Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields

Abstract

Accurate prediction of pore pressures has become almost essential to exploration, completion and drilling wells with higher-than-normal pore pressures. There are some limitations with the current existing pore pressure estimation methods, as they are based on empirical relations and constants which can differ from basin to basin. In this study, a feed-forward network with back-propagation algorithm and a generalised regression neural network have been developed to predict pore pressure gradient in Asmari reservoir in one of oilfields in Iran. To show the accuracy of the method, the results of the developed models have been compared with Eaton’s method.