Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the...Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the reservoir water level is an essential physical indicator for the reservoirs.Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies.In recent years,deep learning models have been widely applied to solve forecasting problems.In this study,we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,ConvLSTM,and linear interpolation to predict reservoir water levels.It utilizes data from Sentinel-2 satellite images,generated from visible spectrum bands(Red-Blue-Green)to reconstruct true-color reservoir images.Adam is used as the optimization algorithm with the loss function being MSE(Mean Squared Error)to evaluate the model’s error during training.We implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in Vietnam.To assess its performance,we also conducted comparative experiments with other related models,including SegNet_ConvLSTM and UNet_ConvLSTM,on the same dataset.The model performances were validated using k-fold cross-validation and ANOVA analysis.The experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared models.It has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.展开更多
Various uncertainty quantification methodologies are presented using a combination of several deter-ministic decline curve analysis models and two bootstrapping algorithms.These probabilistic models are applied to 126...Various uncertainty quantification methodologies are presented using a combination of several deter-ministic decline curve analysis models and two bootstrapping algorithms.These probabilistic models are applied to 126 sample wells from the Permian basin.Results are presented for 12-72 months of pro-duction hindcast given an average well production history of 103 months.Based on the coverage rate and the forecast error(with the coverage rate being more significant in our choice of the best probabilistic models)and using up to one-half of the available production history for a group of sample wells from the Permian Basin,we find that the CBM-SEPD combination is the best probabilistic model for the Central Basin Platform,the MBM-Arps combination is the best probabilistic model for the Delaware Basin,the CBM-Arps is the best probabilistic model for the Midland Basin,and the best probabilistic model for the overall Permian Basin is the CBM-Arps when early time data is used as hindcast and CBM-SEPD for when one-quarter to one-half of the data is used as hindcast.When three-quarters or more of the available production history is used for analysis,the MBM-SEPD probabilistic model is the best combination in terms of both coverage rate and forecast error for all the sub-basins in the Permian.The novelty of this work lies in its extension of bootstrapping methods to other decline curve analysis models.This work also offers the engineer guidance on the best choice of probabilistic model whilst attempting to forecast production from the Permian Basin.展开更多
基金funded by International School,Vietnam National University,Hanoi(VNU-IS)under project number CS.2023-10.
文摘Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the reservoir water level is an essential physical indicator for the reservoirs.Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies.In recent years,deep learning models have been widely applied to solve forecasting problems.In this study,we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,ConvLSTM,and linear interpolation to predict reservoir water levels.It utilizes data from Sentinel-2 satellite images,generated from visible spectrum bands(Red-Blue-Green)to reconstruct true-color reservoir images.Adam is used as the optimization algorithm with the loss function being MSE(Mean Squared Error)to evaluate the model’s error during training.We implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in Vietnam.To assess its performance,we also conducted comparative experiments with other related models,including SegNet_ConvLSTM and UNet_ConvLSTM,on the same dataset.The model performances were validated using k-fold cross-validation and ANOVA analysis.The experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared models.It has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.
文摘Various uncertainty quantification methodologies are presented using a combination of several deter-ministic decline curve analysis models and two bootstrapping algorithms.These probabilistic models are applied to 126 sample wells from the Permian basin.Results are presented for 12-72 months of pro-duction hindcast given an average well production history of 103 months.Based on the coverage rate and the forecast error(with the coverage rate being more significant in our choice of the best probabilistic models)and using up to one-half of the available production history for a group of sample wells from the Permian Basin,we find that the CBM-SEPD combination is the best probabilistic model for the Central Basin Platform,the MBM-Arps combination is the best probabilistic model for the Delaware Basin,the CBM-Arps is the best probabilistic model for the Midland Basin,and the best probabilistic model for the overall Permian Basin is the CBM-Arps when early time data is used as hindcast and CBM-SEPD for when one-quarter to one-half of the data is used as hindcast.When three-quarters or more of the available production history is used for analysis,the MBM-SEPD probabilistic model is the best combination in terms of both coverage rate and forecast error for all the sub-basins in the Permian.The novelty of this work lies in its extension of bootstrapping methods to other decline curve analysis models.This work also offers the engineer guidance on the best choice of probabilistic model whilst attempting to forecast production from the Permian Basin.
