Using Acoustically Derived Completion Data, Machine Learning, and Economics to Evaluate Completion Effectiveness in the Bakken

Leveraging machine learning alongside the visualization of acoustically measured data enables the continuous evaluation and optimization of hydraulic fracturing treatments. By analyzing real-time completion metrics from the Bakken formation, this methodology assesses both completion effectiveness and its direct economic impact. Integrating these advanced data analytics provides a deeper understanding of subsurface fracture networks, ultimately guiding highly efficient, data-driven completion designs that maximize return on investment.