Development of intelligent model for assisted gas production history matching / Muhammad Ezzat Ibrahim Saafan ; Supervised Eissa Mohamed Shokir

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Metallurgical Engineering

Production data analysis is used for predicting initial gas in place, future production and reservoir parameters such as permeability and skin factor. The process of matching the production history using production data analysis is highly subjected to human errors and usually the production data doesn{u2019}t exactly fit a unique curve. Moreover, methods used in commercial softwares didn{u2019}t account for non - darcy flow effect. In this work, an integrated intelligent model has been developed to obtain the best match of production history and predict future performance of gas wells under existing conditions and altered conditions for volumetric dry gas reservoirs. This intelligent model accounts for non-Darcy flow in unsteady state and pseudo-steady state flow in the reservoir and the need for manual matching was eliminated. A hybrid intelligent algorithm, genetic algorithm (GA) and steepest ascent hill climbing, was implemented in the developed intelligent model to obtain the best match of production history. The developed model was validated by using two cases. The results from both cases indicate that the new developed intelligent model is powerful tool for obtaining the best match of production history and predicting initial gas in place and reservoir parameters

Issued also as CD