Digital Rock Technology for Quantitative Prediction of Acid Stimulation Efficiency in Carbonates

Abstract

The complex pore structure and porosity in carbonate rocks have a strong influence on the stimulation efficiency of the acid. Recently, it was shown that nondestructive tracers can be used to predict pore volume of acid to breakthrough (PVBT) for both plain and emulsified acids [1,2]. The present study explores the use of digital rock technology to simulate tracer injection in carbonate rocks. The accuracy of the digital rock method is compared to the laboratory measured values for five carbonate rocks of different porosity types and heterogeneity scales. The digital rock simulations are performed by an industry-unique direct hydrodynamic pore flow simulator that operates on pore geometries from digital rock models obtained from 3D scanning electron microscope images. The pressure-volume-temperature and rheological models used in the simulator represent real treatment and reservoir fluids. Fluid-solid interactions are introduced using distributed microscale wetting properties. The simulator uses the density functional approach applied to the hydrodynamics of complex systems. The simulations are used to calculate a measure of the accessible rock volume which is then used to predict the acid response.Conventional laboratory testing methods require large volumes of formation rocks for optimization of acid type and volume and it often prohibitively expensive. The digital rock methodology has the potential of significantly reducing the required volume of formation cores, which could be an enabler for more frequent carbonate porosity specific stimulation optimization. The present study explores the limits of its applicability.

Real-time Monitoring of Multiphase Flow within Rock Miniplugs using 2D X-ray Imaging

“…One of the contemporary concepts widely applied to rock analysis nowadays includes construction of digital models followed by the subsequent numerical simulations at pore scale of various physical processes taking place under real conditions [1][2]. In the framework of this concept the rock permeability is determined using single-or multiphase fluid flow simulations on X-ray microtomography (X-ray microCT) image-based digital model of the sample.…”

An Application of Deep Neural Networks for Segmentation of Microtomographic Images of Rock Samples

“…DR technology is becoming more and more attractive nowadays due to development of image acquisition and processing methods. There are many industrial applications of DR [3]: estimation of fluid transport properties such as absolute and relative permeabilities [4][5][6]; assessment of enhanced oil recovery (EOR) methods [7,8]; calculation of elastic moduli and electrical conductivity [9]; and modelling of nuclear magnetic resonance (NMR) response [10]. Figure 1 shows a typical DR workflow [11].…”