Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks
High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop...
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/10/2787 |
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| author | Ahmed Gowida Salaheldin Elkatatny Khaled Abdelgawad Rahul Gajbhiye |
| author_facet | Ahmed Gowida Salaheldin Elkatatny Khaled Abdelgawad Rahul Gajbhiye |
| author_sort | Ahmed Gowida |
| collection | DOAJ |
| description | High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (Y<sub>P</sub>), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE. |
| first_indexed | 2024-03-10T19:50:35Z |
| format | Article |
| id | doaj.art-669b3ada8c7a4f4a88d12b27c5e0614b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:50:35Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-669b3ada8c7a4f4a88d12b27c5e0614b2023-11-20T00:25:35ZengMDPI AGSensors1424-82202020-05-012010278710.3390/s20102787Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural NetworksAhmed Gowida0Salaheldin Elkatatny1Khaled Abdelgawad2Rahul Gajbhiye3Petroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaPetroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaPetroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaPetroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi ArabiaHigh-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (Y<sub>P</sub>), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.https://www.mdpi.com/1424-8220/20/10/2787rheological propertieshigh-bentonite mudartificial neural networkmud weightmarsh funnel |
| spellingShingle | Ahmed Gowida Salaheldin Elkatatny Khaled Abdelgawad Rahul Gajbhiye Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks Sensors rheological properties high-bentonite mud artificial neural network mud weight marsh funnel |
| title | Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks |
| title_full | Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks |
| title_fullStr | Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks |
| title_full_unstemmed | Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks |
| title_short | Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks |
| title_sort | newly developed correlations to predict the rheological parameters of high bentonite drilling fluid using neural networks |
| topic | rheological properties high-bentonite mud artificial neural network mud weight marsh funnel |
| url | https://www.mdpi.com/1424-8220/20/10/2787 |
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