Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties
Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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University of Baghdad
2020-07-01
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| Series: | Journal of Engineering |
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| Online Access: | http://joe.uobaghdad.edu.iq/index.php/main/article/view/1037 |
| _version_ | 1797718088677851136 |
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| author | Yasser abbas khudaier Fadhil Sarhan Kadhim Yousif Khalaf Yousif |
| author_facet | Yasser abbas khudaier Fadhil Sarhan Kadhim Yousif Khalaf Yousif |
| author_sort | Yasser abbas khudaier |
| collection | DOAJ |
| description | Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal. The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in Mishrif formation in Nasiriya oil field for the selected wells. The mean square error for the results obtained from the ANFIS model was 0.015. The model was trained and simulated using MATLAB and Simulink platform. Laboratory measurements were conducted on core samples selected from two wells. Ultrasonic device was used to measure the transit time of compressional and shear waves and to compare these results with log records. Ten wells in Nasiriya oil field had been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software and based on the las files and log records provided. The average rate of penetration of the studied wells was determined and listed against depth with the average dynamic elastic properties and fed into the fuzzy system. The average values of bulk modulus for the ten wells ranged between (20.57) and (27.57) . For shear modulus, the range was from (8.63) to (12.95) GPa. Also, the Poisson’s ratio values varied from (0.297) to (0.307). For the first group of wells (NS-1, NS-3, NS-4, NS-5, and NS-18), the ROP values were taken from the drilling reports and the lowest ROP was at the bottom of the formation with a value of (3.965) m/hrs while the highest ROP at the top of the formation with a value (4.073) m/hrs. The ROP values predicted by the ANFIS for this group were (3.181) m/hrs and (4.865) m/hrs for the lowest and highest values respectively. For the second group of wells (NS-9, NS-15, NS-16, NS-19, and NS-21), the highest ROP obtained from drilling reports was (4.032) m/hrs while the lowest value was (3.96) m/hrs. For the predicted values by ANFIS model were (2.35) m/hrs and (4.3) m/hrs for the lowest and highest ROP values respectively. |
| first_indexed | 2024-03-12T08:45:55Z |
| format | Article |
| id | doaj.art-84e449eec21f4e52b04d1e8e10f08801 |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-12T08:45:55Z |
| publishDate | 2020-07-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-84e449eec21f4e52b04d1e8e10f088012023-09-02T16:26:41ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392020-07-0126710.31026/j.eng.2020.07.04Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic PropertiesYasser abbas khudaier0Fadhil Sarhan Kadhim1Yousif Khalaf Yousif2Petroleum Technology Department , University of Technology, IraqPetroleum Technology Department , University of Technology, IraqMinistry of Higher Education and scientific research Iraq. BaghdadRate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal. The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in Mishrif formation in Nasiriya oil field for the selected wells. The mean square error for the results obtained from the ANFIS model was 0.015. The model was trained and simulated using MATLAB and Simulink platform. Laboratory measurements were conducted on core samples selected from two wells. Ultrasonic device was used to measure the transit time of compressional and shear waves and to compare these results with log records. Ten wells in Nasiriya oil field had been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software and based on the las files and log records provided. The average rate of penetration of the studied wells was determined and listed against depth with the average dynamic elastic properties and fed into the fuzzy system. The average values of bulk modulus for the ten wells ranged between (20.57) and (27.57) . For shear modulus, the range was from (8.63) to (12.95) GPa. Also, the Poisson’s ratio values varied from (0.297) to (0.307). For the first group of wells (NS-1, NS-3, NS-4, NS-5, and NS-18), the ROP values were taken from the drilling reports and the lowest ROP was at the bottom of the formation with a value of (3.965) m/hrs while the highest ROP at the top of the formation with a value (4.073) m/hrs. The ROP values predicted by the ANFIS for this group were (3.181) m/hrs and (4.865) m/hrs for the lowest and highest values respectively. For the second group of wells (NS-9, NS-15, NS-16, NS-19, and NS-21), the highest ROP obtained from drilling reports was (4.032) m/hrs while the lowest value was (3.96) m/hrs. For the predicted values by ANFIS model were (2.35) m/hrs and (4.3) m/hrs for the lowest and highest ROP values respectively.http://joe.uobaghdad.edu.iq/index.php/main/article/view/1037Rate of penetration (ROP), Adaptive neuro fuzzy inference system (ANFIS), Nasiriya oil field, Dynamic elastic properties. |
| spellingShingle | Yasser abbas khudaier Fadhil Sarhan Kadhim Yousif Khalaf Yousif Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties Journal of Engineering Rate of penetration (ROP), Adaptive neuro fuzzy inference system (ANFIS), Nasiriya oil field, Dynamic elastic properties. |
| title | Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties |
| title_full | Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties |
| title_fullStr | Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties |
| title_full_unstemmed | Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties |
| title_short | Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties |
| title_sort | using adaptive neuro fuzzy inference system to predict rate of penetration from dynamic elastic properties |
| topic | Rate of penetration (ROP), Adaptive neuro fuzzy inference system (ANFIS), Nasiriya oil field, Dynamic elastic properties. |
| url | http://joe.uobaghdad.edu.iq/index.php/main/article/view/1037 |
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