Bit selection using field drilling data and mathematical investigation
A drilling process will not be complete without the usage of a drill bit. Therefore, bit selection is considered to be 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...
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| Main Authors: | , , , , |
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| Format: | Article |
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Institute of Physics Publishing
2018
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044532121&doi=10.1088%2f1757-899X%2f328%2f1%2f012008&partnerID=40&md5=1505b640fea9c7b927cf5249465a9901 http://eprints.utp.edu.my/21696/ |
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| Summary: | A drilling process will not be complete without the usage of a drill bit. Therefore, bit selection is considered to be 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, aback propagation ANN Model is developed. This is done by training the model using several wells and it is done by the usage of drilling bit records from offset wells. In this project, two models are 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. 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. 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 output is the Predicted IADC bit code. Thus, at the end, there are two models that give the Predicted ROP values and Predicted IADC bit code values. © Published under licence by IOP Publishing Ltd. |
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