Framework of Fracture Network Modeling using Conditioned Data with Sequential Gaussian Simulation

TL;DR

This paper presents a geostatistical framework using conditioned data and sequential Gaussian simulation to model fracture networks, aiding subsurface flow and transport analysis.

Contribution

It introduces a novel geostatistical method for fracture network modeling that efficiently incorporates conditioning data and propagates fracture angles from seed points.

Findings

Simulation results match original fracture networks

Method is computationally efficient for subsurface applications

Applicable to geological faults and field data

Abstract

The fracture characterization using a geostatistical tool with conditioning data is a computationally efficient tool for subsurface flow and transport applications. The main objective of the paper is to propose a framework of geostatistical method to model the fracture network. In the method, we have chosen neighborhood area to apply the Gaussian Sequential Simulation in order to generate the fracture network in the unknown region. The angle was propagated from the seed where direction is guided by the neighborhood data in simulation regime. Initial seeds can be distributed by Poisson procedure. The method is applied for geological faults from the Central Kazakhstan and for field data from Scotland, UK. The simulation results are compatible with the original fracture network in the flow and transport modeling setting. From the research that has been carried out, it is possible to conclude that the numerical simulation of fracture network is a valuable tool in the subsurface flow and transport applications.