CO2 foam as a fracturing fluid for unconventional reservoir has been of huge interest due to its potential in solving various challenges related to conventional water-based fracturing. The rheological property of CO2 foam is a key factor controlling the efficiency of fracturing process and it is strongly influenced by different parameters such as foam quality, temperature, pressure and shear rate. The quantification of these parameters under reservoir conditions leads to the design of optimum injection strategy. However, the traditional modeling approaches are unable to provide fast and accurate prediction while considering combined effect of all these parameters. Here, we proposed a data driven approach based on supervised deep learning to estimate rheological property of CO2 foam as a function of foam quality, temperature, pressure, and shear rate. We exploit deep neural networks (DNNs) that are trained to learn the complex nonlinear aspects of the data. For the data generation, we performed a series of experiments for CO2 foams by varying different process variables. CO2 foams at different qualities were generated using conventional surfactant in a flow loop system and foam viscosity measurements were performed at HPHT under wide range of shear rate. The architecture of DNN was optimized to accurately estimate the foam apparent viscosity for given foam quality, temperature, pressure, and shear rate. The predictive capability of designed network is found to be significantly high, analyzed by regression coefficient approaching unity, low mean squared error, and low average absolute relative deviation (≪ 2.5%). The designed neural network allows robust and accurate prediction of foam apparent viscosity at different foam qualities under various reservoir condition, which demonstrates its practicality for CO2 foam projects for fracturing unconventional reservoirs.

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