AI applied to evaluate waterflood response, gas behind pipe, and imbibition stimulation treatments

The neural network architectures were designed using a trial-and-error technique. Initially, a constructive design was employed by adding complexity to the architecture in terms of increasing the number of input variables as well as the number of hidden layers and nodes. A technique based on conventional statistical parameters was developed to numerically describe the patterns observed in log crossplots. These numerical descriptions were then prioritized and used as neural network inputs to be correlated with known production response.