Benefits of maximum likelihood estimators for fracture attribute analysis: Implications for permeability and up-scaling

Roberto Emanuele Rizzo* (Corresponding Author), D. Healy, L. De Siena

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)
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The success of any predictive model is largely dependent on the accuracy with which its parameters are known. When characterising fracture networks in rocks, one of the main issues is accurately scaling the parameters governing the distribution of fracture attributes. Optimal characterisation and analysis of fracture lengths and apertures are fundamental to estimate bulk permeability and therefore fluid flow, especially for rocks with low primary porosity where most of the flow takes place within fractures. We collected outcrop data from a fractured upper Miocene biosiliceous mudstone formation (California, USA), which exhibits seepage of bitumen-rich fluids through the fractures. The dataset was analysed using Maximum Likelihood Estimators to extract the underlying scaling parameters, and we found a log-normal distribution to be the best representative statistic for both fracture lengths and apertures in the study area. By applying Maximum Likelihood Estimators on outcrop fracture data, we generate fracture network models with the same statistical attributes to the ones observed on outcrop, from which we can achieve more robust predictions of bulk permeability.
Original languageEnglish
Pages (from-to)17-31
Number of pages15
JournalJournal of Structural Geology
Early online date9 Dec 2016
Publication statusPublished - Feb 2017

Bibliographical note

The authors thank Andrew Hurst for support during the field work, and also Antonio Grippa, Giuseppe Palladino and Gustavo Zvirtes for help and constructive discussions during the field work. We are particularly grateful to Tom Manzocchi for his important comments that greatly improved the first version of this paper. We acknowledge constructive reviews by Julia Gale and Ken McCaffrey, which have greatly improved our manuscript. This work forms part of a NERC New Investigator award for David Healy (NE/I001743/1), which is gratefully acknowledged. Finally, Roberto Emanuele Rizzo is very appreciative of AFES (Aberdeen Formation Evaluation Society) for funding support during his PhD.


  • fractures
  • maximum likelihood
  • fracture networks
  • up-scaling
  • permeability networks


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