Ensemble Learning for Predicting TOC from Well-Logs of the Unconventional Goldwyer Shale

Partha Pratim Mandal, Reza Rezaee and Irina Emelyanova
Additional contact information
Partha Pratim Mandal: Western Australia School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Perth, WA 6151, Australia
Reza Rezaee: Western Australia School of Mines: Minerals, Energy and Chemical Engineering, Curtin University, Perth, WA 6151, Australia
Irina Emelyanova: CSIRO Energy, Geoscience Data Analytics, Perth, WA 6151, Australia

Energies, 2021, vol. 15, issue 1, 1-30

Abstract: Precise estimation of total organic carbon (TOC) is extremely important for the successful characterization of an unconventional shale reservoir. Indirect traditional continuous TOC prediction methods from well-logs fail to provide accurate TOC in complex and heterogeneous shale reservoirs. A workflow is proposed to predict a continuous TOC profile from well-logs through various ensemble learning regression models in the Goldwyer shale formation of the Canning Basin, WA. A total of 283 TOC data points from ten wells is available from the Rock-Eval analysis of the core specimen where each sample point contains three to five petrophysical logs. The core TOC varies largely, ranging from 0.16 wt % to 4.47 wt % with an average of 1.20 wt %. In addition to the conventional MLR method, four supervised machine learning methods, i.e., ANN, RF, SVM, and GB are trained, validated, and tested for continuous TOC prediction using the ensemble learning approach. To ensure robust TOC prediction, an aggregated model predictor is designed by combining the four ensemble-based models. The model achieved estimation accuracy with R 2 value of 87%. Careful data preparation and feature selection, reconstruction of corrupted or missing logs, and the ensemble learning implementation and optimization have improved TOC prediction accuracy significantly compared to a single model approach.

Keywords: TOC; Goldwyer shale; well-logs; ensemble learning; canning basin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/1/216/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/1/216/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2021:i:1:p:216-:d:713802

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().