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Bit Selection Using Drilling Data By Artificial Neural Networks

Bit selection is an important task in drilling optimization process. To select a bit is considered as an important issue in planning and designing a well. This is simply because the cost of drilling bit in total cost is quite high. Thus, to perform this task, a back propagation ANN model will be dev...

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Main Author: Watalingam, Prethipkumar
Format: Final Year Project
Language:English
Published: Universiti Teknologi PETRONAS 2014
Subjects:
T Technology (General)
Online Access:http://utpedia.utp.edu.my/14233/1/dissertation%20report%20prethip%2015202.pdf
http://utpedia.utp.edu.my/14233/
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spelling my-utp-utpedia.142332017-01-25T09:37:42Z http://utpedia.utp.edu.my/14233/ Bit Selection Using Drilling Data By Artificial Neural Networks Watalingam, Prethipkumar T Technology (General) Bit selection is an important task in drilling optimization process. To select a bit is considered as an important issue in planning and designing a well. This is simply because the cost of drilling bit in total cost is quite high. Thus, to perform this task, a back propagation ANN model will be developed. This is done by training the model using drilling bit records from offset wells. In this project, two models will be developed by the usage of the ANN. One is to find predicted IADC bit code and one is to find Predicted ROP. Stage 1 was to find the IADC bit code by using all the given filed data. This data includes Size(in), Flow area (in2), Depth out (m MD), Bit meter (m), Rotation hours (hrs), ROP(m/hr), Minimum Rotation (rpm), Maximum Rotation (rpm), Total bit revolution, Minimum Weight (kN), Maximum Weight (kN), Minimum Flow (l/min), Maximum Flow (l/min), Minimum Pump Pressure (bar), and Maximum Pump Pressure (bar). The output is the Targeted IADC bit code. Stage 2 was to find the Predicted ROP values using the gained IADC bit code in Stage 1. This time, the data used as input in the ANN modeling process includes Targeted IADC bit code, Size(in), Flow area (in2), Depth out (m MD), Bit meter (m), Rotation hours (hrs), Minimum Rotation (rpm), Maximum Rotation (rpm), Total bit revolution, Minimum Weight (kN), Maximum Weight (kN), Minimum Flow (l/min), Maximum Flow (l/min), Minimum Pump Pressure (bar), and Maximum Pump Pressure (bar). The output is the Predicted ROP. Next is Stage 3 where the Predicted ROP value is used back again in the data set to gain Predicted IADC bit code value. The input parameters was Size(in), Flow area (in2), Depth out (m MD), Bit meter (m), Rotation hours (hrs), Predicted ROP(m/hr), Minimum Rotation (rpm), Maximum Rotation (rpm), Total bit revolution, Minimum Weight (kN), Maximum Weight (kN), Minimum Flow (l/min), Maximum Flow (l/min), Minimum Pump Pressure (bar), and Maximum Pump Pressure (bar). The output is the Predicted IADC bit code. Thus, at the end there will be two models that give the Predicted ROP values and Predicted IADC bit code values. Results showed that the final Regression value obtained overall was more than 95% accurate for Predicted IADC bit code and Predicted ROP values Universiti Teknologi PETRONAS 2014-05 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/14233/1/dissertation%20report%20prethip%2015202.pdf Watalingam, Prethipkumar (2014) Bit Selection Using Drilling Data By Artificial Neural Networks. Universiti Teknologi PETRONAS. (Unpublished)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Watalingam, Prethipkumar
Bit Selection Using Drilling Data By Artificial Neural Networks
description Bit selection is an important task in drilling optimization process. To select a bit is considered as an important issue in planning and designing a well. This is simply because the cost of drilling bit in total cost is quite high. Thus, to perform this task, a back propagation ANN model will be developed. This is done by training the model using drilling bit records from offset wells. In this project, two models will be developed by the usage of the ANN. One is to find predicted IADC bit code and one is to find Predicted ROP. Stage 1 was to find the IADC bit code by using all the given filed data. This data includes Size(in), Flow area (in2), Depth out (m MD), Bit meter (m), Rotation hours (hrs), ROP(m/hr), Minimum Rotation (rpm), Maximum Rotation (rpm), Total bit revolution, Minimum Weight (kN), Maximum Weight (kN), Minimum Flow (l/min), Maximum Flow (l/min), Minimum Pump Pressure (bar), and Maximum Pump Pressure (bar). The output is the Targeted IADC bit code. Stage 2 was to find the Predicted ROP values using the gained IADC bit code in Stage 1. This time, the data used as input in the ANN modeling process includes Targeted IADC bit code, Size(in), Flow area (in2), Depth out (m MD), Bit meter (m), Rotation hours (hrs), Minimum Rotation (rpm), Maximum Rotation (rpm), Total bit revolution, Minimum Weight (kN), Maximum Weight (kN), Minimum Flow (l/min), Maximum Flow (l/min), Minimum Pump Pressure (bar), and Maximum Pump Pressure (bar). The output is the Predicted ROP. Next is Stage 3 where the Predicted ROP value is used back again in the data set to gain Predicted IADC bit code value. The input parameters was Size(in), Flow area (in2), Depth out (m MD), Bit meter (m), Rotation hours (hrs), Predicted ROP(m/hr), Minimum Rotation (rpm), Maximum Rotation (rpm), Total bit revolution, Minimum Weight (kN), Maximum Weight (kN), Minimum Flow (l/min), Maximum Flow (l/min), Minimum Pump Pressure (bar), and Maximum Pump Pressure (bar). The output is the Predicted IADC bit code. Thus, at the end there will be two models that give the Predicted ROP values and Predicted IADC bit code values. Results showed that the final Regression value obtained overall was more than 95% accurate for Predicted IADC bit code and Predicted ROP values
format Final Year Project
author Watalingam, Prethipkumar
author_facet Watalingam, Prethipkumar
author_sort Watalingam, Prethipkumar
title Bit Selection Using Drilling Data By Artificial Neural Networks
title_short Bit Selection Using Drilling Data By Artificial Neural Networks
title_full Bit Selection Using Drilling Data By Artificial Neural Networks
title_fullStr Bit Selection Using Drilling Data By Artificial Neural Networks
title_full_unstemmed Bit Selection Using Drilling Data By Artificial Neural Networks
title_sort bit selection using drilling data by artificial neural networks
publisher Universiti Teknologi PETRONAS
publishDate 2014
url http://utpedia.utp.edu.my/14233/1/dissertation%20report%20prethip%2015202.pdf
http://utpedia.utp.edu.my/14233/
_version_ 1739831978508681216
score 13.211869

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