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Application of ARX neural networks to model the rate of penetration of petroleum wells drilling
  • Universidade Estadual de Campinas (UNICAMP)
  • Universidade Estadual Paulista (UNESP)
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.
Issue Date: 
Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006, p. 152-157.
Time Duration: 
  • ARX model
  • Drilling performance
  • Neural networks
  • Petroleum wells drilling
  • Rate of penetration
  • Artificial intelligence
  • Drilling
  • Forecasting
  • Image classification
  • Intelligent control
  • Offshore oil fields
  • Offshore oil wells
  • Oil well drilling
  • Oil well production
  • Oil wells
  • Petroleum industry
  • Petroleum refineries
  • Vegetation
  • Voltage control
  • Arx models
  • Bit performances
  • Challenging problems
  • Drilling bits
  • Drilling performances
  • Dynamic Neural networks
  • Input signals
  • Modeling problems
  • Offshore fields
  • Petroleum wells
  • Price rises
  • Rate of penetrations
  • Well planning
  • Well drilling
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Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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