Modelagem e simulação computacional da injeção de água inteligente campos maduros em de petróleo

Abstract

Low salinity waterflooding (LSW) is an enhanced oil recovery (EOR) technique that highlights from conventional EOR methods due to some advantages such as low implementation cost, low environmental impact, and versatility of application for both sandstone and carbonate reservoirs. Since the potential of LSW was discovered, numerous studies have been conducted to investigate the mechanism responsible for the effects of LSW on enhanced oil recovery, however, due to the complexity of the physicochemical interactions between the crude/brine/rock system no consensus has been reached. Nevertheless, the variety of scenarios in which LSW effects may or may not be observed suggests that the increase in oil recovery is the result of simultaneous occurrence of several mechanisms, such as wettability alteration and fines migration, which are the most supported mechanisms in the literature. In order to investigate how these two mechanisms, contribute to the effects of LSW in increasing the recovery factor of mature oil fields, a one-dimensional multiphase flow simulator in porous media was developed to enable the study of different mechanisms and their considerations regarding the specific variables of each mechanism. The simulator development was based on three operational modules, in which the first module is responsible for simulating the injection of high salinity water. The remaining modules were aimed at simulating LSW via models based on the mechanism of wettability alteration and fines migration, respectively. With the development of the first module, it was possible to validate the selected numerical method by comparing it with the analytical solution. Then, with the help of the second module, seven models based on three different physicochemical approaches were implemented and their performance was evaluated by comparing the predicted results with experimental data. The best performing model was the MIE model, with a relative error of 4.3% and a standard deviation of 2.54. Subsequently, in order to gain additional insights into the conditions regarding the injection of low salinity water, a data analysis was performed with the results of 17 experiments, and as a result, a pseudo-scenario was elaborated with the intention of supporting the optimization of the MIE model through some sensitivity analyses. The third module implemented the fines migration model, and thereby it was possible to investigate how the energy profile of the fines can be impacted with varying salinity and some physicochemical parameters. In addition, four models were developed to predict the zeta potential of kaolinite and quartz as a function of salinity and pH. Subsequently, in the validation of the fine migration model, there was less accuracy of the model before the breakthrough, especially with the breakthrough of concentration front during LSW, with a relative error of 2.4%. Nevertheless, the results showed that the module is able to simulate new scenarios.