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

Area of Science:

• Petroleum Engineering
• Materials Science
• Chemical Engineering

Background:

• Nanoparticle profile agents are crucial for enhancing oil recovery by plugging water breakthrough channels in low-permeability heterogeneous reservoirs.
• Existing research lacks comprehensive understanding of nanoparticle plugging characteristics and predictive models, leading to suboptimal performance in actual reservoir applications.
• This limits the effectiveness and duration of profile control, hindering efficient oil extraction.

Purpose of the Study:

• To investigate the plugging characteristics of controllable self-aggregation nanoparticles in simulated reservoir pore throats.
• To develop accurate prediction models for nanoparticle injection and plugging performance.
• To identify key factors influencing nanoparticle behavior in porous media for optimized oil recovery strategies.

Main Methods:

• Utilized controllable self-aggregation nanoparticles (500 nm diameter) at various concentrations.
• Employed microcapillaries of different diameters to simulate reservoir pore throat structures.
• Applied Gray Correlation Analysis (GRA) and Gene Expression Programming (GEP) to develop predictive models using experimental data.

Main Results:

• Effective plugging of nanoparticle agents occurs at pressure gradients > 100 MPa/m.
• Nanoparticle solutions exhibit aggregation-to-breakthrough states within 20-100 MPa/m injection pressure gradients.
• Key factors influencing injectability: injection speed > pore length > concentration > pore diameter.
• Key factors influencing plugging rate: pore length > injection speed > concentration > pore diameter.

Conclusions:

• Developed prediction models for nanoparticle injection resistance coefficient (accuracy 0.91) and plugging rate (accuracy 0.93).
• These models accurately forecast the performance of self-aggregating nanoparticles in pore throats.
• Findings provide a basis for optimizing nanoparticle-based enhanced oil recovery techniques.