From face detection to fractured reservoir characterization: Big data analytics for restimulation candidate selection

In recent years, there has been a proliferation of massive subsurface data from instrumented wells. This places significant challenges on traditional production data analysis methods for extracting useful information, in support of reservoir management and decision-making. Additionally, with increased exploration interest in unconventional shale gas reservoirs, there is a heightened need for improved techniques and technologies to enhance understanding of induced and natural fracture characteristics in the subsurface, as well as their associated impacts on fluid flow and transport. The above challenges have the potential to be addressed by developing Big Data analytic tools that focus on uncovering masked trends related to fracture properties from large volumes of subsurface data, through the application of pattern recognition techniques. We present a new framework for fast and robust production data classification, which is adapted from a real-time face detection algorithm. This is achieved by generalizing production data as vectorized 1-D images with pixel values indicating rate magnitudes. Using simulated shale gas production data, we train a boosted binary classification algorithm which is capable of providing probabilistic predictions. We demonstrate the viability of this approach for identifying hydraulically fractured wells which have the potential to benefit from restimulation treatment. The results show significant improvements over existing type-curve based approaches for recognizing favorable candidate wells, using solely gas rate profiles.