Predicting Well Performance and Reservoir Behaviour Using Deep Neural Networks |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-3 |
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| Year of Publication : 2024 | ||
| Authors : Abhay Dutt Paroha | ||
| DOI : 10.14445/22312803/IJCTT-V72I3P112 | ||
How to Cite?
Abhay Dutt Paroha , "Predicting Well Performance and Reservoir Behaviour Using Deep Neural Networks," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 84-90, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P112
Abstract
By employing Deep Neural Networks (DNNs), this research paper introduces an innovative approach to forecast reservoir behaviour and performance. A data-driven methodology is employed to analyze various categories of reservoir data using DNNs. These data types consist of well logs, production data, and geology information. Due to the fact that the DNN model discovers intricate connections between data points during training and preprocessing, it is possible to forecast reservoir dynamics with a certain degree of accuracy. The experimental findings provide evidence that the proposed methodology is effective at detecting complex patterns and accurately forecasting production outputs. Operators may be able to make more informed decisions regarding reservoir management by employing this strategy, which could result in enhanced recovery and production techniques. The oil and gas industry is highly motivated to adopt the encouraging developments in machine learning that result from the integration of DNNs into reservoir engineering methods. Ultimately, this may result in a more efficient and sustainable utilization of resources.
Keywords
Reservoir Engineering, Deep Neural Networks, Reservoir Behaviour Analysis, Data-Driven Modeling, Machine Learning.
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