We have developed a statistical method for investigating the importance of different log measurements for picking the best zones for hydraulic fracturing. We have determined the method’s applicability using data from unconventional reservoirs (Eagle Ford, Haynesville, Barnett, and a reservoir from the Middle East). The analysis began with single log measurements (e.g., gamma ray [GR], compressional and shear sonic [DTC and DTS], and spectral gamma ray [SGR], which could measure the radioactivity of uranium [U], potassium [K], and thorium [Th]). Other types of measurements, including density (RhoB), neutron porosity (NPHI), and resistivity, were added to obtain more complex logging suites. These log measurements were the inputs for this analysis. Each input combination was referred to as a “scenario.” Parameters such as effective porosity (PhiE), brittleness, total organic carbon (TOC), production index (PI), and fracture index (FI) were referred to as the outputs for the analysis. We have investigated linear and nonlinear combinations of the inputs to predict the outputs. Various scenarios, beginning with the simplest cases and ending with the most complete combination, were tested. The selection of log combinations was either based on the importance of individual logs or on industry-standard combinations (such as triple and quad combos). For each scenario, we computed correlation coefficients and root-mean-square errors of predicting the output parameters. The prediction accuracies generally increased as a result of increasing the number of input logs. Our analysis clearly found the importance of using SGR (for PI and FI prediction) and resistivity (for TOC prediction) logs. Based on comparison of the reconstruction results, actual values, and correlation coefficients/errors, we ranked the log combinations for predicting/modeling a specific parameter. The most challenging properties to model included TOC, PhiE, PI, and FI; the easiest properties to predict were brittleness and Young’s modulus.