Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate
The rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, i...
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| Format: | Article |
| Language: | English |
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Petroleum University of Technology
2018-07-01
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| Series: | Iranian Journal of Oil & Gas Science and Technology |
| Subjects: | |
| Online Access: | http://ijogst.put.ac.ir/article_55716_4d0da24ff9e74084554174ff9574731b.pdf |
| _version_ | 1818410992471638016 |
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| author | Hossein Yavari Mohammad Sabah Rassoul Khosravanian David. A Wood |
| author_facet | Hossein Yavari Mohammad Sabah Rassoul Khosravanian David. A Wood |
| author_sort | Hossein Yavari |
| collection | DOAJ |
| description | The rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, including mathematical models and artificial intelligence have been proposed to predict the rate of penetration. Previous research has showed that artificial intelligence such as neural network and adaptive neuro-fuzzy inference system are superior to conventional methods in the prediction of drilling rate. On the other hand, many complicated analytical ROP models have also been developed during recent years that are able to predict drilling rate with a high degree of accuracy. Therefore, comparing different approaches to find the most accurate model and assess the conditions in which each model works well can be highly effective in reducing drilling time as well as drilling cost. In this study, Hareland-Rampersad (HR) model, Bourgoyne and Young (BY) model, and an adaptive-neuro-fuzzy inference system (ANFIS) are employed to predict the drilling rate in the South Pars gas field (SP) offshore of Iran, and their results are compared to find the best ROP-prediction model for each formation. A database covering the drilling parameters, sonic log data, and modular dynamic test data collected from several drilling sites in SP are used to construct the mentioned models for each formation. The results show that when a large amount of data is available, the ANFIS is more accurate than the other approaches in predicting drilling rate. In the case of ROP models, BY model works considerably better than HR model for the majority of the formations. However, in formations where some drilling parameters are constant, but formation strength is variable, HR model shows better prediction performance than BY model. |
| first_indexed | 2024-12-14T10:24:20Z |
| format | Article |
| id | doaj.art-eb51e7c92b5446bd902ec0a8d5ef144f |
| institution | Directory Open Access Journal |
| issn | 2345-2412 2345-2420 |
| language | English |
| last_indexed | 2024-12-14T10:24:20Z |
| publishDate | 2018-07-01 |
| publisher | Petroleum University of Technology |
| record_format | Article |
| series | Iranian Journal of Oil & Gas Science and Technology |
| spelling | doaj.art-eb51e7c92b5446bd902ec0a8d5ef144f2022-12-21T23:06:26ZengPetroleum University of TechnologyIranian Journal of Oil & Gas Science and Technology2345-24122345-24202018-07-01737310010.22050/ijogst.2018.83374.139155716Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling RateHossein Yavari0Mohammad Sabah1Rassoul Khosravanian2David. A Wood3M.S. Student of Petroleum Department of Amirkabir University of Technology, Tehran, IranM.S. Student of Petroleum Department of Amirkabir University of Technology, Tehran, IranAssistant Professor of Petroleum Department of Amirkabir University of Technology, Tehran, IranDWA Energy Limited, Lincoln, United KingdomThe rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, including mathematical models and artificial intelligence have been proposed to predict the rate of penetration. Previous research has showed that artificial intelligence such as neural network and adaptive neuro-fuzzy inference system are superior to conventional methods in the prediction of drilling rate. On the other hand, many complicated analytical ROP models have also been developed during recent years that are able to predict drilling rate with a high degree of accuracy. Therefore, comparing different approaches to find the most accurate model and assess the conditions in which each model works well can be highly effective in reducing drilling time as well as drilling cost. In this study, Hareland-Rampersad (HR) model, Bourgoyne and Young (BY) model, and an adaptive-neuro-fuzzy inference system (ANFIS) are employed to predict the drilling rate in the South Pars gas field (SP) offshore of Iran, and their results are compared to find the best ROP-prediction model for each formation. A database covering the drilling parameters, sonic log data, and modular dynamic test data collected from several drilling sites in SP are used to construct the mentioned models for each formation. The results show that when a large amount of data is available, the ANFIS is more accurate than the other approaches in predicting drilling rate. In the case of ROP models, BY model works considerably better than HR model for the majority of the formations. However, in formations where some drilling parameters are constant, but formation strength is variable, HR model shows better prediction performance than BY model.http://ijogst.put.ac.ir/article_55716_4d0da24ff9e74084554174ff9574731b.pdfRate of Penetration (ROP)ANFISBourgoyne and YoungHareland-RampersadSimulated Annealing Algorithm (SAA) |
| spellingShingle | Hossein Yavari Mohammad Sabah Rassoul Khosravanian David. A Wood Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate Iranian Journal of Oil & Gas Science and Technology Rate of Penetration (ROP) ANFIS Bourgoyne and Young Hareland-Rampersad Simulated Annealing Algorithm (SAA) |
| title | Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate |
| title_full | Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate |
| title_fullStr | Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate |
| title_full_unstemmed | Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate |
| title_short | Application of an Adaptive Neuro-fuzzy Inference System and Mathematical Rate of Penetration Models to Predicting Drilling Rate |
| title_sort | application of an adaptive neuro fuzzy inference system and mathematical rate of penetration models to predicting drilling rate |
| topic | Rate of Penetration (ROP) ANFIS Bourgoyne and Young Hareland-Rampersad Simulated Annealing Algorithm (SAA) |
| url | http://ijogst.put.ac.ir/article_55716_4d0da24ff9e74084554174ff9574731b.pdf |
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