Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis
Shale gas reservoirs have huge amounts of reserves. Economically evaluating these reserves is challenging due to complex driving mechanisms, complex drilling and completion configurations, and the complexity of controlling the producing conditions. Decline Curve Analysis (DCA) is historically consid...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/23/8835 |
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| author | Taha Yehia Ali Wahba Sondos Mostafa Omar Mahmoud |
| author_facet | Taha Yehia Ali Wahba Sondos Mostafa Omar Mahmoud |
| author_sort | Taha Yehia |
| collection | DOAJ |
| description | Shale gas reservoirs have huge amounts of reserves. Economically evaluating these reserves is challenging due to complex driving mechanisms, complex drilling and completion configurations, and the complexity of controlling the producing conditions. Decline Curve Analysis (DCA) is historically considered the easiest method for production prediction of unconventional reservoirs as it only requires production history. Besides uncertainties in selecting a suitable DCA model to match the production behavior of the shale gas wells, the production data are usually noisy because of the changing choke size used to control the bottom hole flowing pressure and the multiple shut-ins to remove the associated water. Removing this noise from the data is important for effective DCA prediction. In this study, 12 machine learning outlier detection algorithms were investigated to determine the one most suitable for improving the quality of production data. Five of them were found not suitable, as they remove complete portions of the production data rather than scattered data points. The other seven algorithms were deeply investigated, assuming that 20% of the production data are outliers. During the work, eight DCA models were studied and applied. Different recommendations were stated regarding their sensitivity to noise. The results showed that the clustered based outlier factor, k-nearest neighbor, and the angular based outlier factor algorithms are the most effective algorithms for improving the data quality for DCA, while the stochastic outlier selection and subspace outlier detection algorithms were found to be the least effective. Additionally, DCA models, such as the Arps, Duong, and Wang models, were found to be less sensitive to removing noise, even with different algorithms. Meanwhile, power law exponential, logistic growth model, and stretched exponent production decline models showed more sensitivity to removing the noise, with varying performance under different outlier-removal algorithms. This work introduces the best combination of DCA models and outlier-detection algorithms, which could be used to reduce the uncertainties related to production forecasting and reserve estimation of shale gas reservoirs. |
| first_indexed | 2024-03-09T17:49:51Z |
| format | Article |
| id | doaj.art-d308b8b7ca7e406cab3b7695b7d3f4d8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T17:49:51Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d308b8b7ca7e406cab3b7695b7d3f4d82023-11-24T10:51:22ZengMDPI AGEnergies1996-10732022-11-011523883510.3390/en15238835Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve AnalysisTaha Yehia0Ali Wahba1Sondos Mostafa2Omar Mahmoud3Department of Petroleum Engineering, Faculty of Engineering and Technology, Future University in Egypt (FUE), Cairo 11835, EgyptDepartment of Petroleum Engineering, Faculty of Petroleum and Mining Engineering, Suez University, Suez 43512, EgyptDepartment of Petroleum Engineering, Faculty of Petroleum and Mining Engineering, Suez University, Suez 43512, EgyptDepartment of Petroleum Engineering, Faculty of Engineering and Technology, Future University in Egypt (FUE), Cairo 11835, EgyptShale gas reservoirs have huge amounts of reserves. Economically evaluating these reserves is challenging due to complex driving mechanisms, complex drilling and completion configurations, and the complexity of controlling the producing conditions. Decline Curve Analysis (DCA) is historically considered the easiest method for production prediction of unconventional reservoirs as it only requires production history. Besides uncertainties in selecting a suitable DCA model to match the production behavior of the shale gas wells, the production data are usually noisy because of the changing choke size used to control the bottom hole flowing pressure and the multiple shut-ins to remove the associated water. Removing this noise from the data is important for effective DCA prediction. In this study, 12 machine learning outlier detection algorithms were investigated to determine the one most suitable for improving the quality of production data. Five of them were found not suitable, as they remove complete portions of the production data rather than scattered data points. The other seven algorithms were deeply investigated, assuming that 20% of the production data are outliers. During the work, eight DCA models were studied and applied. Different recommendations were stated regarding their sensitivity to noise. The results showed that the clustered based outlier factor, k-nearest neighbor, and the angular based outlier factor algorithms are the most effective algorithms for improving the data quality for DCA, while the stochastic outlier selection and subspace outlier detection algorithms were found to be the least effective. Additionally, DCA models, such as the Arps, Duong, and Wang models, were found to be less sensitive to removing noise, even with different algorithms. Meanwhile, power law exponential, logistic growth model, and stretched exponent production decline models showed more sensitivity to removing the noise, with varying performance under different outlier-removal algorithms. This work introduces the best combination of DCA models and outlier-detection algorithms, which could be used to reduce the uncertainties related to production forecasting and reserve estimation of shale gas reservoirs.https://www.mdpi.com/1996-1073/15/23/8835Decline Curve Analysisshale gasproduction forecastoutlier detectionmachine learning |
| spellingShingle | Taha Yehia Ali Wahba Sondos Mostafa Omar Mahmoud Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis Energies Decline Curve Analysis shale gas production forecast outlier detection machine learning |
| title | Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis |
| title_full | Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis |
| title_fullStr | Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis |
| title_full_unstemmed | Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis |
| title_short | Suitability of Different Machine Learning Outlier Detection Algorithms to Improve Shale Gas Production Data for Effective Decline Curve Analysis |
| title_sort | suitability of different machine learning outlier detection algorithms to improve shale gas production data for effective decline curve analysis |
| topic | Decline Curve Analysis shale gas production forecast outlier detection machine learning |
| url | https://www.mdpi.com/1996-1073/15/23/8835 |
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