BSEO-Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices.

Area of Science:

• Petroleum Engineering
• Chemical Engineering
• Materials Science

Background:

• Microfluidic models are vital for studying enhanced oil recovery (EOR) by simulating fluid-rock interactions.
• Lab-on-a-chip devices offer cost-effectiveness and efficiency in data extraction for EOR research.
• Efficient data management is critical for analyzing complex datasets from microfluidic EOR experiments.

Purpose of the Study:

• To introduce a semiautomatic method for calculating oil recovery in microfluidic experiments.
• To evaluate the performance of the background subtraction (BSEO) method against established techniques.
• To demonstrate the efficiency and accuracy of BSEO for polymeric nanofluid flooding.

Main Methods:

• Developed a semiautomatic oil recovery calculation method based on background subtraction (BSEO).
• Generated foreground binary masks to identify injected fluids within microfluidic micromodels.
• Compared BSEO performance against manual segmentation (ground truth) and Fiji-ImageJ software.

Main Results:

• The BSEO method demonstrated high accuracy with low Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
• A coefficient of determination (R²) of 98% confirmed a strong correlation between BSEO predictions and experimental outcomes.
• BSEO proved to be a robust alternative to existing methods for oil recovery quantification.

Conclusions:

• The BSEO method is a viable and robust technique for calculating oil recovery in microfluidic flooding experiments.
• BSEO offers significant advantages in reduced computational resource usage and faster processing times.
• This method supports efficient data analysis for enhanced oil recovery research using microfluidic platforms.