AUTHORS: R.Di Cuia, R.Harrigal, D.Ferraretti, G.Gamberoni
YEAR: 2013
“HYDROCARBON EXPLORATION IN THE ZAGROS MOUNTAINS OF IRAQI KURDISTAN AND IRAN” Conference, Geological Society of UK, London
Image logs hold important information about the characteristics of reservoir units and they can supply insight on the rock texture, textural organization and porosity types and distribution. To reduce the subjectivity of the interpretation and cut the interpretation time we have used to interpret the image logs of an exploration well in Northern Iraq a new semi-automatic process based on an expert system that exploits image processing algorithms and clustering techniques to analyze and classify borehole images. This system extrapolates the maximum amount of information from the image logs by considering not only the surfaces that cut the borehole but also the textural features of the images.
Once the image log is analysed the application of clustering techniques to the values extracted from the borehole images supply a consistent classification of the images and the propagation of this classification along the logged section. In this way, we can automatically extract rock characteristics. The final results of this process is a set of “image facies” identified along the image log obtained by a largely automated log interpretation, although some level of human interaction and correction is still necessary.
The clustering process and the propagation of the classes along the logged section is very fast allowing an interactive approach, producing several scenarios with different number of classes and/or allowing a quick update of the image log interpretation once more data/knowledge is acquired. The different outputs have been used to frame the initial reservoir model of a recent discovery in the “High folded zone” of Northern Iraq.