Recurrent neural network model for bottomhole pressure and temperature in stepdown analysis

A system for fracturing a formation, the system comprising:

• a downhole tool having a plurality of sensors thereon configured to acquire real-time fracturing data from a well bore;
• a processor in data communication with the downhole tool;
• and
• a memory device coupled to the processor, the memory device containing a set of instructions that, when executed by the processor, cause the processor to:
• acquire the real-time fracturing data from the well bore during fracturing operation;
• process the real-time fracturing data using a recurrent neural network trained using historical data from analogous wells;
• determine a real-time response variable prediction using the processed real-time fracturing data;
• estimate a total number of open perforations in the well bore and tortuosity friction in the well bore based on the determined real-time response variable;
• and
• adjust fracturing parameters for the fracturing operation in real-time based on the real-time response variable prediction, the estimated total number of open perforations in the well bore, and the estimated tortuosity friction in the well bore.