Application of a probability model to detect unrecognised igneous intrusions in sedimentary basins

Simon Holford* (Corresponding Author), Mark Bunch, Nick Schofield, Michael Curtis

*Corresponding author for this work

Research output: Contribution to journalAbstract


Mafic igneous intrusions are a common feature in extensional sedimentary basins, particularly those located at volcanic rifted margins, and are important in both exploration and development contexts due to their range of interactions with the petroleum system and their role as potential drilling hazards. Experience from a range of basins containing mafic igneous intrusions suggests that seismically resolvable intrusions are typically accompanied by a large number of intrusions that are too thin to be confidently identified and interpreted from seismic reflection surveys. The increased vertical resolution of wireline log data affords an opportunity to identify such sub-seismic-scale intrusions, though in many wells with full wireline suites igneous intrusions are often misidentified as sedimentary units, including felsic intrusions whose physical properties are more similar to sedimentary rocks. Here we apply a wireline-log-based probability model to well data from a number of basins. In previous applications, the model has proven highly effective in predicting the occurrence of carbonate cementation zones in sandstones in comparison to neural network approaches. We demonstrate its ability to predict the presence of igneous intrusions that were not previously identified by either seismic interpretation, or through the analysis of well-derived datasets. The broader application of this model to large suites of legacy data could lead to improved knowledge of the occurrence of intrusions in basins with implications for basin modelling and well planning.
Original languageEnglish
Pages (from-to)S426-S430
Number of pages5
JournalThe APPEA Journal
Early online date13 May 2022
Publication statusPublished - 13 May 2022

Bibliographical note

Declaration of funding. SH thanks funding support from the South Australian Department for Energy and Mining and the Australian Society of Exploration
Geophysics Research Foundation


  • Drilling
  • igneous intrusions
  • wireline logging
  • machine learning
  • magmatism
  • probability
  • sedimentary basins
  • seismic reflection


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