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Scaling of counter-current imbibition recovery curves using artificial neural networks

Jafari, I ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1088/1742-2140/aa9fe3
  3. Publisher: Institute of Physics Publishing , 2018
  4. Abstract:
  5. Scaling imbibition curves are of great importance in the characterization and simulation of oil production from naturally fractured reservoirs. Different parameters such as matrix porosity and permeability, oil and water viscosities, matrix dimensions, and oil/water interfacial tensions have an effective on the imbibition process. Studies on the scaling imbibition curves along with the consideration of different assumptions have resulted in various scaling equations. In this work, using an artificial neural network (ANN) method, a novel technique is presented for scaling imbibition recovery curves, which can be used for scaling the experimental and field-scale imbibition cases. The imbibition recovery curves for training and testing the neural network were gathered through the simulation of different scenarios using a commercial reservoir simulator. In this ANN-based method, six parameters were assumed to have an effect on the imbibition process and were considered as the inputs for training the network. Using the 'Bayesian regularization' training algorithm, the network was trained and tested. Training and testing phases showed superior results in comparison with the other scaling methods. It is concluded that using the new technique is useful for scaling imbibition recovery curves, especially for complex cases, for which the common scaling methods are not designed. © 2018 Sinopec Geophysical Research Institute
  6. Keywords:
  7. Artificial neural network ; Bayesian regularization ; Imbibition ; Recovery curve ; Scaling ; Algorithm ; Artificial neural network ; Bayesian analysis ; Enhanced oil recovery ; Hydrocarbon reservoir ; Imbibition ; Oil production
  8. Source: Journal of Geophysics and Engineering ; Volume 15, Issue 3 , 2018 , Pages 1062-1070 ; 17422132 (ISSN)
  9. URL: https://academic.oup.com/jge/article/15/3/1062/5203205