Advanced Rock Characterization by Dual Energy CT Imaging: A Novel Method in Complex Reservoir Evaluation

Abstract A quantitative model of the spatial distribution of reservoir properties is key to understanding reservoir heterogeneity. Special Core Analysis (SCAL) data is essential input for static and dynamic modeling of heterogenous reservoirs. To provide high-quality reliable data, the SCAL program should use the right samples from the core. Conventionally, integrated geological and petrophysical approaches are applied to select samples but they generally lack consistency and seldom incorporate upscaling options. This paper presents a novel methodology for core characterization and SCAL sample selection. SCAL data is used as input for spatial distribution of reservoir properties in a static reservoir model. The analysis is performed in siliciclastics and carbonate reservoirs from wells in the Bahrah Oil Field. An integrated X-ray, CT scanning, geological, and conventional core analysis approach is applied for understanding the reservoirs. We demonstrate the efficiency of dual-energy CT imaging in producing continuous whole core scans at 0.5 mm (500 micron) spacing and in deriving bulk density (BD) and effective atomic number (Zeff) logs along the core intervals. The high resolution 3D CT images improved the sedimentological descriptions of the core and the X-ray CT-derived numerical data (BD and Zeff) are used to derive porosity and mineralogy along the whole core sections. This information is then converted into lithology logs which predicted the cross-well correlation and enhanced the previously established correlation from conventional core descriptions. BD and Zeff cross plots suggested four lithotypes in the core intervals and the corresponding lithology log helped in deriving the percentage of each type: 1. Low BD (high pososity) carbonate formed around 20% of the whole cores. 2. High BD (low porosity) carbonate formed around 36% of the whole cores. 3. Low BD (high porosity) sandstone formed around 28% of the whole cores. 4. High BD (low porosity) sandstone formed around 16% of the whole cores. The data provided a unique capability for ensuring that the plugs adequately and correctly represented the lithotype variations along the core. The overall procedure helped minimize uncertainties in defining the rock types and effectively assign those rock types to the selected samples and core intervals. Introduction Accurate knowledge of petrophysical properties is required for efficient development, management, and prediction of future performance of oil and gas fields. This knowledge necessitates the understanding of the physical properties of the reservoir rocks, the interactions of various fluids with interstitial surfaces, and the distribution of pores and minerals within the porous medium. Reservoir characterization is often acquired at wide scales ranging from seismic data to core plug data (Ringrose et al. 2008). This is a complicated reservoir modeling process that involves upscaling and averaging of measurements at various scales. Core laboratory data has a major impact on this process and can help establish a sound basis for reservoir modeling and developing effective strategies for reservoir exploitation (secondary and EOR) schemes (Masalmeh and Jing 2008). However, the full characterization of cored intervals is often overlooked and random sampling is usually acquired for special core analysis (SCAL) measurements. This can lead to unrepresentative selection of the core samples and raises questions about the effectiveness of the core data in the reservoir model and its calibration. This is particularly of great importance in highly heterogeneous reservoirs such as carbonates, which are commonly characterized by multiple-porosity systems that impart petrophysical heterogeneity to the gross of the reservoir interval. This heterogeneity complicates the task of reservoir characterization and thus necessitates an accurate and detailed understanding of the geological heterogeneities and their impact on petrophysics and reservoir engineering.

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