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Short Communication | Open Access

APyCE: A Python module for parsing and visualizing 3D reservoir digital twin models

TRIL Lab, Department of Scientific Computing, Federal University of Paraíba, João Pessoa 58000-000, Brazil
National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, P. R. China
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Abstract

Engineers, geoscientists, and analysts can benefit from fast, easy, and real-time immersive 3D visualization to enhance their understanding and collaboration in a virtual 3D world. However, converting 3D reservoir data formats between different software programs and open-source standards can be challenging due to the complexity of programming and discrepancies in internal data structures. This paper introduces an open-source Python implementation focused on parsing industry reservoir data formats into a popular open-source visualization data format, Visual Toolkit files. Using object-oriented programming, a simple workflow was developed to export corner-point grids to Visual Toolkit-hexahedron structures. To demonstrate the utility of the software, standard raw input files of reservoir models are processed and visualized using Paraview. This tool aims to accelerate the digital transformation of the oil and gas industry in terms of 3D digital content generation and collaboration.

Keywords

digital twinReservoir modelinggrid processing3D visualization

References

 

Ayachit, U., Geveci, B., Avila, L. The Paraview Guide: A Parallel Visualization Application. Kitware, New York, USA, 2015.
 

Lie, K. A. An introduction to reservoir simulation using MATLAB/GNU Octave: User guide for the MATLAB Reservoir Simulation Toolbox (MRST). Cambridge, UK, Cambridge University Press, 2019.
Crossref
 

Masison, J., Beezley, J., Mei, Y. et al. A modular computational framework for medical digital twins. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(20): e2024287118.
Crossref Google Scholar
 

Ponting, D. K. Corner point geometry in reservoir simulation. European Association of Geoscientists & Engineers, 1989: cp-234.
Google Scholar
 
Schlumberger. Eclipse Reservoir Simulation Software Reference Manual. Schlumberger, Texas, USA, 2014.
 

Schroeder, W., Martin, K. M., Lorensen, W. E. The Visualization Toolkit An Object-Oriented Approach to 3D Graphics. Prentice-Hall, New Jersey, USA, 1998.
 

Sircar, A., Nair, A., Bist, N. et al. Digital twin in hydrocarbon industry. Petroleum Research, 2022, in press, https://doi.org/10.1016/j.ptlrs.2022.04.001.
Crossref Google Scholar
 

Sullivan, C., Kaszynski, A. Pyvista: 3d plotting and mesh analysis through a streamlined interface for the visualization toolkit (vtk). Journal of Open Source Software, 2019, 4(37): 1450.
Crossref Google Scholar
 

Sun, S., Zhang, T. A 6m digital twin for modeling and simulation in subsurface reservoirs. Advances in Geo-Energy Research, 2020, 4(4): 349-351.
Crossref Google Scholar
 
Wang, B. Pygrdecl a python-based grdecl visualization library, 2018.
Advances in Geo-Energy Research
Volume 8 Issue 3,
June 2023
Pages 206-210
DOI: 10.46690/ager.2023.06.07
Cite this article:
Tosta M, Oliveira GP, Wang B, et al. APyCE: A Python module for parsing and visualizing 3D reservoir digital twin models. Advances in Geo-Energy Research, 2023, 8(3): 206-210. https://doi.org/10.46690/ager.2023.06.07

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Received: 21 May 2023
Revised: 03 June 2023
Accepted: 09 June 2023
Published: 12 June 2023
© The Author(s) 2023.

This article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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