Implementing artificial neural networks and support vector machines to predict lost circulation
Lost circulation is one of the major challenges encountered during drilling operations. The events related to the lost circulation can be responsible for losses of hundreds of millions of dollars each year. This paper presents a study on the application of artificial neural networks (ANNs) and suppo...
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| Format: | Article |
| Language: | English |
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Egyptian Petroleum Research Institute
2019-12-01
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| Series: | Egyptian Journal of Petroleum |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110062119301746 |
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| author | Ahmed K. Abbas Najim A. Al-haideri Ali A. Bashikh |
| author_facet | Ahmed K. Abbas Najim A. Al-haideri Ali A. Bashikh |
| author_sort | Ahmed K. Abbas |
| collection | DOAJ |
| description | Lost circulation is one of the major challenges encountered during drilling operations. The events related to the lost circulation can be responsible for losses of hundreds of millions of dollars each year. This paper presents a study on the application of artificial neural networks (ANNs) and support vector machine (SVM) to develop a robust system that can be used to predict the lost circulation occurrence. In the first step, field dataset, including drilling operation parameters, formation type, and lithology of the rock, as well as the drilling fluid characteristics, were collected from 385 wells drilled in southern Iraq from different fields. Then, the user-controlled parameters for ANNs (e.g., training function, number of hidden layers, transferring function, and number of neurons in each hidden layer) and SVMs (e.g., regularization factor, the type of kernel function, and its specific parameters) were optimized using the most common conventional performance criteria. Finally, the best-proposed models were examined using a few examples of real lost circulation cases from the field. The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results being more promising. The application of the machine learning methods could assist drilling engineers in modifying drilling parameters to minimize the likelihood of lost circulation. Keywords: Lost circulation, Artificial neural networks, Support vector machine |
| format | Article |
| id | doaj-art-6a8a30e5a4ca4eeb984b055f9ffb523c |
| institution | OA Journals |
| issn | 1110-0621 |
| language | English |
| publishDate | 2019-12-01 |
| publisher | Egyptian Petroleum Research Institute |
| record_format | Article |
| series | Egyptian Journal of Petroleum |
| spelling | doaj-art-6a8a30e5a4ca4eeb984b055f9ffb523c2025-08-20T01:57:15ZengEgyptian Petroleum Research InstituteEgyptian Journal of Petroleum1110-06212019-12-0128433934710.1016/j.ejpe.2019.06.006Implementing artificial neural networks and support vector machines to predict lost circulationAhmed K. Abbas0Najim A. Al-haideri1Ali A. Bashikh2Iraqi Drilling Company, Basra 61004, Iraq; Corresponding author.Iraqi Drilling Company, Basra 61004, IraqMissan Oil Company, Missan 62001, IraqLost circulation is one of the major challenges encountered during drilling operations. The events related to the lost circulation can be responsible for losses of hundreds of millions of dollars each year. This paper presents a study on the application of artificial neural networks (ANNs) and support vector machine (SVM) to develop a robust system that can be used to predict the lost circulation occurrence. In the first step, field dataset, including drilling operation parameters, formation type, and lithology of the rock, as well as the drilling fluid characteristics, were collected from 385 wells drilled in southern Iraq from different fields. Then, the user-controlled parameters for ANNs (e.g., training function, number of hidden layers, transferring function, and number of neurons in each hidden layer) and SVMs (e.g., regularization factor, the type of kernel function, and its specific parameters) were optimized using the most common conventional performance criteria. Finally, the best-proposed models were examined using a few examples of real lost circulation cases from the field. The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results being more promising. The application of the machine learning methods could assist drilling engineers in modifying drilling parameters to minimize the likelihood of lost circulation. Keywords: Lost circulation, Artificial neural networks, Support vector machinehttp://www.sciencedirect.com/science/article/pii/S1110062119301746 |
| spellingShingle | Ahmed K. Abbas Najim A. Al-haideri Ali A. Bashikh Implementing artificial neural networks and support vector machines to predict lost circulation Egyptian Journal of Petroleum |
| title | Implementing artificial neural networks and support vector machines to predict lost circulation |
| title_full | Implementing artificial neural networks and support vector machines to predict lost circulation |
| title_fullStr | Implementing artificial neural networks and support vector machines to predict lost circulation |
| title_full_unstemmed | Implementing artificial neural networks and support vector machines to predict lost circulation |
| title_short | Implementing artificial neural networks and support vector machines to predict lost circulation |
| title_sort | implementing artificial neural networks and support vector machines to predict lost circulation |
| url | http://www.sciencedirect.com/science/article/pii/S1110062119301746 |
| work_keys_str_mv | AT ahmedkabbas implementingartificialneuralnetworksandsupportvectormachinestopredictlostcirculation AT najimaalhaideri implementingartificialneuralnetworksandsupportvectormachinestopredictlostcirculation AT aliabashikh implementingartificialneuralnetworksandsupportvectormachinestopredictlostcirculation |