Design of neural networks using genetic algorithm for the permeability estimation of the reservoir

Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. Attempts have been made to utilize artificial neural networks (ANNs) for identification of the relationship which may exist between the well log data and core permeability. Despite of the wide range of applications and flexibility of ANNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of neural networks is still strongly dependent upon the designer's experience. This is an obvious barrier to the wider applications of neural network. To mitigate this problem, a new method for the auto-design of neural networks was used, based on genetic algorithm (GA). The new proposed method was evaluated by a case study in South Pars gas field in Persian Gulf. Design of topology and parameters of the neural networks as decision variables was done first by trial and error, and then using genetic algorithms in order to improve the effectiveness of forecasting when ANN is applied to a permeability predicting problem from well logs.