Predictive Model for Silica Nano-Dispersion Gelation to Enhance Conformance Control in Oilfields

Excessive water production leads to increased treatment costs, elevated carbon emissions, and diminished operational efficiency. To mitigate these adverse effects, this study investigates a novel conformance control method employing a nanofluid system. This innovative approach is designed to effectively intervene in high-permeability zones, wormholes, and fractured reservoirs, thereby optimizing water management practices. Nanofluids incorporating colloidal silica dispersions were selected for their enhanced reservoir penetration capabilities, attributed to their high silica concentration and low viscosity. To elucidate the gelation process of the precursor solution activated by sodium silicate, rigorous rheological characterization was undertaken under diverse conditions, including activator concentration, temperature, water dilution, metal ion presence, and applied shear rate. The resulting data facilitated the development of an analytical model to predict viscosity evolution over time. This model was subsequently validated through a 1D flow experiment in a laboratory-simulated porous medium and subsequently applied to an upscaled numerical simulation of a reservoir featuring a high-permeability layer. A critical parameter, termed the "high viscosity threshold time" (HVTT), was defined to characterize the transition point at which the precursor solution attains a high viscosity state. Kinetic analysis revealed a strong Arrhenius-type dependence of gelation on temperature. To account for the influence of activator concentration and precursor dilution, a modified Arrhenius equation was proposed, providing a robust predictive tool for HVTT under diverse treatment conditions. Furthermore, an exponential model was developed to approximate viscosity evolution over time for specified target viscosity and HVTT values. By integrating these parameters, the model can effectively forecast viscosity evolution throughout the treatment process, offering a predictive framework for the rheological behavior of the system. The numerical simulations successfully validated the analytical model, solidifying its applicability in reservoir scenarios and demonstrating its potential for predictive utilization and optimization in field applications. The integration of this predictive model into field deployment workflows is paramount. Such integration facilitates in-silico evaluation of conformance control strategies, enabling the optimization of treatment efficacy under a wide range of downhole conditions. The synergistic combination of experimental and numerical methodologies presents a novel approach for optimizing and predicting the success of conformance control treatments in future field applications.