Data-driven simulation of reservoir dynamics based on erroneous inputs from fast supply streamline data

Behzad Saberali; Kai Zhang

doi:10.1117/12.3009573

16 October 2023 Data-driven simulation of reservoir dynamics based on erroneous inputs from fast supply streamline data

Behzad Saberali, Kai Zhang

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031B (2023) https://doi.org/10.1117/12.3009573
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

This work introduced an AI-based simulation method as an innovative solution to address the limitations of conventional numerical reservoir simulation methods. The primary motivations behind this research were the time-consuming nature of finite difference (FD) simulation and the considerably lower accuracy of streamline (SL) simulation. We adopted a novel approach to overcome these challenges by incorporating erroneous yet rapidly supplied streamline data as inputs to our model. This unique input selection allowed us to leverage the direct training strategy in a deep learning-based network. Furthermore, by integrating FD grid data as training outputs, we improved the model’s accuracy and expanded its functionalities. The developed proxy model demonstrates remarkable performance, surpassing the FD simulator’s computational efficiency while outperforming the SL simulator ’s accuracy. Through validation of the benchmark Egg model, our research confirmed the potential of this AI-based proxy model as a reliable, accurate, and efficient alternative in dynamic reservoir modeling.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Behzad Saberali and Kai Zhang "Data-driven simulation of reservoir dynamics based on erroneous inputs from fast supply streamline data", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031B (16 October 2023); https://doi.org/10.1117/12.3009573

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KEYWORDS

Data modeling

Education and training

Computer simulations

Performance modeling

Modeling

Finite difference methods

Databases

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