A novel custom ensemble learning model for an improved reservoir permeability and water saturation prediction
Document Type
Article
Publication Date
7-1-2021
Abstract
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.
Keywords
Reservoir characterisation, Feature selection, Ensemble learning, Artificial intelligence
Divisions
Science
Funders
University Teknologi Petronas through Y-UTP grant[015LCO-105],Centre of Research in Enhanced Oil recovery through Y-UTP grant[015LCO-105]
Publication Title
Journal of Natural Gas Science and Engineering
Volume
91
Publisher
Elsevier Sci Ltd
Publisher Location
THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND