Extended Fluid Discretization and Physics Informed Neural Network Modelling for Fracking Carbonate Reservoirs with Silica Sand-Based Proppants in Kazakhstan

Collaborative Research Program for 2025-2027

The aim of this project is to enhance the effectiveness of silica sand (SS) proppant for hydraulic fracturing in Kazakhstan. The synthesis of proppants with silica sands, solvents, deflocculants, binders, and plasticizers in a laboratory setting offers a unique method for maximizing oil and gas production through fracture conductivity. Additionally, this project will extensively employ computational simulations through the development of computational fluid dynamics using an extended discrete method (CFD-XDEM) and a TensorFlow deep learning algorithm to dynamically estimate the rheology of nanofluids (silica sand) for optimizing fracture conductivity.The uniqueness of this project lies in its ability to assist well completion engineers and contractors in locating SS-sites and adapting the physics and data-driven experiments of SS-proppants rheology as a nanofluid hydraulics alternative for future fracking purposes. From this informed project, the research will eliminate the need for expensive downhole tools for monitoring and predictions, and instead provide alternative multiphysics and multiscale measures that are more suitable for the research communities and local industry, thereby enhancing the decision-making process.