resulted in R2, R, MAE, ARE, and AARE of 0.95740, 0.97847, 2.0677, -0.0011, and 0.0343, respectively, while the predicted φ had R2 of 0.96336, R of 0.98151, MAE of 0.0055, ARE of -0.0006, and AARE of 0.0185. The predicted Sw had an R2 of 0.98430, R of 0.99212, MAE of 0.0265, ARE of -0.0045, and AARE of 0.0521. Also, the developed DT model predicted k resulted in R2, R, MAE, ARE and AARE of 0.95250, 0.97596, 0.0277, 5.6981 and 0.0382, respectively, while the predicted φ had R2 of 0.9380, R of 0.9685, MAE of 0.0276, ARE of -0.5796 and AARE of 5.8199. The predicted Sw had R2 of 0.99039, R of 0.9518, MAE of 0.0182, ARE of -0.49969 and AARE of 5.0452. Furthermore, the developed RF model predicted k resulted in R2, R, MAE, ARE, and AARE of 0.88438, 0.94041, 0.0552, -6.8754 and 15.8391, respectively, while the predicted φ had R2 of 0.90377, R of 0.95067, MAE of 0.0504, ARE of -5.3429 and AARE of 12.8260. The predicted Sw had R2 of 0.95495, R of 0.97722, MAE of 0.0469, ARE of -25.1422 and AARE of 32.6698. The relative importance of the ML input parameters on the predicted outputs is RES>D>GR>VSh>RHOB>NPHI>CALI. Based on the statistical indicators obtained, the predictions of the developed ML-based models were close to the actual field datasets. Thus, the ML-based models should be used as tools for predicting k, φ and Sw in the Niger Delta.