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.

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High resolution Backscattered Electron images (BSE) can be used to extract textural information such as grain and pore size; specific surface area; and so forth. Based on this information porosity and permeability can be estimated. On the other hand, low resolution Energy Dispersive Spectroscopy Imaging (EDS) provides valuable information about the mineralogy and chemical composition of the rock samples. Scanning Electron Microscopy (SEM) instruments are capable of generating BSE images of core and cuttings samples in a relatively short period of time at very high resolutions. The story is different if EDS images are also acquired. For example EDS images at 20 μm point spacing usually do not take longer than 30 minutes to be captured, but if point spacing is reduced to 10 μm, then measurement time increases approximately by 4 times. This paper proposes a new methodology to combine high resolution BSE images with low resolution EDS images and to use the combined textural and mineralogical information in order to improve grain segmentation, grain size calculations as well as estimating porosity and permeability in rock samples. This methodology starts with the acquisition of the BSE and EDS images looking for an optimal relationship between acquisition time and image quality. Secondly the registration and fusion of the two images is performed and advanced image processing techniques are applied to extract information that corresponds to underlying physical characteristics such as porosity and permeability. Results of this methodology are encouraging, 4 to 5 μm point spacing BSE images have been registered and fused with 20 μm EDS images and excellent grain segmentation has been achieved. Calculated porosity values show a good match with helium porosity core data and estimated permeability using the Kozeny-Carman equation gives results in the same order of magnitude. This new approach saves time, operating costs and enable geoscientists to collect valuable rock data at the micro-scale level. Furthermore, this information can be used to estimate important rock properties when integrated with other data sources to improve reservoir characterization especially in cases where information is scarce or difficult to obtain.

Objectives/Scope While Image processing is still an area of research, standard workflows have emerged and are routinely used in Oil&Gas companies. However, while hardware capabilities have increased consequently, allowing large samples to be scanned with a high fidelity, permeability simulations are still limited to small samples unless having access to HPC. Direct simulations are known to be more flexible in terms of type of rocks, but limited in terms of sample size, while Pore Network Model based allow much larger sample sizes but less rock types. Methods In this study, we will focus on the pore space analysis of a middle-eastern carbonate sample. The rock sample is 7.5 cm tall and has a diameter of 3.8 cm. It has been acquired at 3 different resolution: a microCT scan at 16μm, a microCT scan of a 10 mm of diameter subsample at 5 μm, and a 10 mm of diameter SEM section at 2μm. This study will propose a methodology to mix the different scales in order to get an accurate pore space analysis of the largest possible sample size. Results As micro porous regions are visible at every scale, bringing uncertainty to the segmentation step, the first part of our analysis will consist of determining the most accurate pore space at the three different resolutions. We will rely on image registration (2D to 3D and 3D to 3D) and image based upscaling methods, further validated by simulation results. Given the large numerical size of the samples, specific workflows involving large data 3D visualization and processing will be presented. Then, different measures will be conducted: porosity and connected porosity, absolute permeability with three different methods (Lattice Boltzmann, Finite Volume, Pore Network Modeling), relative permeability curves using a Pore Network Model simulator. A new pore network model generation applicable to highly concave pore spaces such as carbonates ones will also be introduced. Novel A scalable method using automation will be presented, so that repeating the simulations on different samples of different space origins and size is easy. We will expose the results and limits of every method and will determine which size is bringing a convergence of the results. We will especially look at the convergence of direct based simulations and pore network model based ones, such that expanding the size prior to Pore Network Model generation can be reliable. In addition to the benchmark of the different simulation methods and their associated limits, the results will help us determining the representative elementary volume at different resolutions and the associated uncertainty depending on whether sub-resolution acquisitions are available or not.