SSelecting the "Right" ASP Model by History Matching Coreflood Experiments
- Volodymyr Karpan (Shell Exploration & Production) | Rouhollah Farajzadeh (Shell Intl E&P BV) | Maria Zarubinska (Shell Exploration & Production) | Harm Dijk (Shell Intl E&P Co) | Tsuyoshi Matsuura (Shell Exploration & Production) | Martin Stoll (Shell E&P International Ltd)
- Document ID
- Society of Petroleum Engineers
- SPE Enhanced Oil Recovery Conference, 19-21 July, Kuala Lumpur, Malaysia
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.7.2 Recovery Factors, 4.3.4 Scale, 5.3.2 Multiphase Flow, 2.5.2 Fracturing Materials (Fluids, Proppant), 5.5.8 History Matching, 5.4.7 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 5.2.1 Phase Behavior and PVT Measurements, 5.4.1 Waterflooding
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In order to design and analyze Alkaline Surfactant Polymer (ASP) pilots and to generate reliable ASP field forecasts a robust scalable modeling workflow for the ASP process is required. A starting point of such a workflow is to carry out ASP coreflood tests and history match those using numerical models. This allows validation of the models and generates a set of chemical flood parameters that can be used for field-scale simulation forecasts.
It is well established that lowering of interfacial tension due to maximum of in-situ generated soap with injected surfactant and improved mobility control due to the polymer play a crucial role in the ASP process. Therefore, all models for the ASP process take into account these mechanisms in one way or the other. However, ASP models can differ in the detail in which (geo-) chemical reactions and the phase behavior are addressed. Inclusion of the more details into the numerical model could result in better understanding and more accurate prediction, but it comes at a price, viz., it requires more measured input data and increases computational time. Thus, depending on the accuracy requirements, available experimental data and time the modeling of ASP flood can be performed using different simulation approaches.
This paper describes several modeling approaches for ASP. We start with a brief description of these methods and their input requirements. Then we compare the ASP coreflood simulation results demonstrating the advantages and disadvantages of presented approaches. We also demonstrate that both ASP models can be applied at the field level by simulating an ASP flood in a sector model. Finally we give some recommendations and guidelines on how and when the proposed models should be used.
Alkaline/surfactant/polymer (ASP) flooding is an enhanced oil recovery (EOR) technique that involves the injection of a solution of surfactant, alkaline and polymer into the oil reservoir to mobilize the remaining oil. In this process the injected surfactant and the petroleum soaps generated in situ reduce the oil-water interfacial tension (IFT), improving the microscopic sweep efficiency (Nelson et al., 1984). Moreover, the macroscopic sweep efficiency is enhanced through improvement of the mobility ratio due to the injected polymer. Another important benefit of the alkali is the reduction of surfactant retention on the rock surface, allowing for the injection of smaller amounts of surfactant. Indeed, in some cases where the crude oil does not react with the alkali, the injection of alkali is recommended to prevent surface retention of expensive surfactant. As a further improvement, the addition of a co-solvent may enhance the combined solubility of the surfactant and the polymer in the injected ASP solution and reduce the viscosity of (micro-) emulsions formed when the ASP solution contacts the crude oil.
The ASP process is usually applied to tertiary floods in the drive mode. Because of the considerable costs of the chemicals associated with the ASP flooding, an ASP slug (a fraction of the reservoir pore volume) is generally injected, and then followed by a solution of a water-soluble polymer. Typical estimated incremental recoveries for ASP flooding after water flood are of the order of 10 to 20% STOIIP (Pitts et al., 2006, Qu et al., 1998, Vargo et al., 2000).
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