Prediction of frictional pressure losses in annulus with artificial neural network modeling

Muhammed Adil Ismail, Fikret Terzi, Ammar M. Al-bayati

Abstract


Pressure loss of single phase in annular flow for non-Newtonian drilling mud have been predicted utilizing an artificial neural network (ANN). Based on statistical study, an optimization procedure was performed to choose the optimum of ANN technique. With Levenberg-Marquardt back-propagation training algorithm, six variables as input and once as outcome were modeled and trained using a three-layer feed forward neural network. Three neurons, the log sigmoid transfer function in the hidden layer and the linear transfer function inside the result layer were introduced in this article. Simulated results of ANN technique are been demonstrated that there is an outstanding agreement among predicting values and measuring pressure drops in annulus for test data and training sets. Additionally, typical model used (power law model) that were applied to calculate the performance of ANN model and comparison the three wells results. Based on the analysis of the results obtained, it is shown that the ANN method outperforms other models significantly and delivers excellent predictions, the correlation coefficient (R2) of 0.9999 average absolute relative error (AARE) equal to 0.00001.

Keywords


Pressure Losses, Effective Diameter, Power Law, Friction, Hydraulic

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DOI: http://dx.doi.org/10.21533/pen.v8i3.1551

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License