A novel custom ensemble learning model for an improved reservoir permeability and water saturation prediction

With the advances of technology, many new well logs have been acquired over the past decade that carries vital information about the reservoir and subsurface layers. Thus, identifying the most relevant data that can improve the determination and prediction of petrophysical parameters has become very challenging. There has been an increase in the application of machine learning models that can accurately determine the petrophysical parameters of reservoirs, but further studies are still in demand. In this study, enhanced data analytics were used together with the visualisation techniques to pre-process the wireline logs acquired from the Volve field in the North Sea. Descriptive statistical methods were used to understand the relationship between the variables (input and output parameters), followed by applying the Extreme Gradient Boosting (XGBoost) regression model to predict the reservoir permeability and water saturation. A new ensemble model of Random Forest and Lasso Regularisation with an enhanced feature engineering technique was then proposed to improve the accuracy of the results. It appeared that the proposed ensemble model has a better performance than the traditional XGBoost and the hybrid PCA-XGBoost models in terms of precision, consistency and accuracy. The immense potential of ensemble modelling to enhance reservoir characterisation has been demonstrated by the success of this research.

Automated summary

With the advancement of technology, new well logs carrying vital information about reservoir and subsurface layers have been acquired. The application of machine learning models for predicting petrophysical parameters is increasing, with this study demonstrating the potential of ensemble modeling to improve reservoir characterization.