Otchere, Daniel Asante and Ganat, Tarek Omar Arbi and Gholami, Raoof and Lawal, Mutari (2021) A novel custom ensemble learning model for an improved reservoir permeability and water saturation prediction. Journal of Natural Gas Science and Engineering, 91. ISSN 1875-5100, DOI https://doi.org/10.1016/j.jngse.2021.103962.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | University Teknologi Petronas through Y-UTP grant[015LCO-105], Centre of Research in Enhanced Oil recovery through Y-UTP grant[015LCO-105] |
| Uncontrolled Keywords: | Reservoir characterisation; Feature selection; Ensemble learning; Artificial intelligence |
| Subjects: | Q Science > QE Geology T Technology > TC Hydraulic engineering. Ocean engineering T Technology > TN Mining engineering. Metallurgy |
| Divisions: | Faculty of Science |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Apr 2022 00:46 |
| Last Modified: | 18 Apr 2022 00:46 |
| URI: | http://eprints.um.edu.my/id/eprint/26766 |
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