Geological Risk Analysis in PBE by Applying Evidence Theory and GIS

Document Type : Research Paper

Authors

1 Surveying Department, NIOCEXP, Tehran, Iran

2 Geochemistry Research Group, Faculty of Research and Development of the Upstream Oil Industry, RIPI, Tehran, Iran

Abstract

Geological risk analysis in an exploration process based on a set of traps is an example of a spatial multi-criteria decision-making process. Since the process of formation, gathering and exploration of hydrocarbons has many complex influencing parameters, and finding the potential of these hydrocarbon reserves is also time-consuming and costly, it is necessary to control and manage the risks in the process. Hydrocarbon exploration as much as possible. Most of these risks are caused by uncertainty in spatial data. This research is intended to design a framework for managing and reducing the geological risk of buildings in the sedimentary region of Fars by using the features of intuition theory and spatial information systems. In the case of this research, the main statement is «Which places of the play have less risk than other places in terms of the presence of a hydrocarbon system, and what is the uncertainty of this estimate». For this purpose, the theory of evidence or Dempster-Shafer has been used in this research. This theory has been used to calculate geological risk uncertainty in an interval form. This type of approach required that the data used in the formation of petroleum systems be collected in the form of a spatial database so that by combining spatial analysis with uncertainty models, geological risk maps can be created with the help of spatial information systems with proper accuracy. The results showed that geological risk can be monitored with 79.6 % accuracy. The integration of the methods used provides a framework that every point of the studied sedimentary area has a certain amount of geological risk, the uncertainty of which is controlled by the Dempster-Shafer theory. The proposed model enhanced the precision of the prospect classification by considering an uncertainty interval.

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Main Subjects

References

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Journal of Petroleum Research
Volume 34, 1403-3 - Serial Number 136
September and October 2024
Pages 82-93

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