The Impact of Ion Exchange and Surfactant Partitioning on ASP Modeling, A Brown Offshore Field
- Nader Ghadami (Petronas) | Raj Deo Tewari (Petronas) | Maria Zarubinska (Shell) | Eghbal Motaei (Petronas)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference Asia, 20-23 March, Kuala Lumpur, Malaysia
- Publication Date
- Document Type
- Conference Paper
- 2018. Offshore Technology Conference
- 2.6 Acidizing, 5.5.3 Scaling Methods, 2.4 Hydraulic Fracturing, 1.6.9 Coring, Fishing, 2 Well completion, 5.5.2 Core Analysis, 5.5.8 History Matching, 5.4 Improved and Enhanced Recovery, 5 Reservoir Desciption & Dynamics, 5.5 Reservoir Simulation, 5.3.2 Multiphase Flow, 5.3.6 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 1.6 Drilling Operations, 5.2.1 Phase Behavior and PVT Measurements, 2.5.2 Fracturing Materials (Fluids, Proppant), 5.2 Fluid Characterization
- Surfactant Partitioning, Upscaling, Coreflood History Matching, Alkaline Surfactant Polymer, Ion Exchange
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In order to design and analyse Alkaline Surfactant Polymer (ASP) pilots and generate reliable field forecasts, a robust scalable modeling workflow for the ASP process is required. Accurate modeling of an ASP flood requires detailed representation of the geochemistry and the saponification process, if natural acids are present. The objective of this study is to extend the existing models of ion exchange and surfactant partitioning between phases to improve the quality of the model.
Geochemistry and saponification affect the propagation of the injected chemicals. This in turn determine the chemical phase behaviour and hence the effectiveness of the ASP process. A starting point of such a workflow is to carry out ASP coreflood tests and history matching (HM) using numerical models. This allows validation of the models and generates a set of chemical flood parameters that can be used for forecasts. The next step is upscaling from lab to field. The presence of (geo)-chemistry in ASP model improves significantly the quality of core HM especially for produced chemicals, breakthrough time and their profiles shape.
The addition of surfactant partitioning between the oleic and the aqueous phases based on salinity of the system as well as propagated distance (time) improves understanding of the required surfactant concentration. The partitioning of surfactant is important for coreflood matching of native cores as they tend to have more clays and minerals that affect ASP phase behaviour. The upscaling of the HM coreflood was conducted in two steps. First step the coreflood was scaled up with the distance between injector–producer pair as the scaling parameter. Second step was the application of the scaled up injection rates, residual saturations, etc. to the full field model. Sensitivity study for parameters such as grid size, well distance, ASP slug size, and rate of surfactant partitioning was performed. It was found that grid size of 50ft was optimum for ASP modeling. The higher rate of surfactant partitioning resulted to lower recovery. The optimal well distance was determined as 700ft for optimization of oil recovery. The reduction of ASP slug size from 0.5PV to 0.3PV leads to the reduction in oil recovery by 2-3%.
Usually chemical reactions accompanied ASP process are left out of the model due to increase in complexity as well as longer computational time. However, their addition as well as presence of surfactant partitioning between the oleic and the aqueous phases makes ASP models more realistic and it results in significant improvement to coreflood HM quality and prediction of ASP process.
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