Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs

Wang, Lian; Yao, Yuedong; Wang, Kongjie; Adenutsi, Caspar Daniel; Zhao, Guoxiang; Lai, Fengpeng

doi:10.1016/j.energy.2021.122747

Cited by 15 publications

(4 citation statements)

References 32 publications

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“…For the validation of the proxy model, five criteria were considered: mean square error (MSE), organizing parameter (γ), kernel width parameter (σ 2 ), mean absolute error, and R 2 . These parameters are taken into account when comparing the calculated values to the actual values . If the proxy’s quality is determined to be appropriate, it can be made ready for usage after validation.…”

Section: Methodsmentioning

confidence: 99%

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Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Alavi Nezhad Khalil Abad,

Hazbeh,

Rajabi

et al. 2023

ACS Omega

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Underground resources, particularly hydrocarbons, are critical assets that promote economic development on a global scale. Drilling activities are necessary for the extraction and recovery of subsurface energy resources, and the rate of penetration (ROP) is one of the most important drilling parameters. This study forecasts the ROP using drilling data from three Iranian wells and hybrid LSSVM-GA/PSO algorithms. These algorithms were chosen due to their ability to reduce noise and increase accuracy despite the high level of noise present in the data. The study results revealed that the LSSVM-PSO method has an accuracy of roughly 97% and is more precise than the LSSVM-GA technique. The LSSVM-PSO algorithm also demonstrated improved accuracy in test data, with RMSE = 1.92 and R 2 = 0.9516. Furthermore, it was observed that the accuracy of the LSSVM-PSO model improves and degrades after the 50th iteration, whereas the accuracy of the LSSVM-GA algorithm remains constant after the 10th iteration. Notably, these algorithms are advantageous in decreasing data noise for drilling data.

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Section: Methodsmentioning

confidence: 99%

“… 61 These parameters are taken into account when comparing the calculated values to the actual values. 62 If the proxy’s quality is determined to be appropriate, it can be made ready for usage after validation. The procedure will then be carried out once more to improve the proxy’s quality in such a situation.…”

Section: Methodsmentioning

confidence: 99%

Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Alavi Nezhad Khalil Abad,

Hazbeh,

Rajabi

et al. 2023

ACS Omega

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“…Hybrid ML approaches that combine complementary models have been reported to have higher accuracy or a better interpretation of results than standalone models [ 23 - 25 ]. Combining supervised models such as XGBoost and logistic regression with unsupervised learning may help to overcome the challenges of predicting measles cases, based on the assumption that unsupervised learning processes will extract patterns from data that can be used as a new set of features that are less prone to biases introduced by multicollinearity and imbalanced data [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches

Ru¹,

Kujawski²,

Afanador³

et al. 2023

JMIR Form Res

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Background Measles, a highly contagious viral infection, is resurging in the United States, driven by international importation and declining domestic vaccination coverage. Despite this resurgence, measles outbreaks are still rare events that are difficult to predict. Improved methods to predict outbreaks at the county level would facilitate the optimal allocation of public health resources. Objective We aimed to validate and compare extreme gradient boosting (XGBoost) and logistic regression, 2 supervised learning approaches, to predict the US counties most likely to experience measles cases. We also aimed to assess the performance of hybrid versions of these models that incorporated additional predictors generated by 2 clustering algorithms, hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and unsupervised random forest (uRF). Methods We constructed a supervised machine learning model based on XGBoost and unsupervised models based on HDBSCAN and uRF. The unsupervised models were used to investigate clustering patterns among counties with measles outbreaks; these clustering data were also incorporated into hybrid XGBoost models as additional input variables. The machine learning models were then compared to logistic regression models with and without input from the unsupervised models. Results Both HDBSCAN and uRF identified clusters that included a high percentage of counties with measles outbreaks. XGBoost and XGBoost hybrid models outperformed logistic regression and logistic regression hybrid models, with the area under the receiver operating curve values of 0.920-0.926 versus 0.900-0.908, the area under the precision-recall curve values of 0.522-0.532 versus 0.485-0.513, and F2 scores of 0.595-0.601 versus 0.385-0.426. Logistic regression or logistic regression hybrid models had higher sensitivity than XGBoost or XGBoost hybrid models (0.837-0.857 vs 0.704-0.735) but a lower positive predictive value (0.122-0.141 vs 0.340-0.367) and specificity (0.793-0.821 vs 0.952-0.958). The hybrid versions of the logistic regression and XGBoost models had slightly higher areas under the precision-recall curve, specificity, and positive predictive values than the respective models that did not include any unsupervised features. Conclusions XGBoost provided more accurate predictions of measles cases at the county level compared with logistic regression. The threshold of prediction in this model can be adjusted to align with each county’s resources, priorities, and risk for measles. While clustering pattern data from unsupervised machine learning approaches improved some aspects of model performance in this imbalanced data set, the optimal approach for the integration of such approaches with supervised machine learning models requires further investigation.

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“…Hybrid ML approaches that combine complementary models have been reported to have higher accuracy or a better interpretation of results than standalone models [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches (Preprint)

Ru¹,

Kujawski²,

Afanador³

et al. 2022

Preprint

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BACKGROUND Measles is resurging in the US, driven by international importation and declining domestic vaccination coverage. Improved methods to predict outbreaks at the county level would facilitate the optimal allocation of public health resources. OBJECTIVE We aimed to develop and compare supervised, unsupervised and hybrid machine learning models to identify US counties at risk of measles outbreaks. METHODS We constructed a supervised machine learning model based on eXtreme Gradient Boosting (XGBoost) and unsupervised models based on Hierarchical Density-based Spatial Clustering of Applications with Noise (HDBSCAN) and unsupervised Random Forest (uRF). The unsupervised models were used to investigate clustering patterns among counties with measles outbreaks; these clustering data were also incorporated into hybrid XGBoost models as additional input variables. The machine learning models were then compared to weighted logistic regression models with and without input from the unsupervised models. RESULTS Both HDBSCAN and uRF identified clusters that included a high percentage of counties with measles outbreaks. XGBoost and its hybrid models outperformed weighted logistic regression and its hybrid models, with area under the receiver operating curve values of 0.920–0.926 versus 0.900–0.908, area under the precision–recall curve (AUPRC) values of 0.522–0.532 versus 0.485–0.513, and F2 scores of 0.595–0.601 versus 0.385–0.426. Weighted logistic regression and its hybrid models had higher sensitivity than XGBoost and its hybrid models (0.837–0.857 versus 0.704–0.735) but lower positive predictive value (0.122–0.141 versus 0.340–0.367) and specificity (0.793–0.821 versus 0.952–0.958). The hybrid versions of the weighted logistic regression and XGBoost models had slightly higher AUPRC, specificity, and positive predictive values than the respective models that did not include any unsupervised features. CONCLUSIONS XGBoost provided more accurate predictions of measles cases at the county level compared with weighted logistic regression. The threshold of prediction in this model can be adjusted to align with each county’s resources, priorities, and measles risk. While clustering pattern data from unsupervised machine learning approaches improved some aspects of model performance in this imbalanced data set, the optimal approach for integration of such approaches with supervised machine learning models requires further investigation. CLINICALTRIAL N/A

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Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs

Cited by 15 publications

References 32 publications

Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Determination of the Rate of Penetration by Robust Machine Learning Algorithms Based on Drilling Parameters

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches (Preprint)

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