Papers Production Forecasting
“Production Forecasting” 태그가 달린 논문 15편 · 필터 해제
SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturing
In the dynamic landscape of Industry 4.0, achieving efficiency, precision, and adaptability is essential to optimize manufacturing operations. Industries suffer due to supply chain disruptions caused by anomalies, which …
Decision MakingProduction ForecastingQuestion AnsweringGraph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are…
Anomaly DetectionDemand ForecastingProduct CategorizationProduction Forecasting+2Solarcast-ML: Per Node GraphCast Extension for Solar Energy Production
This project presents an extension to the GraphCast model, a state-of-the-art graph neural network (GNN) for global weather forecasting, by integrating solar energy production forecasting capabilities. The proposed appro…
Decision MakingGraph Neural NetworkProduction ForecastingWeather ForecastingQuantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting
Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Shor…
BenchmarkingHyperparameter OptimizationProduction ForecastingQuantum Machine Learning+3Hierarchical Forecasting at Scale
Existing hierarchical forecasting techniques scale poorly when the number of time series increases. We propose to learn a coherent forecast for millions of time series with a single bottom-level forecast model by using a…
Production ForecastingTime SeriesAdvanced Deep Regression Models for Forecasting Time Series Oil Production
Global oil demand is rapidly increasing and is expected to reach 106.3 million barrels per day by 2040. Thus, it is vital for hydrocarbon extraction industries to forecast their production to optimize their operations an…
Production ForecastingregressionTime SeriesA Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems
Machine learning has emerged as a powerful tool in various fields, including computer vision, natural language processing, and speech recognition. It can unravel hidden patterns within large data sets and reveal unparall…
Decision MakingManagementPhysics-informed machine learningProduction Forecasting+2Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery
The responsible and sustainable agave-tequila production chain is fundamental for the social, environment and economic development of Mexico's agave regions. It is therefore relevant to develop new tools for large scale …
Active LearningData AugmentationProduction ForecastingSegmentationPhysics-Informed Graph Neural Network for Spatial-temporal Production Forecasting
Production forecast based on historical data provides essential value for developing hydrocarbon resources. Classic history matching workflow is often computationally intense and geometry-dependent. Analytical data-drive…
Graph Neural NetworkProduction ForecastingTime SeriesTime Series AnalysisForecasting the production of Distillate Fuel Oil Refinery and Propane Blender net production by using Time Series Algorithms
Oil production forecasting is an important step in controlling the cost-effect and monitoring the functioning of petroleum reservoirs. As a result, oil production forecasting makes it easier for reservoir engineers to de…
Production ForecastingTime SeriesTime Series AnalysisTowards Better Shale Gas Production Forecasting Using Transfer Learning
Deep neural networks can generate more accurate shale gas production forecasts in counties with a limited number of sample wells by utilizing transfer learning. This paper provides a way of transferring the knowledge gai…
Production ForecastingTransfer LearningEstimating Solar and Wind Power Production using Computer Vision Deep Learning Techniques on Weather Maps
Accurate renewable energy production forecasting has become a priority as the share of intermittent energy sources on the grid increases. Recent work has shown that convolutional deep learning models can successfully be …
Deep LearningProduction ForecastingHybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea
The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimization and two tasks within it: improving t…
BIG-bench Machine LearningDecision MakingProduction ForecastingGrocery Store Flexibility Management Using Model Predictive Control With Neural Networks
As more and more energy is produced from renewable energy sources (RES), the challenge for balancing production and consumption is being shifted to consumers instead of the power grid. This requires new and intelligent w…
ManagementModel Predictive ControlProduction ForecastingSequential model aggregation for production forecasting
Production forecasting is a key step to design the future development of a reservoir. A classical way to generate such forecasts consists in simulating future production for numerical models representative of the reservo…
modelProduction Forecastingregression