Improved Reservoir Characterization Through Rapid Visualization and Analysis of Multiscale Image Data Using A Digital Core Analysis Ecosystem

Abstract

Efficient integration of multiscale image and petrophysical data is becoming increasingly important to tackle emerging reservoir characterization challenges associated with complex carbonate and unconventional reservoirs. In this paper we illustrate an integrated digital rock physics and petrophysical data analysis methodology empowered by a digital core analysis ecosystem, for defining reservoir rock types and flow units in a micritic carbonate formation. We apply the methodology to 35 meters of cored well data acquired from the Late Jurassic Upper Jubayla Formation, equivalent to the lower Arab-D reservoir in Saudi Arabia. Pre-processing, segmentation and digital rock physics calculations are performed using whole core computed tomography (CT), plug micro-CT, thin-section micrographs and scanning electron microscopy data. Further whole core CT data analysis includes generation of mean intensity and heterogeneity logs. The digital rock ecosystem is applied to these multiscale image data and to spatially correlate with petrophysical well logs. The unique whole core CT processing step in the workflow allows the core barrels to be intelligently removed, and all the cores to be stitched together regardless of the total size of data. We thus access the full advantage of 3D whole core CT data that provides significantly high vertical resolution of rock properties in the well interval. Furthermore, the live ecosystem enables the continuous integration of image and petrophysical data as they become available over the duration of this study. Results from digital image analysis reveal the micro-and macro-pore types and their connectivity across multiple scales. Combined with plug and thin section data, log interpretation and digital image analysis, these pore types are upscaled into well log scale through texture-based rock-typing. The digital core analysis ecosystem we employ in this study has a unique capability of visualizing and analyzing large volumes of image and petrophysical data, allowing a novel method for rock-typing. The proposed methodology is scalable to data sets consisting of many wells, thus making it a valuable tool for accurate characterization of complex carbonate and shale reservoirs, which are becoming increasingly reliant on high resolution imaging techniques for pore space characterization.

Optimizing digital image analysis of thin sections for reliable pore network characterization

“…The samples cover wackestone, packstone, and mudstone facies of predominantly calcite mineralogy, with various degrees of diagenetic alteration, including dolomitization and intensive micrite recrystallization. Helium porosity and air permeability measurements were performed on all the core plugs (Chandra et al 2019). Thin section photomicrographs were acquired for all the samples using 10x optical magnification with a resolution of 1 𝜇𝑚 2 /pixel.…”

“…Optimizing digital image analysis of thin sections for reliable pore network characterization Introduction Digital rock analysis provides a less expensive and much faster approach to obtaining rock properties that are crucial for reservoir characterization. Recent advances in computing and changing global economic and environmental policies are steadily driving digital rock analysis into becoming an integral part of Special Core Analysis in the oil and gas industry (Jobe et al 2005, Al-Bazzaz et al 2007, Al Ibrahim et al 2012, Budennyy et al 2017, Chandra et al 2019. In this study we present a digital image analysis methodology that applies machine learning for image processing and classification of thin section images for reliable pore network characterization.…”