Drilling rate of penetration prediction using artificial neural network: a case study of one of Iranian Southern oil fields
Rate of Penetration (ROP) estimation is a key parameter in drilling optimization, due to its crucial role in minimizing drilling operation costs. However, a lot numbers of unforeseen factors affect the ROP and make it a complex process and consequently difficult to predict. This paper presents an application of Artificial Neural Network (ANN) methods for estimation of ROP among drilling parameters obtained from one of Iranian southern oil fields, according to the fact that this method is useful when relationships of parameters are too complicated. The method is proposed as a more effective prognostic tool than are currently available procedures. The methodology enables drilling industry personnel to estimate the ROP not only during well planning procedure but also during drilling. A three layer feed-forward network has been selected which has the best correlation coefficient in testing the models. Simulation results show that the ANN approach is superior to the conventional methods in drilling rate prediction accuracy.
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artificial neural network; Drilling efficiency; rate of penetration; ROP Prediction; искусственные нейронные сети; прогнозирование ROP; скорость проходки; эффективность бурения References to this article (GOST) M. Monazami, A. Hashemi, M. Shahbazian. Drilling rate of penetration prediction using artificial neural network: a case study of one of Iranian Southern oil fields // Electronic scientific journal "Oil and Gas Business". 2012. №6. P.21-31. URL: http://ogbus.ru/eng/authors/MonazamiM/MonazamiM_1.pdf