Plugging Characteristics and Evaluation Predicting Models by Controllable Self-Aggregation Nanoparticles in Pore Throat Microcapillaries.

ABSTRACT

Injecting nanoparticle profile agents into low-permeability heterogeneous reservoirs to plugging water breakthrough channels is a widely used technical method to enhance oil recovery. However, insufficient research on the plugging characteristics and prediction models of nanoparticle profile agents in the pore throat has led to a poor profile control effect, short profile control action time, and poor injection performance in the actual reservoir. This study uses controllable self-aggregation nanoparticles with a diameter of 500 nm and different concentrations as profile control agents. Microcapillaries of different diameter sizes were used to simulate the pore throat structure and flow space of oil reservoirs. Based on a large number of cross-physical simulation experimental data, the plugging characteristics of controllable self-aggregation nanoparticles in the pore throat were analyzed. Gray correlation analysis (GRA) and gene expression programming algorithm (GEP) analysis were used to determine the key factors affecting the resistance coefficient and plugging rate of profile control agents. With the help of GeneXproTools, the evolutionary algebra 3000 was selected to obtain the calculation formula and prediction model of the resistance coefficient and plugging rate of the injected nanoparticles in the pore throat. The experimental results show that the controllable self-aggregation nanoparticles will achieve effective plugging when the pressure gradient is greater than 100 MPa/m in the pore throat, and when the injection pressure gradient is 20-100 MPa/m, the nanoparticle solution will be in the aggregation to breakthrough state in the pore throat. The main factors affecting the injectability of nanoparticles, from strong to weak, are as follows injection speed > pore length > concentration > pore diameter. The main factors affecting the plugging rate of nanoparticles, from strong to weak, are as follows pore length > injection speed > concentration > pore diameter. The prediction model can effectively predict the injection performance and plugging performance of controllable self-aggregating nanoparticles in the pore throat. The prediction accuracy of the injection resistance coefficient is 0.91, and the accuracy of the plugging rate is 0.93 in the prediction model.