Application of ARX neural networks to model the Rate of Penetration of petroleum wells drilling
Nenhuma Miniatura disponível
Data
2006-01-01
Autores
Fonseca, Tiago C.
Mendes, Jose Ricardo P.
Serapiao, Adriane B. S. [UNESP]
Guilherme, Ivan R. [UNESP]
Kovalerchuk, B.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Acta Press Anaheim
Resumo
Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.
Descrição
Palavras-chave
Neural Networks, ARX model, petroleum wells drilling, Rate of Penetration, and drilling performance
Como citar
Proceedings Of The Second Iasted International Conference On Computational Intelligence. Anaheim: Acta Press Anaheim, p. 152-+, 2006.