Abstract

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Prediction formation fracture pressure is an essential task in designing safer drilling operations and economical well planning. The errors in formulas, correlations which using to predict formation fracture pressure can lead several serious problems such as lost circulation and kick, even blowout. Accurate formation fracture pressure prediction plays an important role in controls, operations and stimulations. Fracture pressure depends on several factors including magnitude of overburden stress, formation stress, formation pore pressure, depth, Poisson’s ratio, bulk modulus, Young modulus, etc. Any prediction methods should incorporate most of the above factors for a realistic prediction of the fracture pressure.

This paper presents an artificial neural network model with depth, Poisson’s ratio, pore pressure and overburden stress in Nam Con Son basin or borehole geophysicial logs in Cuu Long basin as input data to predict formation fracture pressure. The results obtained from the model are compared with those obtained from correlations. The comparison shows that the method is promising and under some circumstances it is superior to the available techniques.