In the quest to optimize oil and gas extraction from naturally fractured reservoirs, a novel approach developed by Ali Heshmati of the Department of Mechanical Engineering at Sharif University of Technology in Tehran, Iran, is making waves. Published in the journal ‘مهندسی عمران شریف’ (translated as ‘Sharif Civil Engineering’), Heshmati’s research integrates advanced image processing techniques with sophisticated simulation methods to enhance the understanding and prediction of fluid flow in complex geological structures.
The study introduces an innovative two-step process that begins with converting aerial images of surface outcrops into detailed fracture network models. Using Python libraries, Heshmati and his team meticulously analyze these images, adjusting parameters like resolution and edge detection to refine the fracture representations. “The key here is to capture the intricate details of the fracture network accurately,” Heshmati explains. “This precision is crucial for reliable simulations.”
Once the fracture model is established, the team employs the Embedded Discrete Fracture Method (EDFM) within the MATLAB Reservoir Simulation Toolbox (MRST) to simulate two-phase flow dynamics. This method accounts for all types of intersections and fluid exchanges between fractures and the rock matrix, providing a comprehensive analysis of fluid behavior within the reservoir.
The findings reveal that rock permeability has a more significant impact on fluid flow than fracture aperture or permeability. For instance, a modest increase in matrix permeability from 1 to 20 millidarcy resulted in a substantial 22 percent increase in oil production, while a similar adjustment in fracture aperture yielded only a 3 percent change. “This insight could revolutionize how we approach reservoir management and optimization,” Heshmati notes.
The implications for the energy sector are profound. By leveraging aerial imaging and advanced simulation techniques, oil and gas companies can gain a deeper understanding of reservoir dynamics, leading to more efficient extraction processes and reduced operational costs. “This research not only enhances our predictive capabilities but also paves the way for more sustainable and economically viable extraction methods,” Heshmati adds.
As the industry continues to seek innovative solutions to meet global energy demands, Heshmati’s work offers a promising path forward. By bridging the gap between geological observations and computational modeling, this integrated approach could shape the future of reservoir engineering, driving advancements that benefit both the environment and the economy.