A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques
Determination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields development. Accurate estimation of PP is also essential for safe drilling of oil and gas wells since PP data are used as...
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| Published in: | Energy reports Vol. 8; pp. 2233 - 2247 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.11.2022
Elsevier |
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| ISSN: | 2352-4847, 2352-4847 |
| Online Access: | Get full text |
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| Abstract | Determination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields development. Accurate estimation of PP is also essential for safe drilling of oil and gas wells since PP data are used as the input for safe mud window determination. In the present study, empirical equations along with machine learning methods, namely random forest algorithm, support vector regression (SVR) algorithm, artificial neural network (ANN) algorithm, and decision tree (DT) algorithm, are employed for PP prediction applying well log data. To this end, 2827 data records collected from three wells (Well A, Well B, and Well C) drilled in one of the Middle East oil fields are used. The dataset of Wells A and B is used for models’ training, validating, and testing, while Well C dataset is applied for evaluating the models’ generalizability in PP prediction in the field under study. To construct the predictive algorithms, 12 input variables are initially considered in the study. A feature selection analysis is conducted to find the most influential input variables set for developing PP predictive models. The results obtained suggest that the 9-input-variable set is the most efficient combination of inputs used in the ML models construction. Among all the four ML algorithms proposed, the DT algorithm presents the most accurate predictions for PP, delivering R2 and RMSE values of 0.9985 and 14.460 psi, respectively. Furthermore, the model generalization analysis results reveal that the 9-input-variable DT model developed can be used for PP prediction throughout the field of study since it presented an excellent accuracy performance in predicting PP when applied to Well C dataset. |
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| AbstractList | Determination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields development. Accurate estimation of PP is also essential for safe drilling of oil and gas wells since PP data are used as the input for safe mud window determination. In the present study, empirical equations along with machine learning methods, namely random forest algorithm, support vector regression (SVR) algorithm, artificial neural network (ANN) algorithm, and decision tree (DT) algorithm, are employed for PP prediction applying well log data. To this end, 2827 data records collected from three wells (Well A, Well B, and Well C) drilled in one of the Middle East oil fields are used. The dataset of Wells A and B is used for models’ training, validating, and testing, while Well C dataset is applied for evaluating the models’ generalizability in PP prediction in the field under study. To construct the predictive algorithms, 12 input variables are initially considered in the study. A feature selection analysis is conducted to find the most influential input variables set for developing PP predictive models. The results obtained suggest that the 9-input-variable set is the most efficient combination of inputs used in the ML models construction. Among all the four ML algorithms proposed, the DT algorithm presents the most accurate predictions for PP, delivering R2 and RMSE values of 0.9985 and 14.460 psi, respectively. Furthermore, the model generalization analysis results reveal that the 9-input-variable DT model developed can be used for PP prediction throughout the field of study since it presented an excellent accuracy performance in predicting PP when applied to Well C dataset. |
| Author | Mosavi, Amir Davoodi, Shadfar Zhang, Guodao Moslehpour, Massoud Ghorbani, Hamzeh Band, Shahab S. |
| Author_xml | – sequence: 1 givenname: Guodao orcidid: 0000-0002-6264-5854 surname: Zhang fullname: Zhang, Guodao email: guodaozhang@zjut.edu.cn organization: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China – sequence: 2 givenname: Shadfar orcidid: 0000-0003-1733-1677 surname: Davoodi fullname: Davoodi, Shadfar email: davoodis@hw.tpu.ru organization: School of Earth Sciences & Engineering, Tomsk Polytechnic University, Lenin Avenue, Tomsk, Russia – sequence: 3 givenname: Shahab S. surname: Band fullname: Band, Shahab S. email: shamshirbands@yuntech.edu.tw organization: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan – sequence: 4 givenname: Hamzeh orcidid: 0000-0003-4657-8249 surname: Ghorbani fullname: Ghorbani, Hamzeh email: hamzehghorbani68@yahoo.com organization: Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran – sequence: 5 givenname: Amir surname: Mosavi fullname: Mosavi, Amir email: amir.mosavi@kvk.uni-obuda.hu organization: Institute of Information Society, University of Public Service, 1083 Budapest, Hungary – sequence: 6 givenname: Massoud orcidid: 0000-0001-8808-2407 surname: Moslehpour fullname: Moslehpour, Massoud email: writetodrm@gmail.com organization: Department of Business Administration, Asia University, 500, Lioufeng Rd., Wufeng, Taichung 41354, Taiwan |
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| Keywords | Petrophysical data Decision tree algorithm Machine learning algorithms Pore pressure |
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| Snippet | Determination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in... |
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| Title | A robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques |
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