Mineralogy and Geomechanical Analysis for Hydraulic Fracturing: An Integrated Approach to Assess Rock Fracability in Sandstone Reservoirs

Abstract

Abstract A comprehensive integrated methodology is presented that assesses rock hydraulic fracability using rock geomechanics, mineralogy, and chemical compositions. This petrophysical multi-min based approach utilizes sonic and density log data to relate mineralogy and geomechanics to rock stiffness and its susceptibility to fracture. Rock fracability is assessed in terms of compressional and shear transit times from borehole acoustic logging. These transit times are used to calculate Poisson's ratio and Young's modulus that provide an indication of the tendency of a rock to fracture while preserving the opening of the fracture. The lower the Poisson's ratio, the more brittle is the rock and the higher the value of Young's modulus. Brittleness index and fracability index are introduced to predict fracable rocks; sands with a brittleness index of 50 and higher and fracability index above 0 and less than 1 are fracable candidates. This geomechanical analysis is combined with rock mineralogy to make conclusive predictions about rock fracability. Rocks rich in silicates and sufficient clay volumes exhibit high rock fracability while ductile rocks negatively impact fracability. The success of the hydraulic fracturing jobs was observed to be directly related to rock mineralogy. The mineralogy groups for the wells in study were silicates (Quartz and Orthoclase) and clays (Illite, Kaolinite, and Chlorite). Quartz associated with sufficient Illite volumes promoted fracability. However, high contents of Kaolinite led to unfavorable results, whereas small quantities of Chlorite positively impacted fracability and promoted well deliverability. The integrated approach showed good predictability and consistency of rock fracability with a certain degree of error (within 10%). This approach has been applied successfully on different sands with a wide variation in chemical compositions. It can predict wells with the highest success rate of the fracturing operation, save the injectivity test, minimize the fracturing costs and optimize the economics of tight reservoirs.