Big data hiding in small rocks: Case study of advanced microscopy and image processing to aid upstream asset development

Objectives/Scope: 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. Methods, Procedures, Processes: 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. Results, Observations, Conclusions: 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. Novelty: 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.