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dc.contributor.authorYu, H.
dc.contributor.authorRezaee, M. Reza
dc.contributor.authorWang, Z.
dc.contributor.authorHan, T.
dc.contributor.authorZhang, Yihuai
dc.contributor.authorArif, Muhammad
dc.contributor.authorJohnson, Lukman
dc.identifier.citationYu, H. and Rezaee, M.R. and Wang, Z. and Han, T. and Zhang, Y. and Arif, M. and Johnson, L. 2017. A new method for TOC estimation in tight shale gas reservoirs. International Journal of Coal Geology. 179: pp. 269-277.

Total organic carbon (TOC) estimation is significantly crucial for shale reservoir characterization. Traditional TOC estimation methods (such as Passey and Schmoker method) do not provide accurate TOC predictions in shale gas r eservoirs especially for the self-generated and self-stored reservoirs. This study proposes, for the first time, a new TOC prediction method based on Gaussian Process Regression (GPR) bridging geostatistics and machine learning technique. The method utilizes a non-parametric regression approach in shale TOC predictions, and not only provides the expert solutions in high-dimension processing, small samples and non-linear problems, but also has a better adaptation and generalization ability compared with other machine learning methods. The approach accounts for all the well logging attributes and chooses the relevant logs to build TOC estimation model, and 7 different kernel functions and 5 attributes groups are analyzed to get the optimized hyperparameters in practice. Application of the developed model to two shale gas reservoirs showed that the model predicted TOC matched well with that from the laboratory measurements. The proposed model based on GPR method provides an accurate way for the TOC prediction in the tight shale gas reservoirs.

dc.titleA new method for TOC estimation in tight shale gas reservoirs
dc.typeJournal Article
dcterms.source.titleInternational Journal of Coal Geology
curtin.departmentDepartment of Petroleum Engineering
curtin.accessStatusOpen access

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