A Fully Implicit, Parallel, Compositional Chemical Flooding Simulator

Abstract

Summary A fully implicit, parallel, compositional reservoir simulator has been developed that includes both a cubic equation of state model for the hydrocarbon phase behavior and Hand's rule for the surfactant/oil/brine phase behavior. The aqueous species in the chemical model include surfactant, polymer, and salt. The physical property models include surfactant/oil/brine phase behavior, interfacial tension, viscosity, adsorption, and relative permeability as a function of trapping number. The fully implicit simulation results were validated by comparison with results from our IMPEC chemical flooding simulator (UTCHEM). The results indicate that the simulator scales well using clusters of workstations. Also, simulation results from parallel runs are identical to those using a single processor. Field-scale surfactant/polymer flood simulations were successfully performed with over 1,000,000 gridblocks using multiple processors. Introduction Chemical flooding is a method to improve oil recovery that involves the injection of a solution of surfactant and polymer followed by a polymer solution. The surfactant causes the mobilization of oil by decreasing interfacial tension, whereas the polymer increases the sweep efficiency by lowering the mobility ratio. Chemical flooding has the potential to recover a very high fraction of the remaining oil in a reservoir, but the process needs to be designed to be both cost effective and robust, which requires careful optimization. Several reservoir simulators with chemical flooding features have been developed as a tool for optimizing the design (Delshad et al. 1996; Schlumberger 2004; Computer Modeling 2004). The University of Texas chemical flooding simulator, UTCHEM (Delshad et al. 1996) is an example of a simulator that has been used for this purpose. However, because UTCHEM is an Implicit Pressure and Explicit Concentration (IMPEC) formulation and in its current form cannot run on parallel computers, realistic surfactant/polymer flooding simulations are limited to around 100,000 gridblocks because of small timestep restrictions and insufficient memory. Recently, the appropriate chemical module was added to the fully implicit, parallel, EOS compositional simulator called GPAS (General Purpose Adaptive Simulator) based on a hybrid approach (John et al. 2005). GPAS uses a cubic equation of state model for the hydrocarbon phase behavior and the parallel and object-based Fortran 95 framework for managing memory, input/output, and the necessary communication between processors (Wang et al. 1999; Parashar et al. 1997). In the hybrid approach implemented in GPAS, the material balance equations for hydrocarbon and water components are solved implicitly first. Then, the material balance equations for the aqueous components such as surfactant, polymer, and electrolytes are solved explicitly using the updated phase fluxes, saturations, and densities.

Numerical Approach for Enhanced Oil Recovery with Surfactant Flooding using STARS (CMG)

“…Since the 1970's publications of articles based on different simulation methods in chemical flooding has been done. Some of the articles include a onedimensional and compositional chemical flooding simulator to study additional oil recovery due to different conditions (Pope GA, 1978), study of chemical flooding using an economical model simulator (AJ, 1996), simulator for compositional chemical flooding for surfactant polymer flooding (Han C, 2007) and a vital one in surfactant flooding which is the surfactant phase behavior simulator (Najafabadi et al, 2012).…”

Efficient Scheme for Chemical Flooding Simulation

“…The simulation is performed using a fully implicit solver [8] except for the polymer or tracer. These components are either resolved explicitly with CFL criterion about 1 or implicitly with CFL criterion about 1 or 10.…”

“…This scheme is robust and allows to pass through 10 times the explicit CFL criterion (8). The numerical diffusion is not negligible but does not modify the accuracy of the results: front location, mass conservation, cumulative production.…”

“…Initially, because of the complexity of the physical system, numerical methods are based on fractional step techniques. Pressure and eventually saturation are solved implicitly and then chemical components are deduced using explicit numerical schemes [7][8][9]. This technique leads to severe time step restriction with polymers and numerical simulation at field scale becomes time consuming.…”

Impact of Rock Heterogeneity on Interactions of Microbial-Enhanced Oil Recovery Processes

“…The UTCHEM simulator (Delshad et al 1996) used a simple rational function of the trapping number (Jin 1995;Pennell et al 1996) to compute the residual saturation. Han et al (2007Han et al ( , 2009) developed implicit parallel reservoir simulators with surfactant flooding features. No mathematical model of MEOR has reported details of residual oil saturation reductions during the EOR processes or discussed how biogeochemical parameters can affect residual oil saturation distributions in heterogeneous porous media.…”