4.6 Review

Research Status of and Trends in 3D Geological Property Modeling Methods: A Review

Journal

APPLIED SCIENCES-BASEL
Volume 12, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/app12115648

Keywords

3D model; property modeling; grid generation; property interpolation; geostatistics

Funding

  1. Major National Science and Technology Projects of China [2016ZX05037-006-005]
  2. project of R&D Department of Petrochina [2021DJ2005]

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This article comprehensively analyzes the grid generation and property interpolation methods in three-dimensional geological property modeling. It is found that in numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model. For interpolation methods, most geological phenomena are nonstationary, and the main development trend is to increase geological constraints and reduce the limitation of stationarity.
Three-dimensional (3D) geological property modeling is used to quantitatively characterize various geological attributes in 3D space based on geostatistics with the help of computer visualization technology, and the results are often stored in grid data. The 3D geological property modeling includes two main components, grid model generation and property interpolation. In this review article, the existing grid generation methods are systematically investigated, and both traditional and multiple-point geostatistical algorithms involved in interpolation methods are comprehensively analyzed. It is shown that considering the numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model for simulations in petroleum exploration and development. For the interpolation methods aspect, most geological phenomena are nonstationary, to simulate various types of reservoirs; the main development trends are increasing geological constraints and reducing the limitation of stationarity. Both methods have certain constraints, and the multiscale problem of multiple-point geostatistics poses a main challenge to the field. In addition, the deep-learning based method is a new trend in geological property modeling.

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