Can Simulation Models Validated with Waterflooding Data Reliably Predict Three Phase Flow Processes of Gas and WAG Flooding?
- Amal Saeed Al-Harbi (Zakum Development Co.) | Shawket G. Ghedan (Computer Modelling Group Ltd.) | Kenneth M. Brantferger (ZADCO Petroleum Co) | Gary Kompanik (ZADCO)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Conference and Exhibition, 11-14 November , Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 4.1.5 Processing Equipment, 5.5.11 Formation Testing (e.g., Wireline, LWD), 5.4.2 Gas Injection Methods, 5.1.2 Faults and Fracture Characterisation, 5.5.2 Core Analysis, 2.2.2 Perforating, 5.5.8 History Matching, 1.2.3 Rock properties, 5.5.3 Scaling Methods, 5.1.5 Geologic Modeling, 4.1.2 Separation and Treating, 4.1.4 Gas Processing, 5.8.7 Carbonate Reservoir, 5.6.2 Core Analysis, 5.4 Enhanced Recovery, 5.3.2 Multiphase Flow, 5.2.2 Fluid Modeling, Equations of State, 5.4.1 Waterflooding, 5.2.1 Phase Behavior and PVT Measurements, 3 Production and Well Operations, 4.3.4 Scale, 5.1 Reservoir Characterisation
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Several giant carbonate reservoirs have undergone decades of waterflooding, and are now transitioning to EOR recovery processes. Simulation models that were calibrated via history matching while undergoing a waterflood (i.e.two phase flow
performance) are utilized now to predict three phase flow performance encountered with EOR processes. How reliable are these predictions? Are they accurate enough to be used for business decisions?
In this work, validity and reliability of simulation models, that has been history matched by two-phase flow processes of water flooding, to predict the performance of three-phase flow of WAG processes was assessed.
To accomplish study objective, fine grid of two 5-spot sectors model was built and then upscaled. Upscaled model was then history matched to the results obtained from the fine model using water flooding data and utilizing pseudo functions data. The
resulted cases as well as the fine model were then taken to prediction to estimate the performance of three-phase flow of gas and WAG processes. Results of fine and coarse models were then analyzed and compared to draw conclusions on the
reliability of the coarse models to match the predicted results of the reference model of the fine simulation model
Oil reservoirs are very complex systems with flow properties varying from the pore to reservoir scale. To simulate fluid flow in the reservoir, there are many uncertainties ranging from the spatial distribution of basic rock properties to the quantification
and impact of rock/fluid SCAL models of wettability and associated hysteresis. The process of history matching attempts to reasonably reproduce the past field performance by fine tuning some or all of these uncertain parameters while reasonably
maintaining their physical nature in the simulation model. It is well known that well history matched simulation models are more trustworthy in predicting future reservoir performance. Errors in these future performance predictions may be introduced,
however, when future development plan include processes that were not experienced historically.
The main purpose for using pseudo functions is to reduce the number of grid cells of reservoir models, trying to reproduce the behaviour of the fine scale system with coarser models. More specific, pseudo functions have been used historically to history
match the fine grid models into one representative upscaled model. This model (the upscaled one) should be used for further field development which employs the prediction of reservoir performance under different depletion and operating scenarios.
Most of the found literature was focused on simplifying multi-dimensional (2D) or (3D) systems to 1-dimensional (1D) models 1,2,3,4. Very limited number of grid blocks where allowed by the available computers at that time. The use of pseudo
relative permeabilities and capillary pressures was one way of decreasing grid dimensions into a more tractable level with minimal loss of simulation accuracy.
Another reason was to account for numerical dispersion that occurs through upscaling process. The upscaled relative permeabilities (pseudos) can compensate for the increase in numerical dispersion as the grid is coarsened 2,5,6.
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