A Bird�s Eye View on the Applications of Neural Networks in Reservoir Characterization

• Main Authors: Yazmyradova, G., Hermana, M., Soleimani, H., Sivabalan, V.
• Format: Article
• Published: Slovnaft VURUP a.s 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132970075&partnerID=40&md5=bb2423c2229f6cdf975af422893b839e; http://eprints.utp.edu.my/33403/

This review illustrates the most recent improvements and implementations of ANN in characterizing reservoirs in different regions for faster understanding of young petroleum geoscientists and engineers in the industry. Artificial Neural Networks (ANNs), one of the AI tools, has been effectively employed in several domains and has also gained popularity in reservoir characterization. Reservoir characterization is the act of creating a reservoir model based on its characteristics that are important to its capability to produce and store hydrocarbons with respect to fluid flow. The reservoir characterization domain is tricky because of the high complexity of non-linear data and ambiguity in data and modelling. The major objective of ANN application is to integrate obtained data from various geological, geophysical, petrophysical sources in reservoir characterization by identifying the complex non-linear correlation of input data. This work serves as an insight of the current implementation of ANN in the industry, which encourages more innovative intelligence systems that could accelerate the improvements of reservoir characterization evaluation protocols. © 2022. Petroleum and Coal.All Rights Reserved.