Integrated Framework for Modelling of Thermal-Compositional Multiphase Flow in Porous Media

Abstract

Various novel computing architectures, like massively parallel and multi-core, as well as computing accelerators, like GPUs or TPUs, keep regularly expanding. In order to exploit the benefits of these architectures to the full extent and speed up reservoir simulation, the source code has to be inevitably rewritten, sometimes almost completely. We demonstrate how to extract complex physics-related computations from the main simulation loop, leaving only an algebraic multilinear interpolation kernel instead. In combination with linear solvers, which usually have made available soon once the new architecture is introduced, the approach accommodates execution of the entire nonlinear loop on the latest hardware and computational architectures. We describe the integrated simulation framework built on top of this technique and show the applicability of the approach to various challenging physical and chemical problems. All simulation engines along with linear solvers, well controls, interpolation engines, and state operator evaluators are implemented in C++11 and exposed into Python coupling the flexibility of the script language with the performance of C++.

Efficient Inverse Modeling Framework for Energy Transition Applications Using Operator-Based Linearization and Adjoint Gradients

“…The well controls and constraints are inner boundary conditions of the governing equations. In DARTS, a connection-based multi-segment well model is used to simulate the flow in the wellbore (Khait and Voskov, 2019). Multiple well blocks representing the perforations are connected to the reservoir girdblocks.…”