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Data-driven recurrent neural network model to predict the rate of penetration

  • Published:2021-09 Issue: Volume:7 Page:100047
  • ISSN:2666-2604
  • Container-title:Upstream Oil and Gas Technology
  • language:en
  • Short-container-title:Upstream Oil and Gas Technology

Author:

Alkinani Husam H.,Al-Hameedi Abo Taleb T.,Dunn-Norman Shari

Publisher

Elsevier BV

Reference74 articles.

1. Bayesian prediction of TBM penetration rate in rock mass;Adoko;Eng. Geol.,2017

2. Computational intelligence based prediction of drilling rate of penetration: a comparative study;Ahmed;J. Petrol. Sci. Eng.,2019

3. How to estimate the maximum achievable drilling rate without jeopardizing safety;Akgun;Soc. Petrol. Eng.,2002

4. Drilling rate at the technical limit;Akgun;Int. J. Petrol. Sci. Technol.,2007

5. The effect of drilling mud density on penetration rate;Akpabio;International Research Journal of Engineering and Technology (IRJET),2015
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