Application of Multi-Resolution Graph-based Clustering for electrofacies prediction: a case study from the Horn River Shale, British Columbia, Canada

Abstract

The Horn River Basin is a major shale gas play in British Columbia, Canada. An important component for identifying productive zones is to determine lithofacies. Quantitative core-based lithofacies classifications distinguish reservoirs and non-reservoir quality zones. In this study, Elemental capture spectroscopy (ECS) log data, XRD, and TOC data were integrated with core-description data to define the shale gas development interval in the Horn River Shale. Six lithofacies were determined and used as a training data for the supervised electrofacies classification. The Multi-Resolution Graph-based Clustering method (MRGC) was useful for prediction of electrofacies. The MRGC facilitated estimation of the electrofacies from the well logs of the non-coring wells based on our current construction model. We constructed a supervised MRGC model from well A. The probability scores of the electrofacies were 83%. This method was applied to non-coring wells B and C. Lateral variations in the facies were inferred using an electrofacies correlation or seismic-scale spatial model. Stochastic methods were applied to build a 3D facies model due to the insufficient electrofacies data. To increase the accuracy and applicability we used seismically derived-trend data (density and P-wave sonic inversion, envelope attributes) for sequential indicator simulation (SIS) modeling. After constructing a facies model, the faintly laminated siliceous mudstone (FLSM) and homogeneous siliceous mudstone (HSM) facies were shown to be dominantly distributed throughout the Evie Member and Muskwa Formation. These facies contain high silica compared to clay and higher TOC contents which are generally the main targets of a shale gas play. Therefore, the FLSM and HSM facies of the Muskwa Formation and Evie Member are potentially productive facies for shale gas development in the Horn River Shale.