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 |
| Published: |
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 |