Shale gas reservoirs have become prominent contributors to the world's hydrocarbon resources and production. They exhibit multiple storage mechanisms, two of which are linked to the free and adsorbed gas phase. Since the adsorbed gas may be stored as a denser phase than the free gas, the contribution of the adsorbed phase can be significant. The adsorbed volume is related to the total organic carbon (TOC) and thus, higher TOC can indicate higher hydrocarbon inplace. Furthermore, productivity can be linked to TOC through the potential for overpressure and conversion of kerogen to pore space. However, estimation of the TOC is not a trivial problem, as it depends on geological factors such as depositional environment. In this study, we propose an integrated workflow using concepts of machine learning to estimate TOC.

The workflow is divided into 3 sections which are area selection, sub-region categorization, and prediction modeling. Firstly, 3 active exploration and development areas (Kaybob, Pembina, and East shale basin) of the Duvernay Formation are highlighted and the geology of each specific area is analyzed. Thereafter, using the available core data and average properties of the attributes (Gamma Ray, resistivity, density, and distance from mean vitrinite reflectance line), each area is clustered into sub-regions using SVM, logistic regression, and k-means clustering. Finally, using Random Forest prediction, models for each sub-region are developed and ranked with average mean square errors and standard deviations.

It is observed that the Kaybob area can be clustered into 2 regions. This observation is supported by the principal component plot (PC1 vs PC2), which shows a dual cloud structure. This is further supported through clustering analysis, which also revealed the same observation. Results of the prediction modeling found random forest as the best predictor, achieving a match wiht the core data with a error less than 10% and in some cases only a 1% deviation.

Shale reservoir characterization requires estimation of the key parameters such as TOC. However, it is difficult to estimate TOC with purely physics-based or purely statistical models, as it requires limited specialized data and is impacted by subtle variations in the reservoir. This study suggests that TOC can be accurately estimated by combining geological interpretation and machine learning based algorithms without bearing cost of the specialized data.

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