... Intelligence (AI), there has been a paradigm shift towards using data-driven approaches to optimize SCM processes. AI and machine learning models can analyze vast amounts of historical data to uncover patterns and trends that are not immediately obvious, leading to more accurate forecasts and more efficient...

... machine learning port artificial intelligence freight rate destination port borouge forecast ocean spot forecasting shipment global shipping accuracy algorithm ocean spot and forward price development petroleum engineer system MODEL SETUP AND RESULTS Data Collection and Pre...

... by logging tools. This research introduces a novel approach that involves predicting real-time reservoir porosity during drilling, using time series models to extend our knowledge into unlogged intervals. This method leverages existing log data to forecast porosity ahead of current drilling, allowing...

..., sequence modeling algorithms such as long short-term memory - LSTM - have been widely used in various natural domains, like in earthquake and rainfall forecasting. A new architecture called Transformer has been used by the recent state-of-the-art models, capable of outperforming classical methods. Using...

.... This allowed us to identify the most effective architecture for forecasting gas and condensate rates. However, it is important to acknowledge that hyperparameter tuning and NN training can be computationally intensive. Training the model may require several hours, particularly when dealing with sizable...

... reduction in average time, CPU, and memory usage with application of TL. Additionally, in terms of subsurface forecasting, Alolayan et al. (2021) showed the capabilities of TL by improving accuracy of forecast in Texas Barnett and Pennsylvania Marcellus shale by 47%, compared to Arp's decline curve model...

..., bioinformatics, data compression, computer graphics and machine learning. There are many clustering algorithms dedicated for different cluster models such as: Clustering Literature Review The method introduced in this study proposes faster production forecasting based on wells classification...

... to make the right decisions and it turns out to be much more difficult when the initial information is ambiguous. In this regard, when making decisions, it is desirable to rely on a robust assessment and forecasting the technological efficiency of thermal enhanced oil recovery techniques, for which...

... Abstract Production performance forecasting is considered as one of the most challenging and time consuming tasks in petroleum engineering disciplines, it has important implications on decision-making, planning production and processing of facilities. In Petroleum Development Oman (PDO), which...

... management upstream oil & gas better production data management implementation project team production performance management platform operation snoc platform database requirement strategic planning and management reservoir surveillance forecasting snoc user sharjah end user case study...

... a saturation forecast based on class-based machine learning combined with the time series modelling using multiple time lapse runs in a single well for a field. The first step involves using an unsupervised class-based machine learning model that classifies the input petrophysical data into set of classes...

...-Of-Sequence Fracturing field treatment in Western Canada (in 2017). The same 3-D fracture model is also used to forecast hydraulic fracture geometries when a conventional Sequential Fracturing treatment of the same candidate well is carried out. To evaluate the production potential from the candidate well...

...Physical model The first model which was used for the forecasting was a physics-based model. The employed physics-based model consisted of a coupled material balance (describing reservoir behavior) and deliverability curves (describing the well and inflow behavior) which were calibrated...

... in a short time. Data driven analysis and forecasting is carried out for a mature steamflood. Firstly, all data including production, injection, pressure, well test, well, static models, petrophysical logs, GIS data are integrated on a single data analytics platform. Trends in data are established...

..., and stored in a workflow library for secure deployment and sharing across the organization. machine learning lng liquified natural gas upstream oil & gas neural network innovative approach gas production production data som prediction spe 160445 forecasting information vector...

... upstream oil & gas information artificial intelligence objective collaboration session execution forecasting reservoir knowledge management scenario constrain asset operation optimization operation spe 133450 platform criteria collaboration solution History: asset management...