Blar i Department of Energy and Petroleum Engineering (TN-IEP) på emneord "maskinlæring"
Viser treff 1-6 av 6
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Impact of data pre-processing techniques on recurrent neural network performance in context of real-time drilling logs in an automated prediction framework
(Peer reviewed; Journal article, 2021-11)Recurrent neural networks (RNN), which are able to capture temporal natures of a signal, are becoming more common in machine learning applied to petroleum engineering, particularly drilling. With this technology come ... -
Integration of Neural Networks and Wellbore Stability, a Modern Approach to Recognize Drilling Problems Through Computer Vision and Machine Learning
(Masteroppgave/UIS-TN-IEP/2019;, Master thesis, 2019-06-15)Cavings are an endless and valuable source of information when drilling operations is being performed. Multiple parameters can contribute to produce cavings which indicate that failure has occurred or is about to occur ... -
A LSSVR-PSO machine learning model for the estimation of reservoir porosity from petrophysical well logs
(Masteroppgave/UIS-TN-IEP/2020;, Master thesis, 2020-06-10)Reservoir porosity is a key parameter in the reservoir evaluation and geomechanics. To obtain accurate measurement of porosity can be time-consuming and expensive by core sampling or applying various well logging tools. ... -
Machine learning in reservoir permeability prediction and modelling of fluid flow in porous media
(Peer reviewed; Journal article, 2019)Reliable data on the properties of the porous medium are necessary for the correct description of the process of displacing hydrocarbons from the reservoirs and forecasting reservoir performance. The true permeability of ... -
Physics-Based Swab and Surge Simulations and the Machine Learning Modeling of Field Telemetry Swab Datasets
(Peer reviewed; Journal article, 2023)Drilling operations are the major cost factor for the oil industry. Appropriately designed operations are essential for successful drilling. Optimized drilling operations also enhance drilling performance and reduce drilling ... -
Reference Dataset for Rate of Penetration Benchmarking
(Peer reviewed; Journal article, 2020-10)In recent years, there were multiple papers published related to rate of penetration prediction using machine learning vastly outperforming analytical methods. There are models proposed reportedly achieving R2 values as ...