Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells
Abstract In this paper, an artificial neural network model was developed to predict the downhole density of oil-based muds under high-temperature, high-pressure conditions. Six performance metrics, namely goodness of fit (R 2), mean square error (MSE), mean absolute error (MAE), mean absolute percen...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-11-01
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| Series: | Journal of Petroleum Exploration and Production Technology |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s13202-019-00802-6 |
| _version_ | 1811253684894433280 |
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| author | Okorie E. Agwu Julius U. Akpabio Adewale Dosunmu |
| author_facet | Okorie E. Agwu Julius U. Akpabio Adewale Dosunmu |
| author_sort | Okorie E. Agwu |
| collection | DOAJ |
| description | Abstract In this paper, an artificial neural network model was developed to predict the downhole density of oil-based muds under high-temperature, high-pressure conditions. Six performance metrics, namely goodness of fit (R 2), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), sum of squares error (SSE) and root mean square error (RMSE), were used to assess the performance of the developed model. From the results, the model had an overall MSE of 0.000477 with an MAE of 0.017 and an R 2 of 0.9999, MAPE of 0.127, RMSE of 0.022 and SSE of 0.056. All the model predictions were in excellent agreement with the measured results. Consequently, in assessing the generalization capability of the developed model for the oil-based mud, a new set of data that was not part of the training process of the model comprising 34 data points was used. In this regard, the model was able to predict 99% of the unfamiliar data with an MSE of 0.0159, MAE of 0.101, RMSE of 0.126, SSE of 0.54 and a MAPE of 0.7. In comparison with existing models, the ANN model developed in this study performed better. The sensitivity analysis performed shows that the initial mud density has the greatest impact on the final mud density downhole. This unique modelling technique and the model it evolved represents a huge step in the trajectory of achieving full automation of downhole mud density estimation. Furthermore, this method eliminates the need for surface measurement equipment, while at the same time, representing more accurately the downhole mud density at any given pressure and temperature. |
| first_indexed | 2024-04-12T16:54:41Z |
| format | Article |
| id | doaj.art-6c5063cbde2540c29786aefa8e9837ad |
| institution | Directory Open Access Journal |
| issn | 2190-0558 2190-0566 |
| language | English |
| last_indexed | 2024-04-12T16:54:41Z |
| publishDate | 2019-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj.art-6c5063cbde2540c29786aefa8e9837ad2022-12-22T03:24:17ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662019-11-011031081109510.1007/s13202-019-00802-6Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wellsOkorie E. Agwu0Julius U. Akpabio1Adewale Dosunmu2Department of Chemical and Petroleum Engineering, University of UyoDepartment of Chemical and Petroleum Engineering, University of UyoDepartment of Petroleum and Gas Engineering, University of Port HarcourtAbstract In this paper, an artificial neural network model was developed to predict the downhole density of oil-based muds under high-temperature, high-pressure conditions. Six performance metrics, namely goodness of fit (R 2), mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), sum of squares error (SSE) and root mean square error (RMSE), were used to assess the performance of the developed model. From the results, the model had an overall MSE of 0.000477 with an MAE of 0.017 and an R 2 of 0.9999, MAPE of 0.127, RMSE of 0.022 and SSE of 0.056. All the model predictions were in excellent agreement with the measured results. Consequently, in assessing the generalization capability of the developed model for the oil-based mud, a new set of data that was not part of the training process of the model comprising 34 data points was used. In this regard, the model was able to predict 99% of the unfamiliar data with an MSE of 0.0159, MAE of 0.101, RMSE of 0.126, SSE of 0.54 and a MAPE of 0.7. In comparison with existing models, the ANN model developed in this study performed better. The sensitivity analysis performed shows that the initial mud density has the greatest impact on the final mud density downhole. This unique modelling technique and the model it evolved represents a huge step in the trajectory of achieving full automation of downhole mud density estimation. Furthermore, this method eliminates the need for surface measurement equipment, while at the same time, representing more accurately the downhole mud density at any given pressure and temperature.http://link.springer.com/article/10.1007/s13202-019-00802-6Artificial neural networkDownhole mud densityDrilling mudHTHP |
| spellingShingle | Okorie E. Agwu Julius U. Akpabio Adewale Dosunmu Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells Journal of Petroleum Exploration and Production Technology Artificial neural network Downhole mud density Drilling mud HTHP |
| title | Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells |
| title_full | Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells |
| title_fullStr | Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells |
| title_full_unstemmed | Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells |
| title_short | Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells |
| title_sort | artificial neural network model for predicting the density of oil based muds in high temperature high pressure wells |
| topic | Artificial neural network Downhole mud density Drilling mud HTHP |
| url | http://link.springer.com/article/10.1007/s13202-019-00802-6 |
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