A novel approach to the quantitative evaluation of the mineral composition, porosity, and kerogen content of shale using conventional logs: A case study of the Damintun Sag in the Bohai Bay Basin, China

Abstract

The quantitative prediction of the mineral composition, porosity, and kerogen content of shales is significant for the evaluation of shale oil and gas potential and the hydraulic fracturing process. We have developed a new method for the shale’s components prediction (SCP-[Formula: see text]) by combining the back-propagation (BP) neural network and an improved [Formula: see text] method based on conventional logs. First, we constructed and calibrated the shale fraction model according to the volume of the minerals, kerogen, and porosity determined through laboratory analyses. Subsequently, we calculated the kerogen volume by the combination of the improved [Formula: see text] technique and the conversion equation between the kerogen volume and the organic carbon content. Finally, the BP neural network was trained with the input parameters of the kerogen volume and the sensitive logs, and the output parameters of the mineral volume (clay, silicate, carbonate, and heavy minerals) and porosity. We used the cross validation method to optimize the structural parameters of the BP neural network. The SCP-[Formula: see text] method, which is a nonlinear technique, takes into consideration the influence of the organic carbon of the residual oil on the calculation of the kerogen volume. We successfully implemented the SCP-[Formula: see text] method to evaluate the shale components of well Shen 352 in the Damintun Sag, China. The evaluation results of the SCP-[Formula: see text] method are in good agreement with the measured core sample properties and mineral composition derived from Schlumberger elemental-capture spectroscopy logs, confirming the accuracy and reliability of the SCP-[Formula: see text] method in predicting the mineral composition, porosity, and kerogen content in shale.