Application of deep learning technique to predict downhole pressure differential in eccentric annulus of ultra-deep well
Accurate prediction of downhole pressure differential (surge/swab pressure gradient) in the eccentric annulus of ultra-deep wells during tripping operation is a necessity to optimize well geometry, reduction of drilling anomalies, and prevention of hazardous drilling accidents. Therefore, a new pred...
| Main Authors: | Krishna, S., Ridha, S., Ilyas, S.U., Campbell, S., Bhan, U., Bataee, M. |
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| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.29407 / |
| Published: |
American Society of Mechanical Engineers (ASME)
2021
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117132286&doi=10.1115%2fOMAE2021-62621&partnerID=40&md5=c9678cb470a1ae5d1d981292230c4083 http://eprints.utp.edu.my/29407/ |
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| Summary: |
Accurate prediction of downhole pressure differential (surge/swab pressure gradient) in the eccentric annulus of ultra-deep wells during tripping operation is a necessity to optimize well geometry, reduction of drilling anomalies, and prevention of hazardous drilling accidents. Therefore, a new predictive model is developed to forecast surge/swab pressure gradient by using feed-forward and backpropagation deep neural networks (FFBP-DNN). A theoretical-based model is developed that follows the physical and mechanical aspects of surge/swab pressure generation in eccentric annulus during tripping operation. The data generated from this model, field data, and experimental data are used to train and test the FFBP-DNN networks. The network is developed used Keras�s deep learning framework. After testing the models, the most optimal arrangement of FFBP-DNN is the ReLU algorithm as an activation function, 4-hidden layers, the learning rate of 0.003, and 2300 of training numbers. The optimum FFBP-DNN model is validated by comparing it with field data (Wells K 470 and K 480, North Sea). It shows an excellent argument between predicted data and field data with an error range of ±7.68 . Copyright © 2021 by ASME. |
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