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

Authors

  • Mateus Tosta TRIL Lab, Department of Scientific Computing, Federal University of Para ́ıba, Jo ̃ao Pessoa 58000-000, Brazil
  • Gustavo P. Oliveira TRIL Lab, Department of Scientific Computing, Federal University of Para ́ıba, Jo ̃ao Pessoa 58000-000, Brazil
  • Bin Wang* National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, P. R. China（Email：bin.wang@cup.edu.cn）
  • Zhiming Chen National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, P. R. China
  • Qinzhuo Liao National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, P. R. China

Keywords:

Reservoir modeling, grid processing, 3D visualization, digital twin

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 opensource 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.

Document Type: Short communication

Cited as: Tosta, M., Oliveira, G. P., Wang, B., Chen, Z., Liao, Q. 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

References

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Published

2023-06-12

Issue

Vol. 8 No. 3 (2023): June

Section

Articles