Cuong Thanh Quy Dang

Modeling Low Salinity Waterflooding: Ion Exchange, Geochemistry and Wettability Alteration

Low salinity waterflood (LSW) has become an attractive enhanced oil recovery (EOR) method as it shows more advantages than conventional chemical EOR methods in terms of chemical costs, environmental impact, and field process implementation. Extensive laboratory studies in the past two decades have proposed several porescale mechanisms of oil displacement during LSW flooding, which are still open for discussion. However, the capability of reservoir simulators to model accurately this process is very limited. This paper provides a critical review of the state of the art in research and field applications of LSW. The focus is on a widely agreed mechanism that is the wettability alteration from preferential oil wetness to water wetness of formation rock surfaces. Ion exchange and geochemical reactions have been experimentally found to be important in oil mobilization due to enhanced water spreading at low salinity. To evaluate the significance of this surface wetting mechanism, a comprehensive ion exchange model with geochemical processes has been developed and coupled to the multi-phase multi-component flow equations in an equation-of-state compositional simulator. This new model captures most of the important physical and chemical phenomena that occur in LSW, including intra-aqueous reactions, mineral dissolution/precipitation, ion exchange and wettability alteration. The proposed LSW model is tested using the low-salinity core-flood experiments reported by Fjelde et al. (2012) for a North Sea reservoir and the low-salinity and high-salinity heterogeneous core-flood experiments by Rivet (2009) for a Texas reservoir. Excellent agreements between the model and the experiments in terms of effluent ion concentrations, effluent pH, and oil recovery were achieved. In addition, the model was also proved to be highly comparable with the ion-exchange model of the geochemistry software PHREEQC for both low salinity and high salinity (Appelo, 1994). Important observations in laboratory and field tests such as local pH increase, decrease in divalent effluent concentration, mineralogy contributions, and the influence of connate water and injected brine compositions can be reproduced with the proposed LSW model. Built in a robust reservoir simulator, it serves as a powerful tool for LSW design and the interpretation of process performance in field tests. Introduction Low salinity waterflooding (LSW) is an emerging EOR technique in which the salinity of the injected water is controlled to improve oil displacement efficiency without a significant loss of infectivity due to clay swelling. In particular, the presence of clay minerals is a favorable condition for the high efficiency of this process. This recovery concept is quite attractive as 50% of the world’s conventional petroleum reservoirs are found in sandstones that commonly contain clay minerals. It has been experimentally found that changes in the injected brine composition can improve waterflood performance by up to 38% (Larger et al. 2004, Web et al. 2004), leading to a new concept of optimal injection brine composition for water flood. In the 1990s, Jadhunandan and Morrow (1995) and Yildiz and Morrow (1996) reported the influence of brine composition on oil recovery, which identified a possibility to improve waterfloods with optimized injection brine formulation. Numerous laboratory experiments (Tang and Morrow, 1997; Morrow

A New Approach for Optimization and Uncertainty Assessment of Surfactant-Polymer Flooding

Surfactant-Polymer (SP) flooding has become an attractive Enhanced Oil Recovery (EOR) method. Defining chemical concentrations, chemical types and an injection schedule, according to geological features of a reservoir and well pattern, is key to making decisions for reservoir management. In this paper, we introduce an innovative approach for EOR optimization under geological uncertainty by integrating a reservoir geological property modelling and a robust optimizer. Multiple reservoir realizations are generated automatically by geology-driven modeling software and sent directly to an optimizer to analyze the effect of single or multi-parameters on objective functions such as cumulative oil production and net present value (NPV). Clay minerals play an important role in chemical flooding, but it is rarely included in the reservoir simulation. In this study, the distribution and proportion of clay are investigated in terms of facies and its relationship with porosity and permeability for a sandstone reservoir. Different facies and petrophysical properties are geostatiscally generated in a geologic manner that significantly improves the quality of history matching and optimization processes. It is found that SP flooding has the highest oil recovery factor in comparison with waterflooding, polymer flooding and surfactant flooding, and it demonstrates good performances even in high clay content reservoirs. The optimal formulation of SP and polymer slugs and injection schedule were proposed. The effect of clay content in cumulative oil and NPV were addressed, in which the more clay content is the lower NPVs obtain. A comprehensive geological uncertainty analysis has been performed for: (1) facies distribution only; (2) facies distribution and proportion. The results indicated that NPV uncertainty is less than 2.25% for (1) and about 4.18% to 5.68% for (2). The proposed optimization approach could be effectively applied to tertiary EOR techniques in various reservoir conditions under geological uncertainty. By integrating geological software, reservoir simulator and robust optimizer, it serves as a powerful tool for design and optimization of these processes. SP flooding is definitely a complicated process, therefore, an innovative modeling and optimization approach for SP flooding described in this paper is needed to improve the prediction of process performance.

Effective Factors on Viscosity of ASP Solutions: Experiments and Simulations

This article presents the preliminary experiments, which were studied to evaluate the viscosity of alkaline/surfactant/polymer solution before doing phase behavior experiments. The results of the experiments provide the effective trend of parameters on viscosity. Moreover, the design of the experiment model was conducted by using Minitab software in order to determine the optimum concentration of each chemical in alkaline/surfactant/polymer solution, which has viscosity higher than oil viscosity. Importantly, the results of the model showed the interaction among effective factors and, finally, recommended more accurate concentration of each component. Consequently, these solutions can push oil bank in the front leading to increase oil recovery.