Abstract
Hydraulic fracturing is a typical and vital technique applied in shale gas reservoir development. Numerical simulation used to be a common tool to optimize the parameters in hydraulic fracturing design determining the stage numbers, injection pressure, proppant amount, etc. However, the current understanding of shale gas storage and transport mechanism (e.g. adsorption/desorption, diffusion) is basically adopted from the lessons learned from coal seams through past experience, which might not help an efficient numerical simulation development.
In this study, how artificial intelligence assisted data driven models assist the hydraulic fracturing design in shale gas reservoir is discussed. It starts by collecting field data and generate a spatial-temporal database including reservoir characteristics, operational/production information, completion/stimulation data and other variables, Neural Network models are then developed to study the impacts of all parameters on gas production as well as perform history matching of the field history. The AI assisted model with acceptable matching of field data can be used to model different hydraulic fracturing design scenarios and provide predictions on well production.