A New Generation Chemical Flooding Simulator
- Abraham John (U. of Texas at Austin) | Choongyong Han (U. of Texas at Austin) | Mojdeh Delshad (U. of Texas at Austin) | Gary A. Pope (U. of Texas at Austin) | Kamy Sepehrnoori (The University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2005
- Document Type
- Journal Paper
- 206 - 216
- 2005. Society of Petroleum Engineers
- 2.5.2 Fracturing Materials (Fluids, Proppant), 1.2.3 Rock properties, 1.8 Formation Damage, 2.2.2 Perforating, 4.1.5 Processing Equipment, 5.5 Reservoir Simulation, 5.2.2 Fluid Modeling, Equations of State, 5.1 Reservoir Characterisation, 5.4.7 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 4.1.2 Separation and Treating, 5.1.5 Geologic Modeling, 5.6.5 Tracers, 5.4.1 Waterflooding, 4.3.4 Scale, 5.4.2 Gas Injection Methods, 5.3.2 Multiphase Flow, 5.2.1 Phase Behavior and PVT Measurements
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Compositional reservoir simulators that are based on equation-of-state (EOS)formulations typically do not handle the modeling of aqueous phase behavior,and those that are designed for modeling chemical processes typically assumesimplified hydrocarbon phase behavior. There is a need to have a singlereservoir simulator capable of combining both approaches to benefit from theadvantages of both aqueous and hydrocarbons models. Developing and implementingfully implicit procedures for modeling both hydrocarbon and aqueous phasebehavior simultaneously is a complex process. An approach to integrate asurfactant phase behavior model into an existing fully implicit, parallel, EOScompositional simulator is presented in this paper. Physical property modelsdescribing the flow and transport of surfactant and polymer species have beenimplemented. These properties include surfactant phase behavior, interfacialtension, capillary desaturation, viscosity, adsorption, and relativepermeability as a function of trapping number. Polymer properties includeviscosity, permeability reduction, inaccessible pore volume, and adsorption.The simulation results were validated by comparison with the explicitchemical-flooding simulator UTCHEM and are shown in this paper. Test runs wereperformed with high-resolution models in a parallel environment, with resultsindicating a good scalability of the simulator.
Increased oil production using improved oil recovery processes requiresnumerical modeling of such processes to minimize the risk involved indevelopment decisions. The oil industry is requiring much more detailedanalyses with a greater demand for reservoir simulation with geological,physical, and chemical models of much more detail than the past. Reservoirsimulation has become an increasingly widespread and important tool foranalyzing and optimizing oil recovery projects.
Numerical simulation of large petroleum reservoirs with complex recoveryprocesses is computationally challenging because of the problem size anddetailed property calculations involved. This problem is compounded by thefiner resolution needed to model such processes accurately. Traditionally, suchsimulations have been performed on workstations or high-end desktop computers.These computers restrict the problem size because of their address- able memorylimit, and simulation studies of the entire project life become time-consuming.Parallel reservoir simulation, especially on low-cost, high-performancecomputing clusters, has alleviated these issues to a certain extent. Recentpublications describe the development of such approaches and emphasize thenecessity and advantages of using parallel processing. 1--4
Compositional reservoir simulators that are based on EOS formulations do nothandle the modeling of aqueous phase behavior and those that are designed forchemical-flood modeling typically assume simplified hydrocarbon phase behavior.There is need to have a single reservoir simulator capable of combining bothapproaches to benefit from the advantages of both models. The overall objectiveof this research is to develop such technology using a computational frameworkthat also allows parallel processing. The initial stage of development involvedthe formulation of a fully implicit, parallel, EOS compositional simulator. 5The description of the framework approach used for modular code development andthe application to gas injection is in Wang et al. 6
In this paper, we focus on the implementation of the chemical module to theexisting EOS simulator, its validation, and its application to large-scalechemical-flooding simulations. The formulation of the compositional model isbriefly described. The assumptions for the chemical model and its formulationare described next. We use Hand's rule 7 to describe surfactant/oil/brine TypeII(--) phase behavior. The trapping number model for relative permeability isimplemented to capture the changes in residual saturations caused by thelowered interfacial tension. The validation of the implementation against theexplicit chemical flooding simulator UTCHEM is shown. Application tolarge-scale problems and tests showing the parallel performance of thesimulator are described. The approach we used to couple the models is easy toimplement, computationally efficient, and extendable to many other interestingreservoir problems involving aqueous chemistry. With the capability of parallelprocessing, the general purpose adaptive simulator (GPAS) can now be used tosimulate chemical flooding on a larger scale than before.
|File Size||1 MB||Number of Pages||11|
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