Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)

Tripping in or out drill string/casing with a certain speed from the wellbore will result in downhole pressure surges. These surges could result in well integrity or well control problems which can be avoided if pressure imbalances are predicted before this operation engaged. To predict these pressu...

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Main Authors: Krishna, S., Ridha, S., Vasant, P.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.24791 /
Published: Springer 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085179397&doi=10.1007%2f978-981-15-3284-9_43&partnerID=40&md5=56ae14dc41a428c542910e67f71fe3c4
http://eprints.utp.edu.my/24791/
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spelling utp-eprints.247912021-08-27T06:25:06Z Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN) Krishna, S. Ridha, S. Vasant, P. Tripping in or out drill string/casing with a certain speed from the wellbore will result in downhole pressure surges. These surges could result in well integrity or well control problems which can be avoided if pressure imbalances are predicted before this operation engaged. To predict these pressure imbalances, number of analytical models have been developed but require time-consuming cumbersome numerical analysis. In this paper, an intelligent model (ANN) is developed which can predict the surge pressure under varying rheological and geometrical parameters. ANN is developed with six neurons in input layer representing six input parameters (pipe velocity, PV, YP, diameter of hole, outer diameter of pipe and mud weight) and one neuron in output layer which represents surge pressure. Now, to find the most optimum neural network structure (number of hidden layer and neurons), total 108 ANN configuration is trained and tested. Performance analysis on these configurations indicates network structure with two hidden layers including ten and 16 neurons in first and second layer, respectively, as the most optimum. Since the selected model is complex, another trained model with one hidden layer containing 14 nodes can be considered due to its satisfactory prediction result. The trained intelligent model can be utilized when tripping operation is carried out in low-pressure margin wells where repetitive calculation of surge/swab pressure is required. © Springer Nature Singapore Pte Ltd. 2020. Springer 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085179397&doi=10.1007%2f978-981-15-3284-9_43&partnerID=40&md5=56ae14dc41a428c542910e67f71fe3c4 Krishna, S. and Ridha, S. and Vasant, P. (2020) Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN). Lecture Notes in Networks and Systems, 118 . pp. 379-388. http://eprints.utp.edu.my/24791/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Tripping in or out drill string/casing with a certain speed from the wellbore will result in downhole pressure surges. These surges could result in well integrity or well control problems which can be avoided if pressure imbalances are predicted before this operation engaged. To predict these pressure imbalances, number of analytical models have been developed but require time-consuming cumbersome numerical analysis. In this paper, an intelligent model (ANN) is developed which can predict the surge pressure under varying rheological and geometrical parameters. ANN is developed with six neurons in input layer representing six input parameters (pipe velocity, PV, YP, diameter of hole, outer diameter of pipe and mud weight) and one neuron in output layer which represents surge pressure. Now, to find the most optimum neural network structure (number of hidden layer and neurons), total 108 ANN configuration is trained and tested. Performance analysis on these configurations indicates network structure with two hidden layers including ten and 16 neurons in first and second layer, respectively, as the most optimum. Since the selected model is complex, another trained model with one hidden layer containing 14 nodes can be considered due to its satisfactory prediction result. The trained intelligent model can be utilized when tripping operation is carried out in low-pressure margin wells where repetitive calculation of surge/swab pressure is required. © Springer Nature Singapore Pte Ltd. 2020.
format Article
author Krishna, S.
Ridha, S.
Vasant, P.
spellingShingle Krishna, S.
Ridha, S.
Vasant, P.
Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)
author_sort Krishna, S.
title Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)
title_short Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)
title_full Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)
title_fullStr Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)
title_full_unstemmed Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN)
title_sort prediction of bottom-hole pressure differential during tripping operations using artificial neural networks (ann)
publisher Springer
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085179397&doi=10.1007%2f978-981-15-3284-9_43&partnerID=40&md5=56ae14dc41a428c542910e67f71fe3c4
http://eprints.utp.edu.my/24791/
_version_ 1741196869146509312
score 11.62408

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