Abstract An innovative and groundbreaking mathematical approach is proposed for the determination of the properties of reservoir materials, when oil reserves together with water are moving through porous media. Such problem seems to be very important for oil reservoir engineering. So, the above problem is reduced to the solution of a non-linear singular integral equation, which is numerically e...

A two-dimensional (2D) single particle model for the copolymerization of propylene-ethylene with heterogeneous Ziegler-Natta catalyst is developed. The model accounts for the effects of the initial shape of the catalyst and carck/ pore patterns on the copolymer composition, polymerization rate and the average molecular weight properties. The spherical and oblate ellipsoidal shapes of catalyst p...

as irrigation system installation is expensive and time consuming, it would be better to have an estimate of the water content provided by the system in the active root zone before designing an irrigation system. in this study, hydrus-2d performance was assessed under a tape irrigating in-situ with a heavy textured and relatively heterogeneous soil in rasht region. three different flows i.e. 2....

This paper concerns the discretization of multiphase Darcy flows, in the case of heterogeneous anisotropic porous media and general 3D meshes used in practice to represent reservoir and basin geometries. An unconditionally coercive and symmetric vertex centred approach is introduced in this paper. This scheme extends the Vertex Approximate Gradient scheme (VAG), already introduced for single ph...

Numerical simulation of multiphase flow in porous media is essential for many geoscience applications. Machine learning models trained with numerical data can provide a faster alternative to traditional simulators. Here we present U-FNO, novel neural network architecture solving problems superior accuracy, speed, and efficiency. U-FNO designed based on the newly proposed Fourier operator (FNO),...

Predicting multiphase flow in complex fractured reservoirs is essential for developing unconventional resources, such as shale gas and oil. Traditional numerical methods are computationally expensive, deep learning methods, an alternative approach, have become increasingly popular topic. Fourier neural operator (FNO) networks been shown to be a hundred times faster than convolutional (CNNs) pre...

Two-dimensional (2D) NMR techniques have been proposed as efficient methods to infer a variety of petrophysical parameters, including mixed fluid saturation, in-situ oil viscosity, wettability, and pore structure. However, no study has been presented to quantify the petrophysical limitations of such methods. We address this problem by introducing a pore-scale framework to accurately simulate su...