Carbonate formations are highly heterogeneous, posing challenges like unpredictable acid distribution, suboptimal treatment designs, and environmental concerns. This session will explore how theoretical modelling, combined with digital technologies, machine learning (ML), and artificial intelligence (AI), will transform carbonate acidising. It will discuss lab testing and research supporting new technology applications.

Presentations:

1030-1100    Laboratory Large-Scale Radial Acidizing Experiments to Advance Carbonate Matrix Stimulation Design
                     Ivan Yakimchuk, Research Program Manager, SLB Dhahran Carbonate Research

1100-1130    Integrated Workflow to Evaluate Completion and Stimulation Efficiency in a Carbonate Reservoir  
                    Rachit Vijay, Advance Completion Engineer, ExxonMobil

1130-1200   Core Calibrated Carbonate Acidizing Design: Case Study for Optimization Using Single Phase Retarded Acid and Degradable Diverter  
                    Marco Colombo, Production Optimization Engineer, Eni SpA