ABSTRACT

Digital Rock technologies were developed to augment traditional core analysis and led to a much-improved understanding of the microstructure of many rock core types. However, to produce an upscaled description of the reservoir, one must consolidate the measurements in scale over six orders of magnitude. Here, we show that a whole core CT scan may serve as the natural link between the length scales of Digital Rocks and modern logging tools. While the CT scan contains a fingerprint of the structure of the reservoir, the Digital Rock models show the microscopic composition of each CT scan voxel. For upscaling purposes, we established a quadratic correlation between the grey values in a CT scan and the porosities measured on core plugs. This correlation allowed us to generate a synthetic porosity log of millimeter resolution. After that, the length scale was increased by moving averages in the vertical direction. We investigated a thin bed reservoir with layers of halite filled sandstone alternating with layers free of halite at variable layer thicknesses. In this reservoir, the resulting synthetic porosity log compared well with the NMR log porosity within the uncertainty band over a total depth interval of 53.6 meters. We propose that field decisions could be accelerated if the quadratic correlation parameters are general for these types of sediment. In this case, one may generate synthetic porosity logs as soon as the CT scan is available, which is typically the first step in standard core analysis.

INTRODUCTION

Formation evaluation and reservoir modeling are key elements in integrated subsurface workflows aiming to assess the uncertainty in field development plans. Both formation evaluation and building reservoir models require rock properties like porosity φ, permeability K, but also relative permeability kr and capillary pressure pc functions as input. These are obtained by core analysis and special core analysis in the form of core flooding experiments which are not only costly and time-consuming but often arrive at a stage when the first investment decision on the respective field development plan has already been taken. Digital Rock aims to complement and augment conventional core analysis with the potential benefit of increased speed and improved sampling density. In Digital Rock workflows, the porosity, permeability, and other parameters are computed based on digital images obtained by X-ray computed micro-tomography (Blunt, 2017) on rock samples of few millimeters in size. To accurately predict average rock properties on the scale of conventional core analysis and logging tools requires upscaling when the rocks are heterogeneous (Masalmeh, 2008).

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