A discrete fracture network (DFN) model is only as accurate as the input data, model assumptions and computer simulation techniques used. All have a significant impact on not only the accuracy, but the applicability of a DFN model. As such global statistical representations of data that incorporate mapping biases (orientation, density, or persistence) will incorrectly represent the in-situ discontinuity network and affect any stability analyses incorporating such. Additionally, irrespective of mapping bias, any created DFN should, and at times, must, honor local structural domain spatial characteristics in order to obtain sufficiently realistic results to be utilized for design purposes.
The Influence of Fabric Mapping Bias on Applied DFN’S and Its Impact on Estimation of Failure
Mathis, James, and Marc Elmouttie. "The Influence of Fabric Mapping Bias on Applied DFN’S and Its Impact on Estimation of Failure." Paper presented at the 2nd International Discrete Fracture Network Engineering Conference, Seattle, Washington, USA, June 2018.
Download citation file: