An Integrated Approach to Reservoir Characterization for Evaluating Shale Productivity of Duvernary Shale: Insights from Multiple Linear Regression

In the development of unconventional shale resources, production forecasts are fraught with uncertainty, especially in absence a full, multi-data study reservoir characterization. To forecast Duvernay gas vicinity Fox Creek, Alberta, multi-scale experimental findings thoroughly evaluated. The relationship between and parameters is assessed using multiple linear regression (MLR). Three hundred five core samples from fifteen wells were later examined MLR technique to discover fundamental controlling characteristics potential. Quartz, clay, calcite found comprise bulk shale. average values for effective porosity permeability 3.96% 137.2 nD, respectively, whereas amount total organic carbon (TOC) was 3.86%. predominantly deposited gas-generating timeframe. As input parameters, method calculated components governing productivity, including index (PI), saturation (Sg), clay content (Vcl), (F), (TOC), brittleness (BI), brittle mineral (BMC) (BMC). Shale output accurately predicted MLR-based prediction model. This research may be extended other reservoirs aid selection optimal well sites, resulting resources.