SPE Reservoir Engineering, Vol.11, No.4, 268-272, 1996
Predicting well-stimulation results in a gas-storage field in the absence of reservoir data with neural networks
Selection or candidate wells for stimulation treatment to increase their productivity is a challenging task, A systematic approach that uses a three-layer backpropagation neural network, introduced in this paper, assists engineers in predicting post-stimulation well performance to select candidate wells for stimulation treatment. This approach can also be used to optimize the stimulation design parameters. Unlike conventional simulators that are based on mathematical modeling of the fracturing process, the process introduced in this paper uses no specific mathematical model, As a result, access to explicit reservoir data, such as porosity, permeability-thickness, and stress profile, is not essential. This is a major advantage over conventional hydraulic fracturing simulators, which can translate to considerable savings because it eliminates the need for expensive data collection. The application of this methodology to a gas-storage field is presented in this paper. The developed neural network can predict the postfracture well deliverability with approximately 95% accuracy. These results were achieved in the absence of reservoir data (permeability, porosity, thickness, and stress profiles) that makes conventional fracture simulation impossible. This process is currently being used to select candidate wells for future stimulation treatment in the aforementioned field.