Optimization Of CO2 injection using multi-scale reconstruction of compositional transport

Abstract

The current situation with green gas emission requires the development of low carbon energy solutions. However, a significant part of the modern energy industry still relies on fossil fuels. To combine these two contradictory targets, we investigate a strategy based on a combination of CO2 sequestration with Enhanced Oil Recovery (EOR) in the hydrocarbon reservoirs. In such technology, the development of miscibility is the most attractive strategy from both technological and economic aspects. Modeling of this process involves solving complex nonlinear problem describing compositional flow and transport in highly heterogeneous porous media. An accurate capture of the miscibility development usually requires an extensive number of components to be present in the compositional problem which makes simulation run-time prohibitive for optimization. Here, we apply a multi-scale reconstructing of compositional transport to the optimization of CO2 injection. In this approach, a prolongation operator, based on the parametrization of injection and production tie-lines, is constructed following the fractional flow theory. This operator is tabulated as a function of pressure and pseudocomposition which then is used in the Operator-Based Linearization (OBL) framework for simulation. As a result, a pseudo two-component solution of the multidimensional problem will match the position of trailing and leading shocks of the original problem which helps to accurately predict phase distribution. The reconstructed multicomponent solution can be used then as an effective proxy-model mimicking the behavior of the original multicomponent system. Next, we use this proxy-model in the optimization procedure which helps to improve the performance of the process in several folds. An additional benefit of the proposed methodology is based on the fact that important technological features of CO2 injection process can be captured with lower degrees of freedom which makes the optimization solution more feasible.