Rock Texture Characterization from Automated Petrographic Analysis

Abstract

Abstract The recent crash in the oil market has allowed the industry to reduce the pace of evaluation and completion decisions in unconventional reservoirs, and turn to a more science-based decision-making process for project execution. The traditional stimulation design based on the geometric spacing of induced fractures is now gradually changing to geological spacing (i.e., a design based on an understanding of the reservoir geology) to enhance hydraulic fracture stimulation effectiveness for drastically reduced cost. A methodical rock texture characterization of core samples and cuttings can provide powerful information that can be used reliably and cost-effectively to optimize fracture stimulation designs by placing frac stages based on rock characteristics. This paper presents a new method to quantify rock texture based on automated petrographic analysis that uses advanced microscopy image analysis from scanning electron microscopy (SEM) and optical microscopy. A procedure called "quantitative evaluation of minerals using a scanning electron microscope" (QEMSCAN) and optical microscopy analyses were used to image rock samples prepared from cores and cuttings. Rock texture parameters were extracted automatically using new digital data processing techniques. The information from automated petrographic analysis was used to determine the spatial distribution of all components including mineral composition, framework grains, matrix, cement, grain size and shape, pore size and shape, modes of contact between grains and the nature of porosity. The results showed that while mineral composition of rock is important, texture characterization is far more significant to understand rock behavior than has been reported in the industry. Our results demonstrate the importance of quantitative microscopy and how it can provide an understanding of the key relationship between rock texture and rock behavior. A new method was produced to characterize rock texture quantitatively from advanced image analysis with the aid of an automated petrographic system.