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.
Minhas, Naeem-Ur-Rehman (Baker Hughes Inc.) | Saad, Bilal (Baker Hughes Inc.) | Hussain, Maaruf (Baker Hughes Inc.) | Nair, Asok J. (Baker Hughes Inc.) | Korvin, Gabor (King Fahd University of Petroleum and Minerals)
Though ‘Big Data’ has been a much talked topic in recent years, its potential has not been fully utilized to study rocks for the purpose of improving asset development workflow. Our research has been focused on this topic. Upstream research publications combining imaging; elemental analysis and the mineral compositional information to derive a mineral map have recently started. This is very welcome as both SEM (scanning electron) and Optical Microscopy have tremendous latent potential to assist in reservoir characterization including depositional environment and diagenesis and to develop a more accurate reservoir model. In this study we describe new advanced image analysis that combines both SEM and optical microscopy. Results are used to study rock texture and predict rock fracture behavior.
Carbonate and sandstone rock samples were imaged using QEMSCAN (Quantitative Evaluation of Minerals using Scanning Electron Microscope) and optical microscopy analysis. Rock sections were prepared from cores. New digital data processing techniques were devised to extract the information and compute statistics and eventually automate data extraction.
The information from image processing such as porosity, grain size, shape, mineral associations, average distance between the neighboring grains, spatial distribution, crack patterns etc. has been used to find correlations between crack propagation and the texture of the rock. Combination of SEM and optical imaging techniques allows one to differentiate between cement and the mineral grains. It is found that the crack pattern is affected by the number of mineral grains per unit area. Higher number of mineral grains per unit area leads to more complex crack pattern which has implications for fraccability. Results show that quantitative microscopy provides a relationship between rock texture and fracture behavior. A new mathematical model is developed to predict the crack length as a function of grain size.
While recently XRD/XRF and elemental composition have been more frequently used by Industry, this study focuses on the importance of accurate, comprehensive and quantitative rock texture characterization. Novel image processing techniques and workflows developed by the authors were used to quantify texture. This work also reinforces the case of using complementary microscopy techniques for more accurate and insightful analysis.