The recent proliferation of subsurface data from instrumented wells has created significant challenges for traditional production-data-analysis methods to extract useful information for reservoir management. This paper demonstrates the viability of a production-data-classification approach adapted from real-time face detection for identifying restimulation candidates. The approach has the potential to be used as a big-data analytic tool for long-duration production-data analysis to serve as a screening tool for selection of restimulation candidates.
Introduction
Restimulation treatments in producing shale wells have the potential to improve economic performance by increasing the conductivity of existing fractures or enhancing their contact with the formation. The influence of matrix and fracture characteristics on the success of restimulation, however, is not completely understood, which has led to uncertainty in determining favorable candidate wells.
