LAUSR

Abstract A MATLAB based finite volume CEOR simulator was developed for modeling the recovery of oil by low salinity water polymer flooding (LSWPF) in a proposed geostatistical method of generating porous media. Firstly, this paper coupled ion exchange, wettability and fluid flow transport models to the proposed heterogeneous porous media to investigate LSWPF oil recovery performance. Secondly, for the first time, a new hybrid smart proxy model of particle swarm optimization artificial neural network (PSO-ANN) recovery factor (RF) correlation was derived for LSWPF oil recovery performance using the extracted weights and biases. The proposed porous media as well as stochastic multi-fractal random field and circulant embedding method (CEM) permeability field realizations did not exhibit artificial banding unlike turning bands method (TBM) realizations when compared. A fast convergence pre-conditioner solution technique of algebraic multigrid (AMG) linear solver combined with an acceleration method of biconjugate gradients stabilized method (BI-CGSTAB) was used to solve the fully implicit system of coupled linearized equations. The simulator was validated with PHREEQC geochemical package and a secondary LSWPF coreflood test with outstanding history match. Numerical 2D examples study in this paper revealed the synergetic effects of LSWPF and high salinity water polymer flooding (HSWPF) with higher oil RFs of 39% and 31%, respectively than the standalone LSWF of 23% and normal waterflood of 11% for 50 days of production. The optimal architecture of the PSO-ANN RF model is 7 inputs, 3 hidden neurons and 1 output. Also, the PSO-ANN RF model predictions were compared to the LSWPF numerical simulation results with the former remarkably having reduced computational time in seconds with training and blind testing errors