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: | |
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| Format: | Final Year Project |
| Language: | English |
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Universiti Teknologi PETRONAS
2014
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| Online Access: | http://utpedia.utp.edu.my/14233/1/dissertation%20report%20prethip%2015202.pdf http://utpedia.utp.edu.my/14233/ |
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| Summary: | 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 |
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