Prediction of reservoir brittleness from geophysical logs using machine learning algorithms

Abstract

Brittleness is an important geomechanical property of reservoirs, which is usually estimated from cores or sonic logs that are expensive to acquire. In this study, we report data-driven, machine learning workflows to predict brittleness from less expensive, readily available conventional logs. We propose three strategies to predict brittleness using gamma ray, neutron porosity, density, caliper, sonic, and photoelectric factor logs by utilizing gradient boosting (GB), support vector regression (SVR), and neural networks (NN). The first strategy involves predicting brittleness directly from the logs while the second strategy predicts shear sonic logs used for the estimation of brittleness. The performance of the models given as R2 on deployment on the testing set for the first strategy is: GB (0.87), SVR (0.73), and NN (0.82), while for the second strategy: GB (0.94), SVR (0.87), and NN (0.94). In the third strategy, we convert the prediction into a classification task by grouping the brittleness estimate into ductile, transition, and brittle. The accuracy of model deployment on the testing set for the third strategy is: GB (89.37%), SVM (89.06%), and NN (89.16%). We demonstrate that depending on the strategy adopted, the gradient boosting algorithm outperforms the other peer algorithms in terms of training and validation scores. Furthermore, we combine the three algorithms using a committee machine to improve the performance of the model. The workflow in this study can be adopted to predict other reservoir properties from available logs. The workflow can also be adopted to characterize reservoir heterogeneity from seismic traces trained by well logs.